Abnormity monitoring method and device for gray level test

Abstract

The invention discloses a gray scale test abnormity monitoring method and device. The method comprises the following steps: determining a test container corresponding to a gray scale test, and obtaining at least one operation parameter of the test container when the gray scale test is operated; the operation parameters are data obtained by collecting operation state data of the test container in real time; and determining whether the gray level test is in a preset abnormal state based on the monitoring model and the operation parameters. According to the technical scheme, by monitoring the test container needing to be tested instead of detecting all containers involved in gray release, whether gray test release is abnormal or not can be accurately determined by effectively monitoring the change state of the operation parameters of the test container, and the accuracy of monitoring the abnormal condition in the gray test process is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular, to an abnormal monitoring method and device for gray box testing. Background Art

[0002] Gray box testing is a software release strategy, which refers to a strategy of gradually and phased releasing new functions or versions. Gray box testing is usually used for software updated versions or functions. In order to prevent potential risks in the new version from affecting the use of most users, therefore, the detection of abnormalities during the gray box testing is very important.

[0003] Currently, the detection of abnormalities during the gray box testing usually obtains the target metrics of the current version of the product in the first time period, and the target metrics of the gray version of the product in the first time period, and compares the target metrics of the current version of the product in the first time period with the target metrics of the gray version of the product in the first time period to determine the comparison result, so as to determine whether there is an abnormality in the gray version of the product according to the comparison result. However, in the case of few abnormalities during the gray box testing, the change of the version metric data may not be obvious, resulting in difficulty in finding whether there is an abnormal situation in the gray box testing. In addition, there is no existing technology that can automatically analyze whether there is an abnormality in the gray box testing parameters and trends. Therefore, there is an urgent need for a method that can accurately monitor the abnormalities in the gray box testing parameters. Summary of the Invention

[0004] The present invention provides an abnormal monitoring method and device for gray box testing to improve the accuracy of monitoring abnormal situations during the gray box testing process.

[0005] According to one aspect of the present invention, there is provided an abnormal monitoring method for gray box testing, the method comprising:

[0006] Determine the test container corresponding to the gray box testing, and obtain at least one running parameter of the test container when running the gray box testing; the running parameter is data obtained by real-time collecting the running state data of the test container;

[0007] Based on the monitoring model and the running parameter, determine whether the gray box testing is in a preset abnormal state.

[0008] According to another aspect of the present invention, there is provided an abnormal monitoring device for gray box testing, the device comprising:

[0009] A parameter acquisition module, configured to determine the test container corresponding to the gray box testing, and obtain at least one running parameter of the test container when running the gray box testing; the running parameter is data obtained by real-time collecting the running state data of the test container;

[0010] The monitoring module determines whether the gray-box testing is in a preset abnormal state based on the monitoring model and the operating parameters.

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

[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 executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the abnormal monitoring method of the gray-box testing according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for implementing the abnormal monitoring method of the gray-box testing according to any embodiment of the present invention when executed by a processor.

[0016] The technical solution of the embodiment of the present invention determines the test container corresponding to the gray-box testing, and obtains at least one operating parameter of the test container when running the gray-box testing; the operating parameter is data obtained by real-time collection of the operating state data of the test container; further, based on the monitoring model and the operating parameter, it is determined whether the gray-box testing is in a preset abnormal state, that is, the present invention monitors the real-time parameters and their trends of the test container to be tested, and realizes more accurate container data monitoring based on the real-time parameters and the change trends of the real-time parameters. Thus, it is possible to effectively determine whether an abnormality occurs in the gray-box testing by monitoring the change state of the operating parameters of the test container, and improve the accuracy of monitoring abnormal situations in the process of gray-box testing.

[0017] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily 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 will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0019] Figure 1It is a flowchart of an abnormal monitoring method for gray-scale testing provided according to an embodiment of the present invention;

[0020] Figure 2 It is a flowchart of another abnormal monitoring method for gray-scale testing provided according to an embodiment of the present invention;

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

[0022] Figure 4 It is a flowchart of another abnormal monitoring method for gray-scale testing provided according to an embodiment of the present invention;

[0023] Figure 5 It is a schematic structural diagram of an abnormal monitoring device for gray-scale testing provided according to an embodiment of the present invention;

[0024] Figure 6 It is a schematic structural diagram of an electronic device for implementing the abnormal monitoring method of gray-scale testing according to an embodiment of the present invention. Specific Embodiments

[0025] In order to enable those skilled in the art to better understand the solution 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 accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances 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 "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1The flowchart of an abnormal monitoring method for gray-box testing provided by an embodiment of the present invention. This embodiment is applicable to the situation of abnormal monitoring of gray-box testing. This method can be executed by an abnormal monitoring device for gray-box testing. The abnormal monitoring device for gray-box testing can be implemented in the form of hardware and / or software, and can be configured in any electronic device with network communication functions. As Figure 1 shown, the abnormal monitoring method for gray-box testing of the present invention includes the following processes:

[0029] S110. Determine the test containers corresponding to the gray-box test, and obtain at least one running parameter of the test containers when running the gray-box test; the running parameter is data obtained by real-time collecting the running state data of the test containers.

[0030] Among them, the test container can be a container storing data with updates in the current version or current function compared with the previous version or previous function. Each gray-box test is to test at least one of the test containers, that is, run the test containers until all the test containers can run stably, that is, determine that the current version can be launched stably.

[0031] Specifically, when conducting a gray-box test, the actual number of associated containers is extremely large, but the proportion of test containers that need to be subjected to gray-box testing is relatively small, and may even be less than 1% of the total number of service containers corresponding to the gray-box test. In order to accurately detect whether an abnormality occurs in the gray-box test, it is necessary to accurately monitor the test containers corresponding to the gray-box test. That is, in each gray-box test, it is necessary to first determine the test containers corresponding to the gray-box test, so as to obtain at least one running parameter of the test containers when running the gray-box test in real time, and determine whether an abnormality occurs in the gray-box test by monitoring the change of the running parameter, which can better highlight the test containers that are in the gray-box test, improve the test efficiency, and avoid abnormal omissions.

[0032] Exemplarily, if the total number of containers to which the target application update needs to be applied is the first quantity, but the target application update needs to be gradually subjected to gray-box testing before going online, that is, the updated content is gradually distributed for testing and not all distributed to users at one time. If the current gray-box test involves the second quantity of test containers and no abnormality is found, the next gray-box test will increase to the third quantity of test containers, and so on for gray-box testing until all the updated target applications are distributed to users. The first quantity is much larger than the second quantity and the third quantity; the second quantity is less than the third quantity.

[0033] Optionally, obtain at least one running parameter of the test container during the running of the gray-box test, including: the sampling intervals and sampling frequencies corresponding to each running parameter are different; the sampling interval can be the time range for collecting data. The sampling frequency can be understood as collecting the running status data of the test container every preset time period. The time interval from the start to the end of each collection is the sampling interval.

[0034] Specifically, the sampling intervals and sampling frequencies corresponding to each running parameter can be determined according to the importance and fluctuation sensitivity of each running parameter; for running parameters with high importance, their corresponding sampling intervals are longer and the sampling frequencies are higher, so as to facilitate real-time monitoring of the gray-box test status; for running parameters with low fluctuation sensitivity, their corresponding sampling intervals become longer and the sampling frequencies decrease; conversely, for running parameters with high fluctuation sensitivity, their corresponding sampling intervals become shorter and the sampling frequencies increase. By way of example, for running parameters with high importance and high fluctuation sensitivity, their corresponding sampling frequencies can be 15 - 45 s; for running parameters with low importance and low fluctuation sensitivity, their corresponding sampling frequencies can be 60 s - 90 s.

[0035] Furthermore, for the sampling intervals and sampling frequencies corresponding to each running parameter, the monitoring model can give the preset sampling intervals and preset sampling frequencies corresponding to each running parameter. Before the start of the current gray-box test, based on the preset sampling intervals and preset sampling frequencies corresponding to each running parameter, dynamically adjust the sampling intervals and sampling frequencies corresponding to each current running parameter to obtain the sampling intervals and sampling frequencies corresponding to each running parameter in the current gray-box test.

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

[0037] In this embodiment, optionally, serious anomalies are more likely to occur in the initial stage of the gray-box test, so it is necessary to monitor some running parameters to issue a warning in a timely manner; in the case of serious anomalies, it may cause the gray-box test to be unable to continue running or require the gray-box test to interrupt running. Specifically, after obtaining at least one running parameter of the test container, the method further includes: if the running time of the gray-box test is within the preset monitoring interval and at least one running parameter meets the first warning condition, then issue a first warning message; the preset monitoring interval is the time that lasts for a preset duration from the start of the gray-box test; the first warning message at least includes the running parameter that meets the first warning condition and the first warning condition corresponding to the running parameter that meets the first warning condition; wherein, in response to at least one running parameter meeting the first warning condition, the gray-box test interrupts running.

[0038] Among them, when a serious exception occurs and the gray-box testing is still running, the first warning condition is used to indicate interrupting the execution of the gray-box testing. At least one running parameter satisfies the first warning condition, including: if there is a preset parameter and / or the amplitude of the running parameter fluctuation is greater than or equal to the fluctuation anomaly threshold in at least one running parameter, it is determined that at least one running parameter satisfies the first warning condition; among them, when the amplitude of the running parameter fluctuation is greater than or equal to the fluctuation anomaly threshold, it indicates interrupting the execution of the gray-box testing.

[0039] Among them, the preset parameter is the running data indicating interrupting the execution of the gray-box testing. The preset parameter may include a first parameter, a second parameter, and a third parameter; the first parameter may be that the service associated with the gray-box testing is not started; the second parameter is detecting a core dump file associated with the gray-box testing, that is, when the program under test crashes, the operating system will generate a core dump file to record the memory state, stack information, etc. when the program crashes; the third parameter is the data generated by a kernel panic associated with the gray-box testing, that is, when the operating system kernel encounters a serious error that cannot be handled, it will trigger a kernel panic, resulting in the system crashing and being forced to restart or enter the debug mode;

[0040] If there is an amplitude of the running parameter fluctuation greater than or equal to the fluctuation anomaly threshold in the running parameters, the running parameter also satisfies the first warning condition. For example, when the running parameter is the gateway success rate associated with the gray-box testing, if the gateway success rate associated with the gray-box testing drops by at least a preset threshold, the first warning condition is satisfied; when the running parameter is the error log volume associated with the gray-box testing, when the error log volume associated with the gray-box testing suddenly increases by a preset percentage and the error log volume is greater than the preset volume, the first warning condition is satisfied.

[0041] In addition, it should be noted that the running parameters within a preset monitoring interval of the running time of the gray-box testing can be monitored through a monitoring model to determine whether at least one running parameter satisfies the first warning condition. The monitoring model is an artificial intelligence model.

[0042] The technical solution of this embodiment can timely discover whether the running parameters satisfy the first warning condition by monitoring the running parameters within the preset monitoring interval, avoiding the failure to timely discover the occurrence of a serious exception and affecting the efficiency of the gray-box testing.

[0043] S120. Determine whether the gray-box testing is in a preset abnormal state based on the monitoring model and the running parameters.

[0044] Among them, the monitoring model is an artificial intelligence model, and the monitoring model is configured with a 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 appears the preset operating state data, it is determined that the change state of the operating parameters appears an abnormal change state. The monitoring model can be advanced

[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 state data appears in the operating parameters, so as to output whether the change state of the operating parameters appears an abnormal change state, so as to accurately obtain whether the gray-scale test is in the preset abnormal state.

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

[0047] Among them, the data change trend can be the change trend of the operating parameters over time.

[0048] Specifically, determining the data change trend of each operating parameter according to the operating parameters includes: fitting the operating parameters by using a preset data processing method to determine the data change trend of the operating parameters. Among them, the preset data processing method can be a method of using a mathematical method to determine the data change trend of the operating parameters, and the mathematical methods include but are not limited to linear regression method, moving average method and exponential smoothing method. Or, the operating parameter values obtained at different times are plotted into a data trend graph, and the change trend of each operating parameter is obtained through 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 parameters meets the preset data change trend. If the data change trend of the operating parameters meets the preset data change trend, the monitoring model will output that the gray-scale test is in the preset abnormal state. Among them, the preset data change trend can be understood as the data change trend shown by the operating parameters when the gray-scale test is in the preset abnormal state.

[0050] For the technical solution of this embodiment, first determine the data change trend of each operating parameter, and further identify the data change trend of each operating parameter based on the monitoring model to determine whether the gray-box testing is in a preset abnormal state. This can 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 result can be greatly improved, and the efficiency of the model for data analysis can also be improved.

[0051] For the technical solution of the embodiment of the present invention, determine the test container corresponding to the gray-box testing, and obtain at least one operating parameter of the test container when running the gray-box testing; the operating parameter is data obtained by real-time collection of the operating state data of the test container. Further, based on the monitoring model and the operating parameter, determine whether the gray-box testing is in a preset abnormal state, that is, the present invention monitors the real-time parameters and their trends of the test container to be tested, and realizes more accurate container data monitoring based on the real-time parameters and the change trend of the real-time parameters. Thus, it is possible to effectively determine whether an abnormality occurs in the gray-box testing by monitoring the change state of the operating parameters of the test container, and improve the accuracy of monitoring abnormal conditions during the gray-box testing.

[0052] Embodiment 2

[0053] Figure 2 It is a flowchart of another method for monitoring abnormalities in gray-box testing provided by an embodiment of the present invention. The technical solution of this embodiment further describes in detail "determine the data change trend of each operating parameter according to the operating parameter; based on the monitoring model, identify the data change trend of each operating parameter to determine whether the gray-box testing is in a preset abnormal state" on the basis of the above embodiment. This embodiment can be combined with each optional solution in one or more of the above embodiments. As Figure 2 shown, the method for monitoring abnormalities in gray-box testing includes:

[0054] S210. Determine the test container corresponding to the gray-box testing, and obtain at least one operating parameter of the test container when running the gray-box testing; the operating parameter is data obtained by real-time collection of the operating state data of the test container.

[0055] S220. Plot each operating parameter 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.

[0056] Among them, the running parameters may include at least one of the number of concurrent requests, memory occupancy rate, CPU occupancy rate, number of threads, success rate, latency, and amount of error logs (Error log amount). The number of concurrent requests may be the number of requests per second (QPS); the success rate may include, but is not limited to, the program execution success rate, gateway success rate, and call success rate; the call success rate may include the interface call success rate, service call success rate, etc.; the latency may be a latency parameter associated with the canary test, including but not limited to P50, P90, P99, and average latency. Among them, the P99 and P90 results can reveal occasional problems such as resource competition, slow queries, and network jitter.

[0057] The running parameter trend chart can be understood as a visualization chart that can reflect the data change trend. The running parameter trend chart can be presented in the form of a line chart, scatter chart, or area chart. For example, Figure 3 the line chart shown.

[0058] S230. Based on the monitoring model, determine whether the running parameter trend chart corresponding to the running parameters meets the second warning condition; the second warning condition is used to indicate that the canary test is in a preset abnormal state.

[0059] Among them, the preset abnormal state can be understood as the state when the running state data of the canary container shows different degrees of abnormality.

[0060] Specifically, based on the monitoring model, identify the running parameter trend chart to obtain the first trend data corresponding to the running parameters. When the first trend data meets the preset data trend change range, determine whether the running parameter trend chart corresponding to the running parameters meets the second warning condition. The preset data trend change range can be understood as the trend change information of the running parameters when they are in the preset abnormal state.

[0061] Optionally, the monitoring model may have the ability to analyze the running parameter trend chart. The generation of the monitoring model may include: obtaining at least one historical running parameter in different historical canary test stages, plotting the historical running parameters into a historical running parameter trend chart, different historical running parameter trend charts corresponding to different trend chart analysis results, using the historical running parameter trend chart and the trend chart analysis results corresponding to the historical running parameter trend chart as the training data, and training the to-be-trained monitoring model based on the training data to obtain the monitoring model. Among them, the trend chart analysis result may be the analysis result of the historical running parameter trend chart corresponding to whether the canary test is in the preset abnormal state.

[0062] Further, based on the data to be trained, training the monitoring model to be trained to obtain a monitoring model, including: dividing the data to be trained into training data, test data, and validation data; training the chart analysis ability of the monitoring model to be trained based on the training data to obtain a trained monitoring model; verifying whether the chart analysis ability of the trained monitoring model meets the standard based on the test data and the validation data. If it meets the standard, the trained monitoring model is used as the monitoring model for gray-box testing. The training of the monitoring model in the present invention ensures the accuracy of the ability of the monitoring model to analyze the trend chart of operating parameters.

[0063] S240. If the trend chart of the operating parameter corresponding to the operating parameter meets the second warning condition, it is determined that the gray-box testing is in a preset abnormal state, and a second warning message is sent.

[0064] Among them, the second warning message may include, but is not limited to, the operating parameter corresponding to the second warning condition, the corresponding change trend data, the number of test containers corresponding to the gray-box testing, the environment configuration information corresponding to the gray-box testing, and the gray-box testing item; the change trend data may be the trend data obtained by the monitoring model analyzing the trend chart of the operating parameter corresponding to the operating parameter.

[0065] The present invention may prompt the second warning message in ways including, but not limited to, common warning methods such as pop-up windows, new message prompts, and sound prompts. The present invention may display the second warning message, and the platform for displaying the second warning message may be a web page, an application, a mobile app that is synchronously communicated with the above-mentioned ends, a short message, a server, etc. The present invention does not make specific limitations on this.

[0066] In this embodiment, optionally, the second warning condition may include a stability warning condition, a first abnormal warning condition, and a second abnormal warning condition; the stability warning condition is used to indicate that there is a stability problem in the gray-box testing; the first abnormal warning condition is used to indicate that the gray-box testing is in a first abnormal state; the second abnormal warning condition is used to indicate that the gray-box testing 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 trend chart of the operating parameter corresponding to the operating parameter meets the second warning condition, determining that the gray-box testing is in a preset abnormal state includes steps A1 - A3:

[0068] Step A1. If the trend chart of the operating parameter corresponding to the operating parameter meets the stability warning condition, control the monitoring model to output that there is a stability problem in the gray-box testing, and output the second warning message corresponding to the stability warning condition; the second warning message corresponding to the stability warning condition may include, but is not limited to, the operating parameter that meets the stability warning condition and the change trend data corresponding to the operating parameter that meets the stability warning condition.

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

[0070] Step A3: If the operation parameter trend graph corresponding to the operation parameters meets the second abnormal warning condition, control the monitoring model to output that the gray-box test is in the second abnormal state, and output the second warning information corresponding to the second abnormal warning condition; the second warning information corresponding to the second abnormal warning condition may include, but is not limited to, the operation parameters that meet the second abnormal warning condition and the change trend data corresponding to the operation parameters that meet the second abnormal warning condition.

[0071] In the technical solution of this embodiment, by dividing the second warning condition into warning conditions of different degrees, when an abnormality occurs in the gray-box test, it can be more accurately judged which type of abnormal state the gray-box test currently corresponds to, that is, targeted abnormal inspections can be carried out, and the problems corresponding to the gray-box test can be adjusted quickly, improving the efficiency of the gray-box test.

[0072] In this embodiment, optionally, when certain specific change trends appear in the operation parameters, because they are different from the normal data change trends, they may be misjudged as abnormal data and warnings may be issued. However, in actual situations, the data with certain specific change trends is not abnormal data. Therefore, in order to avoid the principle of false alarms, it is necessary to monitor certain specific change trends. Specifically: based on the monitoring model, identify the data change trends of each operation parameter, and determine whether the gray-box test is in a preset abnormal state. It also includes: based on the monitoring model, determine whether the operation parameter trend graph corresponding to the operation parameters meets the false alarm condition; the false alarm condition is a condition indicating that the operation parameters match the preset operation data; if the operation parameter trend graph corresponding to the operation parameters meets the false alarm condition, the gray-box test is not in the preset abnormal state and no warning information is output.

[0073] Among them, the preset operation data can be understood as data whose change trend does not match the normal data, but is still the change trend information of the normal state data. The preset operation data is determined according to the characteristics of the actual business.

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

[0075] Exemplarily, the preset running data corresponding to the running parameter may be the trend information that the running parameter increases or decreases continuously at the first preset change rate for the first time period and then returns to normal.

[0076] If the running parameter is the QPS value, the preset running data may be that a preset number of QPS values are the preset value; the preset value may be zero.

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

[0078] If the running parameter is one of the memory occupancy rate, CPU occupancy rate, and delay, the preset running data corresponding to the running parameter may be the trend information that the test program corresponding to the gray-box testing restarts, and the running parameter decreases within the second time period after the restart.

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

[0080] The technical solution of this embodiment is based on the monitoring model to determine whether the running parameter trend graph corresponding to the running parameter meets the false alarm condition, so as to accurately judge whether there are certain specific change trends in the change trend of the running parameter, thereby avoiding misreporting normal situations as anomalies and realizing accurate monitoring of the running state of the gray-box testing.

[0081] The technical solution of the embodiment of the present invention determines the test container corresponding to the gray-box testing, and obtains at least one running parameter of the test container when running the gray-box testing; the running parameter is the data obtained by real-time collecting the running state data of the test container. Each running parameter is plotted into a running parameter trend graph corresponding to each running parameter; the running parameter trend graph is used to represent the data change trend of the running parameter over time, and the running parameter trend graph can effectively reflect the data change trend, so as to realize that based on the monitoring model subsequently, it can be quickly and accurately determined whether the running parameter trend graph corresponding to the running parameter meets the second warning condition, effectively avoiding the situation that the output result is inaccurate due to the insensitivity of the monitoring model to a large number of running parameters and directly processing a large number of running parameters, greatly improving the accuracy of the output result and also improving the efficiency of the model for data analysis; further, if the running parameter trend graph corresponding to the running parameter meets the second warning condition, it is determined that the gray-box testing is in the preset abnormal state and a second warning message is sent, realizing accurate determination of whether the gray-box testing has an anomaly and improving the accuracy of monitoring abnormal situations in the process of gray-box testing.

[0082] Embodiment III

[0083] Figure 4 This is a flowchart of another method for monitoring anomalies in grayscale testing provided by an embodiment of the present invention. The technical solution of this embodiment further describes in detail "determining whether the operation parameter trend graph corresponding to the operation parameter satisfies the second warning condition" on the basis of the above embodiment. This embodiment can be combined with various alternative solutions in one or more of the above embodiments. As Figure 4 shown, the method for monitoring anomalies in grayscale testing includes:

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

[0085] S320. Plot each operation parameter into an operation parameter trend graph corresponding to each operation parameter; the operation parameter trend graph is used to represent the data change trend of the operation parameter over time.

[0086] S330. In response to a data input operation, control the monitoring model to call the preset information corresponding to the operation parameter trend graph from the preset information library; the data input operation is an operation of inputting the operation parameter trend graph of each operation parameter into the monitoring model; the preset information library stores preset information for judging whether the operation parameter trend graph satisfies the second warning condition.

[0087] Among them, the monitoring model is associated with a preset information library, that is, the preset information library can be set inside or outside the monitoring model, and is used to assist the monitoring model in judging whether the grayscale test satisfies the second warning condition. The preset information can be understood as the trend information corresponding to different operation parameter trend graphs that satisfies the second warning condition.

[0088] S340. Based on the monitoring model, identify the operation parameter trend graph and the preset information, and determine whether the grayscale test satisfies the second warning condition. If the operation parameter trend graph corresponding to the operation parameter satisfies the second warning condition, it is determined that the grayscale test is in a preset abnormal state, and a second warning message is sent.

[0089] Specifically, based on the monitoring model, identify the operation parameter trend graph, obtain the third trend data corresponding to the operation parameter. When the third trend data matches the preset information corresponding to the operation parameter trend graph, it is determined that the grayscale test satisfies the second warning condition, control the monitoring model to output that the grayscale test is in a preset abnormal state, and send a second warning message.

[0090] Further, 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 the first abnormal state of the gray box testing; the second preset information may be parameter change trend information corresponding to the second abnormal state of the gray box testing; the third preset information may be parameter change trend information corresponding to the stability problem of the change trend of the running parameters;

[0091] The second warning condition may include a stability warning condition, a first abnormal warning condition, and a second abnormal warning condition; the first abnormal warning condition is used to indicate that the gray box testing is in the first abnormal state; the second abnormal warning condition is used to indicate that the gray box testing is in the 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 running parameter trend graph, it is determined that the gray box testing meets the second warning condition, and controlling the monitoring model to output that the gray box testing is in the preset abnormal state may include steps B1 - B3:

[0093] Step B1, if the third trend data meets the first preset information, determine whether the gray box testing meets the first abnormal warning condition, and control the monitoring model to output that the gray box testing is in the first abnormal state.

[0094] Exemplarily, if the running parameter is the CPU occupancy rate; the first preset information may be the information that the CPU occupancy rate is greater than the preset occupancy rate, and the situation that the CPU occupancy rate is greater than the preset occupancy rate lasts for at least the third time period. For example, the CPU occupancy rate is greater than 80% for a long time.

[0095] If the running parameter is the memory occupancy rate; the first preset information may be the information that the memory occupancy rate continuously increases, and the time when the memory occupancy rate continuously increases is greater than the fourth time period and the absolute value of the memory occupancy rate doubles. For example, the memory occupancy rate continuously rises, and the rising time exceeds 10 minutes and the absolute value doubles.

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

[0097] Step B2, if the third trend data meets the second preset information, determine whether the gray box testing meets the first abnormal warning condition, and control the monitoring model to output that the gray box testing is in the first abnormal state.

[0098] Exemplarily, if the running parameter is the CPU occupancy rate; the second preset information is the information that the CPU occupancy rate continuously increases, and the time when the CPU occupancy rate continuously increases is less than the fifth time period.

[0099] If the operating parameter is the memory occupancy rate; the second preset information is the information that the memory occupancy rate continues to increase, and the time during which the memory occupancy rate continues to increase is less than the sixth time period; the sixth time period is less than the fourth time period. For example, the memory occupancy rate continues to rise, but the rising time is short.

[0100] If the operating parameter is the latency, especially P90 and P99; the second preset information is the information that the latency is at least a preset multiple of the latency before the restart of the test program corresponding to the gray-box testing within at least the seventh time period; the second preset information is the information after the restart of the test program corresponding to the gray-box testing. For example, after the restart of the test program corresponding to the gray-box testing, the P90 and P99 latencies are 2 times or more of those before the program restart, and the values continue for a long time.

[0101] Step B3: If the third trend data meets the third preset information, determine whether the gray-box testing meets the stability warning condition, and control the monitoring model to output that there are stability problems in the gray-box testing.

[0102] Among them, the third preset information may be the information that the operating parameter continuously changes at the second preset change rate for at least the eighth time period within the monitoring window corresponding to the gray-box testing. For example, if there are continuous and obvious changes in the operating parameter within the monitoring window, there are stability problems with the test program.

[0103] The technical solution of the embodiment of the present invention determines the test container corresponding to the gray-box testing, and obtains at least one operating parameter of the test container when running the gray-box testing; the operating parameter is the data obtained by real-time collecting the operation state 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. In response to the data input operation, control the monitoring model to call the preset information corresponding to the operating parameter trend graph from the preset information library; store the preset information in the preset information library to facilitate accurately obtaining the data for comparison with the operating parameter trend graph, so as to accurately determine whether the gray-box testing meets the second warning condition subsequently. Based on the monitoring model to identify the operating parameter trend graph and the preset information, determine whether the gray-box testing meets the second warning condition. If the operating parameter trend graph corresponding to the operating parameter meets the second warning condition, determine that the gray-box testing is in the preset abnormal state and issue a second warning message, realizing the accurate determination of whether the gray-box testing has an abnormality and improving the accuracy of monitoring abnormal situations during the gray-box testing process.

[0104] Embodiment 4

[0105] Figure 5The figure is a schematic structural diagram of an abnormal monitoring device for grayscale testing provided by an embodiment of the present invention. This embodiment is applicable to the situation of abnormal monitoring of grayscale testing. The abnormal monitoring device for grayscale testing can be implemented in the form of hardware and / or software, and can be configured in any electronic device with network communication functions. As Figure 5 shown, the abnormal monitoring device for grayscale testing of the present invention includes:

[0106] A parameter acquisition module 410, configured to determine a test container corresponding to the grayscale test, and acquire at least one running parameter of the test container during the grayscale test; the running parameter is data obtained by real-time acquisition of the running state data of the test container;

[0107] A monitoring module 420, configured to determine whether the grayscale test is in a preset abnormal state based on a monitoring model and the running parameter.

[0108] Based on the above embodiment, optionally, the abnormal monitoring device for grayscale testing includes a data monitoring module. The data monitoring module is configured to, if the running time of the grayscale test is within a preset monitoring interval and at least one of the running parameters meets a first warning condition, send a first warning message; the preset monitoring interval is a time period of a preset duration starting from the start of the grayscale test; the first warning message at least includes the running parameter that meets the first warning condition and the first warning condition corresponding to the running parameter that meets the first warning condition; wherein, in response to at least one running parameter meeting the first warning condition, the grayscale test interrupts its operation.

[0109] Based on the above embodiment, optionally, at least one of the running parameters meets the first warning condition, including: if there is a preset parameter among at least one of the running parameters and / or the amplitude of the running parameter fluctuation is greater than or equal to a fluctuation abnormal threshold, it is determined that at least one of the running parameters meets the first warning condition; wherein, when the amplitude of the running parameter fluctuation is greater than or equal to the fluctuation abnormal threshold, it indicates that the grayscale test interrupts its execution.

[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 configured to determine the data change trend of each running parameter according to the running parameter; the monitoring unit is configured to identify the data change trend of each running parameter based on the monitoring model to determine whether the grayscale test is in a preset abnormal state.

[0111] Based on the above embodiments, optionally, the data change trend determination unit is configured 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.

[0112] Based on the above embodiments, optionally, 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 warning condition; the second warning condition is used to indicate that the gray-box testing is in a preset abnormal state; the first judgment subunit is configured to, if the operating parameter trend graph corresponding to the operating parameter satisfies the second warning condition, determine that the gray-box testing is in a preset abnormal state and issue a second warning message.

[0113] Based on the above embodiments, optionally, the second warning condition includes a stability warning condition, a first abnormal warning condition, and a second abnormal warning condition; the stability warning condition is used to indicate that there are stability problems in the gray-box testing; the first abnormal warning condition is used to indicate that the gray-box testing is in a first abnormal state; the second abnormal warning condition is used to indicate that the gray-box testing 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 subunit is configured to: if the operating parameter trend graph corresponding to the operating parameter satisfies the stability warning condition, control the monitoring model to output that there are stability problems in the gray-box testing; if the operating parameter trend graph corresponding to the operating parameter satisfies the first abnormal warning condition, control the monitoring model to output that the gray-box testing is in a first abnormal state; if the operating parameter trend graph corresponding to the operating parameter satisfies the second abnormal warning condition, control the monitoring model to output that the gray-box testing is in a second abnormal state.

[0115] Based on the above embodiments, optionally, the monitoring subunit is configured to: in response to a data input operation, control the monitoring model to call the preset information corresponding to the operating parameter trend graph from the preset information library; the data input operation is an operation of inputting the operating parameter trend graph of each of the operating parameters into the monitoring model; the preset information library stores preset information for judging whether the operating parameter trend graph satisfies the second warning condition; based on the recognition of the operating parameter trend graph and the preset information by the monitoring model, determine whether the gray-box testing satisfies the second warning condition.

[0116] Based on the above embodiments, optionally, the monitoring module includes a second monitoring unit, and the second monitoring unit is configured to: determine whether the operation parameter trend graph corresponding to the operation parameter satisfies a false alarm condition based on the monitoring model; the false alarm condition is a condition indicating that the operation parameter matches the preset operation data; if the operation parameter trend graph corresponding to the operation parameter satisfies the false alarm condition, then the gray box testing is not in a preset abnormal state and no alarm information is output.

[0117] Based on the above embodiments, optionally, obtaining at least one operation parameter of the test container includes: the sampling intervals and sampling frequencies corresponding to each operation parameter are different; the sampling interval is the time range for collecting data.

[0118] Based on the above embodiments, optionally, the operation parameter includes at least one of: the number of concurrent users, the memory occupancy rate, the CPU occupancy rate, the number of threads, the success rate, and the latency.

[0119] The abnormal monitoring device for gray box testing provided by the embodiments of the present invention can execute the abnormal monitoring method for gray box testing provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0120] Embodiment 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 The structural schematic diagram of an electronic device that can be used to implement the abnormal monitoring method for gray box testing in the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0123] As Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as read-only memory (ROM) 12, random access memory (RAM) 13, etc. Among them, the memory stores computer programs executable by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 12 or the computer programs loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. The input / output (I / O) interface 15 is also connected to the 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 disk, an optical disc, 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 through a computer network such as the Internet and / or various telecommunication networks.

[0125] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the abnormal monitoring method for grayscale testing.

[0126] In some embodiments, the abnormal 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 onto 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 abnormal monitoring method for grayscale testing described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the abnormal monitoring method for grayscale testing in any other appropriate manner (for example, by means of firmware).

[0127] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, 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 interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

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

[0129] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc 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 having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds 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 including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0132] The computing system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0133] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed 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, and no limitation is made herein.

[0134] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope 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 the grayscale test is running; the operating parameter is data obtained by real-time collection of operating status data of the test container; Based on the monitoring model and the operating parameters, it is determined whether the grayscale test is in a preset abnormal state.

2. The method according to claim 1, characterized in that 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 the first alarm condition, a first alarm message is issued; the preset monitoring interval is the time lasting for a preset period of time 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.

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

4. The method according to claim 1, characterized in that Determining whether the grayscale test is in a preset abnormal state based on the monitoring model and the operating parameters includes: Determine the data change trend of each of the operating parameters according to the operating parameters; Based on the monitoring model, the data change trend of each operating parameter is identified to determine whether the grayscale test is in a preset abnormal state.

5. The method according to claim 4, characterized in that Determining a data change trend of each of the operating parameters according to the operating parameters includes: Each of the operating parameters is plotted 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.

6. The method according to claim 5, characterized in that 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 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.

7. The method according to claim 6, 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, then 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 the 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 a second abnormal state.

8. The method according to claim 6, characterized in that Determining, based on the monitoring model, whether an operating parameter trend graph corresponding to the operating parameter satisfies a second alarm condition includes: In response to a data input operation, the monitoring model is controlled to call preset information corresponding to the operating parameter trend graph from the 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 judging 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.

9. The method according to claim 5, characterized in that 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 comprising: Based on the monitoring model, determining whether the operating parameter trend graph corresponding to the operating parameter satisfies 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.

10. The method according to claim 1, characterized in that Acquiring at least one operating parameter of the test container when running the grayscale test, including: 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.

11. The method according to any one of claims 1 to 10, characterized in that The operating parameters include: at least one of the number of concurrent connections, memory occupancy, CPU occupancy, number of threads, success rate, and latency.

12. An abnormality monitoring device for grayscale testing, characterized in that: The device comprises: A parameter acquisition module, used 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 running; the operating parameter is data obtained by real-time collection of operating status data of the test container; The monitoring module determines whether the grayscale test is in a preset abnormal state based on the monitoring model and the operating parameters.

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