System performance monitoring method, apparatus, device, medium, and program product

Abstract

The application provides a system performance monitoring method, device, equipment, medium and program product. The method comprises: obtaining a target performance index value generated by a target system; determining a business scenario mode corresponding to the target performance index value; determining a threshold type matched with the business scenario mode in a preset threshold management rule library according to the target performance index and the business scenario mode, wherein the threshold management rule library comprises threshold types corresponding to different performance indexes in different business scenario modes, and the different performance indexes comprise the target performance index; obtaining a performance index threshold corresponding to the target performance index according to the threshold type; and determining the performance of the target system according to a comparison result of the target performance index value and the performance index threshold. The application can obtain a performance index threshold most suitable for the current business state of the target system, thereby accurately evaluating the current performance of the target system and overcoming the defects existing when only a single performance index threshold is used.

Description

Technical Field

[0001] This application relates to the field of information processing technology, and in particular to a system performance monitoring method, apparatus, equipment, medium, and program product. Background Technology

[0002] System performance monitoring is a crucial step in ensuring stable system operation. Currently, related technologies primarily employ single-threshold monitoring mechanisms to monitor system performance, such as using only fixed-threshold monitoring or only dynamic-threshold monitoring.

[0003] However, fixed threshold monitoring mechanisms cannot adapt to dynamic changes in system load and cannot ensure the accuracy of monitoring results. For example, during peak business periods, system performance levels significantly improve, making fixed thresholds prone to triggering false alarms and increasing the burden on operations and maintenance personnel. Secondly, setting fixed thresholds is highly dependent on the experience and understanding of the system by operations and maintenance personnel; improper settings can also generate a large number of false alarms. Dynamic threshold monitoring mechanisms are complex and costly to implement. Dynamic threshold algorithms require continuous monitoring and adjustment, consuming significant computing resources and expertise, especially in large-scale or high-concurrency systems where the resource consumption of dynamic threshold schemes is particularly pronounced. Summary of the Invention

[0004] This application provides a system performance monitoring method, apparatus, equipment, medium, and program product to solve the defects of high error rate when using only fixed thresholds to achieve target system performance evaluation in the prior art, and high cost when using only dynamic thresholds to achieve target system performance evaluation.

[0005] This application provides a system performance monitoring method, including the following steps:

[0006] Obtain the target performance index values ​​to be processed generated by the target system;

[0007] Determine the business scenario mode corresponding to the target performance index value, wherein the business scenario mode represents the business state presented by the target system during operation;

[0008] Based on the target performance metric and the business scenario mode, a threshold type matching the business scenario mode is determined from a preset threshold management rule library. The threshold management rule library includes threshold types corresponding to different performance metrics under different business scenario modes, and the different performance metrics include the target performance metric.

[0009] Based on the threshold type, obtain the performance index threshold corresponding to the target performance index;

[0010] The performance of the target system is determined based on the comparison between the target performance index value and the performance index threshold.

[0011] According to the system performance monitoring method provided in this application, the threshold type includes a fixed threshold and a dynamic threshold; the step of obtaining the performance index threshold corresponding to the target performance index according to the threshold type includes:

[0012] If the threshold type is a fixed threshold, the performance index threshold corresponding to the target performance index is determined from a plurality of pre-stored fixed thresholds;

[0013] If the threshold type is a dynamic threshold, the historical index values ​​corresponding to the target performance index are analyzed using time series data smoothing technology to obtain the performance index threshold corresponding to the target performance index.

[0014] According to the system performance monitoring method provided in this application, the step of analyzing the historical index values ​​corresponding to the target performance index using time series data smoothing technology to obtain the performance index threshold corresponding to the target performance index includes:

[0015] Based on the timestamps carried by the historical indicator values, a preset number of historical indicator values ​​with timestamps within a preset time range are obtained from all historical indicator values ​​corresponding to the target performance indicator.

[0016] Determine the average value of the preset number of historical indicator values;

[0017] The performance index threshold corresponding to the target performance index is determined based on the average value.

[0018] According to the system performance monitoring method provided in this application, the step of obtaining a preset number of historical indicator values ​​whose timestamps are within a preset time range from all historical indicator values ​​corresponding to the target performance indicator based on the timestamps carried by the historical indicator values ​​includes:

[0019] Based on the timestamp carried by the historical indicator value, obtain the historical indicator value whose timestamp is within the preset time range from all historical indicator values ​​corresponding to the target performance indicator after preprocessing.

[0020] Among the historical indicator values ​​whose timestamps fall within the preset time range, obtain the latest preset number of historical indicator values.

[0021] According to the system performance monitoring method provided in this application, determining the business scenario mode corresponding to the target performance index value includes:

[0022] The target performance metric, the target performance metric value, and the timestamp corresponding to the target performance metric value are input into a preset random forest model, and the business scenario mode corresponding to the target performance metric value is determined based on the output of the random forest model.

[0023] The random forest model is obtained by pre-training an initial random forest model based on the historical index values ​​corresponding to the target performance index and the timestamps corresponding to each historical index value. The random forest model is used to identify business scenario patterns corresponding to different performance index values.

[0024] According to the system performance monitoring method provided in this application, the fixed threshold corresponding to the target performance index is determined through the following steps:

[0025] Obtain the historical index values ​​corresponding to the target performance index, and sort the historical index values ​​in ascending order;

[0026] The historical index values ​​arranged in ascending order are divided into multiple different intervals, including a first interval, a second interval, and a third interval. A first historical index value is determined in the first interval, a second historical index value is determined in the second interval, and a third historical index value is determined in the third interval.

[0027] Based on the first historical indicator value, the second historical indicator value, and the third historical indicator value, a fixed threshold corresponding to the target performance indicator is determined.

[0028] According to the system performance monitoring method provided in this application, after determining the performance of the target system, the method further includes:

[0029] If it is determined that the performance of the target system does not meet the preset requirements, an alarm message is generated based on the target performance index value and the performance index threshold corresponding to the target performance index value.

[0030] Output the alarm information.

[0031] This application also provides a system performance monitoring device, including the following modules:

[0032] The first acquisition module is used to acquire the target performance index values ​​to be processed generated by the target system.

[0033] The first determining module is used to determine the business scenario mode corresponding to the target performance index value, wherein the business scenario mode represents the business state presented by the target system during operation;

[0034] The second determining module is used to determine the threshold type that matches the business scenario mode in a preset threshold management rule library based on the target performance index and the business scenario mode. The threshold management rule library includes threshold types corresponding to different performance indices under different business scenario modes, and the different performance indices include the target performance index.

[0035] The second acquisition module is used to acquire the performance index threshold corresponding to the target performance index according to the threshold type.

[0036] The third determining module is used to determine the performance of the target system based on the comparison result between the target performance index value and the performance index threshold.

[0037] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a system performance monitoring method as described above.

[0038] This application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a system performance monitoring method as described above.

[0039] This application also provides a computer program product, including a computer program that, when executed by a processor, implements a system performance monitoring method as described above.

[0040] This application provides a system performance monitoring method, apparatus, device, medium, and program product. Implementing the system performance monitoring method of this application involves first obtaining the target performance index value to be processed generated by the target system; then, determining the business scenario mode corresponding to the target performance index value; next, determining the threshold type matching the business scenario mode from a preset threshold management rule base based on the target performance index and the business scenario mode; and obtaining the performance index threshold corresponding to the target performance index based on the threshold type; finally, determining the performance of the target system based on the comparison result between the target performance index value and the performance index threshold. This application determines the threshold type corresponding to each target performance index by setting threshold management rules, which can obtain the performance index threshold most suitable for the current business state of the target system, thereby accurately evaluating the current performance of the target system. This solution avoids both the high error rate that occurs when using only fixed thresholds for target system performance evaluation and the high cost that occurs when using only dynamic thresholds for target system performance evaluation. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating a system performance monitoring method according to an embodiment of this application;

[0043] Figure 2 This is a structural block diagram of a system performance monitoring device according to an embodiment of this application;

[0044] Figure 3 This is a schematic diagram of the physical structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] The system performance monitoring method of this application is implemented by a performance monitoring system, a system performance monitoring device, or any type of electronic device. The following will describe the system performance monitoring method of this application in detail, taking a performance monitoring system as the implementing entity as an example.

[0047] Figure 1 This is a flowchart illustrating a system performance monitoring method according to an embodiment of this application. (Refer to...) Figure 1 The system performance monitoring method of this application specifically includes the following steps:

[0048] Step 101: Obtain the target performance index values ​​to be processed generated by the target system.

[0049] In this application, the target system is the object to be monitored by the performance monitoring system. The performance monitoring system is responsible for monitoring various performance metrics generated by the target system in real time, thereby achieving performance evaluation of the target system.

[0050] The target system can be any type of system, such as a rail transit monitoring system in the rail transit industry, various financial analysis systems in the financial industry, or various medical analysis systems in the medical industry. The specific system can be set according to actual needs. This embodiment does not impose specific restrictions on the type of target system.

[0051] The target system typically includes at least one server. The performance monitoring system is used to monitor various performance metrics data generated during the operation of each server. A performance metric data includes the performance metric, the corresponding performance metric value, and a timestamp.

[0052] In this embodiment, various performance metrics are used to evaluate the target system's performance, including CPU-related metrics (e.g., CPU utilization, CPU load, CPU idle time), memory-related metrics (e.g., memory utilization, available memory), disk I / O-related metrics (e.g., disk read / write speed, disk utilization, disk I / O wait time), system load and process metrics (e.g., average system load, number of active processes), application health metrics (e.g., application response time, application memory usage, application thread status, application error rate), network status metrics (e.g., network throughput, number of network connections, network error rate, network latency), and custom metrics. This embodiment does not impose specific limitations on the types of performance metrics.

[0053] The target performance metric value refers to the specific numerical value of the target performance metric. For example, when the target performance metric is CPU utilization, the target performance metric value could be 30%; or when the target performance metric is the number of active processes, the target performance metric value could be 10.

[0054] The target performance metric is any one of a variety of performance metrics used to evaluate the performance of a target system.

[0055] For the sake of clarity in describing the methods of this application, unless otherwise specified, each of the following embodiments will be described with respect to the number of target performance index values.

[0056] During normal operation, the target system will generate metric values ​​for the same target performance metric at multiple different times. The performance monitoring system will monitor each metric value in real time to ensure it meets preset requirements. Target performance metric values ​​to be processed refer to those already generated by the target system but not yet processed by the performance monitoring system.

[0057] Step 102: Determine the business scenario mode corresponding to the target performance index value. The business scenario mode represents the business state presented by the target system during operation.

[0058] In this embodiment, the target system will exhibit various business states during operation, such as normal operation state (the state in which the target system operates according to the established workflow and parameters when no abnormalities or special events occur), peak period (the state in which the target system's operating load increases significantly due to increased business demand within a specific time period), maintenance window (the period during which the target system is scheduled to shut down for preventive maintenance, updates, upgrades, or troubleshooting), and abnormal state (unexpected or abnormal states that occur during the operation of the target system, which may lead to performance degradation, failures, or security risks).

[0059] The same target performance metric may generate different values ​​under different business scenarios. For example, CPU utilization may correspond to one metric value under normal operating conditions (let's say 30%), and another metric value (let's say 60%) when the target system is in peak periods.

[0060] At any given moment, a target performance metric value is generated within a specific business scenario mode of the target system. For example, at 12:12:12 on [Date], the CPU utilization rate (30%) cannot simultaneously occur under all four business scenario modes: normal operation, peak hours, maintenance window, and abnormal state. It can only occur under one of these four business scenario modes. Therefore, in this embodiment, the business scenario mode corresponding to the target performance metric value refers to the business scenario mode in which the target system is operating when the target performance metric value is generated.

[0061] In this embodiment, the business scenario mode corresponding to the target performance index value can be determined in any way. For example, the operating data of the target system when the target performance index value is generated can be intelligently analyzed to determine the business scenario mode of the target system. Alternatively, the business scenario mode corresponding to the target performance index value can be determined in other ways, which will be described later.

[0062] Step 103: Based on the target performance indicators and business scenario patterns, determine the threshold type that matches the business scenario pattern in the preset threshold management rule library. The threshold management rule library includes the threshold types corresponding to different performance indicators in different business scenario patterns, and the different performance indicators include the target performance indicators.

[0063] In this embodiment, the threshold types mainly include fixed thresholds and dynamic thresholds.

[0064] In this embodiment, the threshold management rule base includes multiple different performance metrics, and the threshold type corresponding to each performance metric under multiple different business scenario modes. The data records in the threshold management rule base are shown in Table 1 below:

[0065]

[0066] Table 1

[0067] In this embodiment, a performance metric corresponds to a threshold type under a certain business scenario mode.

[0068] When executing step 103, based on the target performance metric and the corresponding business scenario mode, the threshold type matching the business scenario mode can be directly determined from the threshold management rule base. For example, for target performance metric value 1 (the specific value of target performance metric 1), if its corresponding business scenario mode is business scenario mode 1, then by querying the threshold management rule base, it can be determined that the threshold type corresponding to target performance metric value 1 is a fixed threshold; as another example, for target performance metric value N (the specific value of target performance metric N), if its corresponding business scenario mode is business scenario mode M, then by querying the threshold management rule base, it can be determined that the threshold type corresponding to target performance metric value N is a dynamic threshold.

[0069] In this embodiment, the information in the threshold management rule base is set according to the user's actual needs.

[0070] Among them, the fixed threshold refers to the performance index threshold determined before obtaining the target performance index value to be processed, while the dynamic threshold refers to the performance index threshold temporarily determined after obtaining the target performance index value to be processed.

[0071] Step 104: Based on the threshold type, obtain the performance index threshold corresponding to the target performance index.

[0072] In this embodiment, after determining the threshold type, a specific performance indicator threshold can be further obtained. For example, for a target performance indicator value of 30% (CPU utilization), if the corresponding threshold type is a fixed threshold, the corresponding performance indicator threshold, such as 35%, is further obtained from the pre-stored fixed threshold information. As another example, for a target performance indicator value of 30% (CPU utilization), if the corresponding threshold type is a dynamic threshold, a corresponding dynamic threshold is temporarily generated to determine the performance indicator threshold.

[0073] Step 105: Determine the performance of the target system based on the comparison results between the target performance index value and the performance index threshold.

[0074] Specifically, step 105 may include:

[0075] If the comparison results are within the preset range, the performance of the target system is determined to meet the standard.

[0076] If the comparison result is not within the preset range, the performance of the target system is determined to be substandard.

[0077] After step 104, the target performance index value can be compared with the performance index threshold, and the comparison result can be checked to see if it is within the preset range, thereby determining whether the performance of the target system meets the standard.

[0078] For example, if the target performance metric is CPU utilization, the target performance metric value is 30%, and the performance metric threshold is 40%, then the comparison result is that the target performance metric value is less than the performance metric threshold. Assuming the preset range is 0 <= CPU utilization <= 40%, this means the comparison result is within the preset range, and the target system's performance meets the standard. If the target performance metric value is 50%, then the comparison result is that the target performance metric value is greater than the performance metric threshold, and the comparison result is not within the preset range, meaning the target system's performance does not meet the standard.

[0079] In this embodiment, the performance index threshold corresponding to the target performance index can be one or more.

[0080] For example, if the target performance metric is the number of active processes, the target performance metric value is 19, and the performance metric thresholds are 1 and 40, then the comparison result is that the target performance metric value is greater than 1 and less than 40. Assuming the specified preset range is 1 <= number of active processes <= 40, this means the comparison result is within the preset range, and the target system's performance meets the standard. If the target performance metric value is 50, then the comparison result is that the target performance metric value is greater than the performance metric threshold of 40, and the comparison result is not within the preset range, meaning the target system's performance does not meet the standard.

[0081] The system performance monitoring method of this application first obtains the target performance index values ​​to be processed generated by the target system; then, it determines the business scenario mode corresponding to the target performance index value; next, based on the target performance index and the business scenario mode, it determines the threshold type matching the business scenario mode from a preset threshold management rule base, and obtains the performance index threshold corresponding to the target performance index based on the threshold type; finally, it determines the performance of the target system based on the comparison result between the target performance index value and the performance index threshold. This application determines the threshold type corresponding to each target performance index by setting threshold management rules, which can obtain the performance index threshold most suitable for the current business state of the target system, thereby accurately evaluating the current performance of the target system. This scheme avoids the high error rate that occurs when using only fixed thresholds to evaluate the performance of the target system, and also avoids the high cost that occurs when using only dynamic thresholds to evaluate the performance of the target system.

[0082] In conjunction with the above embodiments, in one implementation, in step 104, obtaining the performance index threshold corresponding to the target performance index according to the threshold type may specifically include:

[0083] Step 1041: If the threshold type is a fixed threshold, determine the performance index threshold corresponding to the target performance index from multiple pre-stored fixed thresholds.

[0084] Step 1042: If the threshold type is dynamic threshold, analyze the historical index values ​​corresponding to the target performance index using time series data smoothing technology to obtain the performance index threshold corresponding to the target performance index.

[0085] In this embodiment, the performance monitoring system pre-determines and stores the fixed thresholds corresponding to each performance indicator. Therefore, after determining that the threshold type corresponding to the target performance indicator value is a fixed threshold, the fixed threshold corresponding to the target performance indicator can be determined from the multiple pre-stored fixed thresholds and used as the performance indicator threshold corresponding to the target performance indicator value.

[0086] If the threshold type corresponding to the target performance indicator value is determined to be a dynamic threshold, then a temporary dynamic threshold corresponding to the target performance indicator needs to be generated as the performance indicator threshold corresponding to the target performance indicator value. Specifically, the process of generating a dynamic threshold includes:

[0087] Step 1: Read the historical index values ​​corresponding to the target performance index from the time series database.

[0088] Each historical metric value carries a timestamp. The historical metric values ​​retrieved can be all historical metric values ​​or historical metric values ​​within a specified time interval. The specified time interval can be, for example, the most recent 3 months or the most recent 6 months, and can be set according to actual needs.

[0089] For example, if the target performance metric is memory usage, then the specific values ​​of memory usage generated in the most recent 3 months can be read from the time-series database.

[0090] Step 2: Analyze the historical index values ​​obtained in Step 1 using time series data smoothing techniques to obtain the performance index threshold corresponding to the target performance index.

[0091] Time series data smoothing techniques are used to eliminate or reduce random fluctuations and noise in data, thereby revealing long-term trends and seasonal variations, and analyzing the inherent patterns and potential dynamics of the data. Simple Moving Average (SMA), Weighted Moving Average (WMA), and Exponential Moving Average (EMA) are commonly used methods in time series data smoothing. In step 2, a suitable method can be used to determine the dynamic threshold according to actual needs. For example, the simple moving average algorithm can be used to analyze all historical indicator values, and the resulting dynamic threshold can be used as the performance indicator threshold corresponding to the target performance indicator.

[0092] In this embodiment, a dynamic threshold is obtained by using time series data smoothing technology based on the historical index values ​​corresponding to the target performance index. This ensures that the obtained dynamic threshold is the most suitable performance index threshold for the current business state of the target system, which helps to improve the accuracy of the target system performance evaluation results.

[0093] In conjunction with the above embodiments, in one implementation, analyzing the historical index values ​​corresponding to the target performance index using time series data smoothing technology to obtain the performance index threshold corresponding to the target performance index may include:

[0094] Based on the timestamps carried by the historical indicator values, obtain a preset number of historical indicator values ​​whose timestamps are within a preset time range from all historical indicator values ​​corresponding to the target performance indicator.

[0095] Determine the average value of a preset number of historical indicator values;

[0096] The performance threshold corresponding to the target performance indicator is determined based on the average value.

[0097] In this embodiment, since the time series database stores a large number of data, analyzing all historical index values ​​corresponding to the target performance index would consume a lot of computing resources and reduce the efficiency of performance evaluation of the target system. Therefore, historical index values ​​generated within a preset time range can be initially selected from all historical index values ​​corresponding to the target performance index, and then a preset number of historical index values ​​can be further selected from the initially selected historical index values.

[0098] The method for selecting a preset number of historical indicator values ​​from the initially selected historical indicator values ​​can be determined based on a pre-set filtering strategy. For example, the filtering strategy could be to select any preset number of historical indicator values, to select the most recent preset number of historical indicator values, or to select a preset number of historical indicator values ​​located in the middle of a preset time range.

[0099] In this embodiment, determining the performance index threshold corresponding to the target performance index based on the average value may include:

[0100] The average value is determined as the performance indicator threshold corresponding to the target performance indicator; or

[0101] The product of the average value and the threshold coefficient is determined as the performance index threshold corresponding to the target performance index.

[0102] The threshold coefficient can be set based on experience. The product of the average value and the threshold coefficient is determined as the performance index threshold corresponding to the target performance index. This can improve the accuracy of the determined performance index threshold, thereby improving the accuracy of the performance evaluation results.

[0103] This embodiment can obtain the performance index thresholds most suitable for the current business state of the target system, which helps to improve the accuracy of the target system performance evaluation results.

[0104] In conjunction with the above embodiments, in one implementation, obtaining a preset number of historical indicator values ​​with timestamps within a preset time range from all historical indicator values ​​corresponding to the target performance indicator may include:

[0105] Based on the timestamps carried by the historical indicator values, obtain the historical indicator values ​​whose timestamps are within a preset time range from all the historical indicator values ​​corresponding to the target performance indicator after preprocessing.

[0106] Retrieve the latest preset number of historical indicator values ​​from the historical indicator values ​​whose timestamps are within a preset time range.

[0107] In this embodiment, all the data stored in the time series database are preprocessed data. That is, in the time series database, all historical index values ​​corresponding to each performance index are preprocessed historical index values.

[0108] In practice, firstly, from all historical index values ​​corresponding to the target performance index after preprocessing, the historical index values ​​generated within a preset time range are initially selected. Then, from the initially selected historical index values, the latest preset number of historical index values ​​are used as the final preset number of historical index values.

[0109] The preprocessing operations include, but are not limited to: data cleaning (handling missing values, outliers, duplicate data, etc.) and format conversion (converting data into a format suitable for analysis or storage, such as adjusting the format of timestamps or converting data to specific units). The purpose of preprocessing operations is to improve data quality; therefore, this application may use any preprocessing operation that can improve both quantity and quality.

[0110] In related technologies, performance monitoring using dynamic thresholds does not preprocess historical data. If historical data deviates or becomes abnormal, the accuracy of the dynamic threshold decreases, thus reducing the stability of the performance monitoring results. This embodiment, however, uses a preset number of preprocessed historical indicator values ​​to determine the dynamic threshold, overcoming the problems in related technologies and ensuring the accuracy of the final determined dynamic threshold. Furthermore, using the latest preset number of historical indicator values ​​to determine the dynamic threshold further improves its accuracy, thereby ensuring the accuracy of the performance evaluation.

[0111] In this embodiment, the time-series database stores a sufficiently rich amount of historical data by default. When the performance monitoring system is first launched, there may be insufficient historical data. This can be avoided by modifying the threshold management rule base. For example, the threshold types corresponding to different performance indicators under different business scenario modes can be modified to fixed thresholds. The initial values ​​of these fixed thresholds can be determined based on manual experience. After the time-series database stores a sufficiently rich amount of historical data, the information in the threshold management rule base is readjusted based on the historical data, and the initial values ​​of the fixed thresholds are also readjusted based on the historical data. Subsequently, the initial values ​​of each fixed threshold can be updated every preset time interval based on the historical data stored in the time-series database, thereby improving the accuracy of the fixed thresholds and the accuracy of the performance evaluation results. In one implementation, in step 102, determining the business scenario mode corresponding to the target performance indicator value may specifically include:

[0112] Input the target performance metric, the target performance metric value, and the timestamp corresponding to the target performance metric value into the preset random forest model, and determine the business scenario mode corresponding to the target performance metric value based on the output of the random forest model.

[0113] The random forest model is trained in advance based on the historical index values ​​corresponding to the target performance index and the timestamps corresponding to each historical index value. The random forest model is used to identify the business scenario patterns corresponding to different performance index values.

[0114] In this embodiment, the random forest model is an ensemble learning method that improves the accuracy and robustness of predictions by constructing multiple decision trees and summarizing their prediction results. The training process of the random forest model in this application, which can identify business scenario patterns corresponding to different performance index values, includes:

[0115] Step 1: Data Preparation. Obtain the preprocessed data used to train the model from the time series database. This data includes historical values ​​for various performance metrics.

[0116] Step 2: Feature Selection. Based on business needs and data characteristics, select or create features that help identify business scenario patterns. Feature selection is not simply extracting features from the data obtained in Step 1, but rather analyzing and processing the data to obtain indicators that reflect different business states of the target system. For example, the data obtained in Step 1 includes multiple historical values ​​corresponding to performance indicators such as CPU utilization and memory utilization. The feature selection process may involve calculating the rate of change, average value, and peak value of these performance indicators to obtain features that better reflect the business state of the target system.

[0117] Step 3: Data Tagging. Assign a tag to each data entry that represents the business scenario pattern. This can be obtained through manual annotation or automatic annotation based on an existing knowledge base.

[0118] Step 4: Divide the data obtained in Step 3 into training and test sets. The training set is used to train the random forest model, and the test set is used to evaluate the performance of the trained random forest model.

[0119] Step 5: Train the Random Forest Model. First, construct decision trees. For each tree in the random forest, randomly select a subset of samples from the training set. At each node of the decision tree, randomly select a subset of features (usually about the square root of the total number of features), and then select the optimal split point based on these features. Repeat this process until the maximum depth of the tree is reached or the number of samples in the node is less than a certain threshold. Then, summarize the prediction results of all decision trees. In this application, which is a classification problem (such as the recognition of business scenario patterns), majority voting can be used to determine the final recognition result.

[0120] Step 6: Model tuning and validation.

[0121] In Step 6, time series cross-validation can be used to evaluate model performance. Since time series data are time-dependent, this method ensures the model performs well on unseen data, thus avoiding overfitting or underfitting.

[0122] In Step 6, during the cross-validation process using time series cross-validation, metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Symmetric Mean Absolute Percentage Error (sMAPE) can be used to measure the accuracy of the model's predictions on each validation set. When using MAE, the average of the absolute differences between predicted and true values ​​is calculated; this value reflects the magnitude of the prediction error, with a smaller value indicating better model performance. When using RMSE, the square root of the average of the squares of the differences between predicted and true values ​​is calculated. Compared to MAE, RMSE penalizes larger errors more severely, making it more sensitive to outliers. Symmetric Mean Absolute Percentage Error is a percentage error metric that considers the relative difference between predicted and true values; a smaller value indicates higher prediction accuracy. Therefore, if the model performs poorly during a certain period (such as peak hours or maintenance windows), the model's parameters or structure can be adjusted based on the validation results. When tuning parameters, you can adjust parameters of the random forest, such as the number of trees, maximum depth, minimum number of sample splits, etc., to optimize model performance.

[0123] In this embodiment, after obtaining the trained random forest model, the target performance index, the target performance index value, and the timestamp corresponding to the target performance index value are input into the trained random forest model, and the random forest model can output the recognition result, that is, the business scenario mode corresponding to the target performance index value.

[0124] In this embodiment, the random forest algorithm is used to determine the business scenario mode corresponding to the target performance index value. This can accurately identify the current business state of the target system, which helps to accurately determine the performance index threshold corresponding to the target performance index and improve the accuracy of the performance evaluation results of the target system.

[0125] In conjunction with the above embodiments, in one implementation, the fixed threshold corresponding to the target performance index is determined in advance through the following steps:

[0126] Obtain the historical index values ​​corresponding to the target performance index and sort the historical index values ​​in ascending order;

[0127] The historical index values, sorted in ascending order, are divided into multiple different intervals, including a first interval, a second interval, and a third interval. A first historical index value is determined in the first interval, a second historical index value is determined in the second interval, and a third historical index value is determined in the third interval. Based on the first historical index value, the second historical index value, and the third historical index value, a fixed threshold corresponding to the target performance index is determined.

[0128] In this embodiment, when obtaining historical indicator values ​​corresponding to the target performance indicator, historical indicator values ​​with timestamps within a set time range (set according to actual needs) in the time series database can be obtained. For example, historical indicator values ​​generated in the last three months or the last six months can be obtained.

[0129] In this process, the historical indicator values ​​are also pre-processed data to ensure the accuracy of the final fixed threshold.

[0130] After obtaining the historical indicator values, they are sorted in ascending order from smallest to largest. Then, all the sorted historical indicator values ​​are divided into multiple intervals, including a first interval, a second interval, and a third interval. The first historical indicator value is determined in the first interval, the second historical indicator value in the second interval, and the third historical indicator value in the third interval. The first, second, and third intervals can be determined from among the multiple intervals based on actual needs. When obtaining the first, second, and third historical indicator values, values ​​can also be taken from the corresponding intervals according to actual needs. This embodiment does not impose specific restrictions on the method of dividing the intervals or the method of obtaining the historical indicator values ​​within the corresponding intervals.

[0131] In one implementation, all historical indicator values ​​sorted in ascending order can be divided into four equal parts. Then, following the ascending order of historical indicator values, the first part is designated as the first interval, the second part as the second interval, and the third part as the third interval. Next, the maximum value in the first part is designated as the first historical indicator value, the maximum value in the second part as the second historical indicator value, and the maximum value in the third part as the third historical indicator value. In this way, the first historical indicator value is the maximum value among the top 25% of the historical indicator values ​​sorted in ascending order, the second historical indicator value is the maximum value among the top 50% of the historical indicator values ​​sorted in ascending order, and the third historical indicator value is the maximum value among the top 75% of the historical indicator values ​​sorted in ascending order. For example, if there are 100 historical indicator values ​​sorted in ascending order, the final determined first historical indicator value is the 25th historical indicator value, the second historical indicator value is the 50th historical indicator value, and the third historical indicator value is the 75th historical indicator value.

[0132] In another implementation, all historical indicator values ​​sorted in ascending order can be divided into four equal parts. Then, following the ascending order of historical indicator values, the second part is designated as the first interval, the third part as the second interval, and the fourth part as the third interval. Next, the minimum value in the second part can be designated as the first historical indicator value, the minimum value in the third part as the second historical indicator value, and the minimum value in the fourth part as the third historical indicator value. In this way, the first historical indicator value is the minimum of all historical indicator values ​​greater than the top 25% of the historical indicator values ​​sorted in ascending order. The second historical indicator value is the minimum of all historical indicator values ​​greater than the top 50% of the historical indicator values ​​sorted in ascending order. The third historical indicator value is the minimum of all historical indicator values ​​greater than the top 75% of the historical indicator values ​​sorted in ascending order. For example, there are a total of 100 historical indicator values ​​after being sorted in ascending order. The first historical indicator value is the 26th historical indicator value, the second historical indicator value is the 51st historical indicator value, and the third historical indicator value is the 76th historical indicator value.

[0133] In both of the above methods, dividing all historical indicator values ​​into multiple intervals and ignoring the historical indicator values ​​in the head and tail intervals when obtaining the first to third historical indicator values ​​can avoid the influence of extreme historical indicator values ​​on the fixed threshold and improve the accuracy of the fixed threshold.

[0134] Next, a fixed threshold corresponding to the target performance indicator can be determined based on the first, second, and third historical indicator values. For example, when Q1, Q2, and Q3 represent the first, second, and third historical indicator values ​​respectively, the fixed threshold can be [Q3 + 1.5 * (Q3 - Q1)] or [Q1 - 1.5 * (Q3 - Q1)]. In practical applications, [Q3 + 1.5 * (Q3 - Q1)] can be used as the upper limit and [Q1 - 1.5 * (Q3 - Q1)] as the lower limit. That is, values ​​less than [Q1 - 1.5 * (Q3 - Q1)] or greater than [Q3 + 1.5 * (Q3 - Q1)] are considered outliers. Of course, how to set the outlier range based on the fixed threshold can be determined according to actual business needs.

[0135] This embodiment can improve the accuracy of fixed thresholds corresponding to different target performance indicators, thereby improving the accuracy of the performance evaluation results of the target system.

[0136] In conjunction with the above embodiments, in one implementation, after determining the performance of the target system, the method of this application may further include:

[0137] If it is determined that the performance of the target system does not meet the preset requirements, an alarm message is generated based on the target performance index value and the performance index threshold corresponding to the target performance index value.

[0138] Output alarm information.

[0139] In this embodiment, if the performance of the target system does not meet the preset requirements, the performance monitoring system automatically generates and outputs alarm information to promptly remind relevant maintenance personnel to check the target system.

[0140] In one implementation, the alarm information may include not only the target performance index value and the performance index threshold corresponding to the target performance index value, but also the business scenario mode corresponding to the target performance index value, so that relevant operation and maintenance personnel can check in a timely manner whether the performance index threshold setting is reasonable, and readjust the performance index threshold when it is unreasonable, thereby ensuring the accuracy of the subsequent performance evaluation results of the target system.

[0141] The system performance monitoring method of this application will be described below with a complete embodiment, which includes a pre-processing stage and a real-time monitoring stage, and specifically includes the following steps:

[0142] (a) Preliminary stage:

[0143] Step 1: Deploy components such as Prometheus, IoTDB, Grafana, Ansible, and AlertManager in the performance monitoring system and ensure that each component is running normally.

[0144] Step 2: Prometheus is responsible for monitoring multiple servers in the target system and collecting various performance metrics data (including performance metrics, performance metric values, and timestamps) generated by each server.

[0145] Step 3: Set up the data source in Grafana, connect to Prometheus, create a dashboard, use PromQL statements to query a specific performance metric, and analyze and display the queried performance metric data.

[0146] Step 4: Preprocess all collected performance metric data. Specifically, clean all collected performance metric data and convert it to a suitable format to improve data quality. Finally, store the preprocessed performance metric data in the IoTDB time-series database.

[0147] Prometheus is a system monitoring and alerting tool suite suitable for cloud-native environments. It collects time-series data from a configured target (the target system in this example) via the HTTP protocol. By configuring the `scrape_configs` section of Prometheus, multiple servers (or applications) within the target system can be specified as monitoring targets. Prometheus pulls performance metric data from the configured target based on the configured time intervals; all performance metric data is time-series data.

[0148] Grafana is a platform-independent analytics and interactive visualization software that supports querying, visualization, and alerting. In Grafana, after adding Prometheus as a data source, by configuring the Prometheus data source URL and other necessary authentication information, Grafana can connect to Prometheus and access all the data collected by Prometheus.

[0149] A Dashboard is a concept in Grafana that allows users to organize and display multiple dashboards, each of which can show the results of one or more queries.

[0150] PromQL (Prometheus Query Language) is the query language for Prometheus, used to extract data from the Prometheus time-series database. Using Grafana's charts and visualization tools, users can visually view and analyze the data collected from Prometheus.

[0151] IoTDB (Internet of Things Database) is a time-series database designed specifically for Internet of Things (IoT) and big data applications. IoTDB features efficient data compression, fast query performance, and the ability to support complex queries.

[0152] Ansible is an automation platform whose core functionality is implemented through playbooks (YAML files). Playbooks define the tasks to be executed, the goals, the execution order, and the dependencies between tasks. After determining fixed or dynamic thresholds, Ansible can apply these fixed thresholds to a performance monitoring system, which may involve updating configuration files, restarting services, or sending commands to the performance monitoring system to apply new performance metric thresholds.

[0153] AlertManager is part of Prometheus and is responsible for handling alerts from Prometheus. When Prometheus detects that a performance metric value does not match a preset threshold, it generates an alert and sends it to AlertManager.

[0154] (II) Real-time monitoring phase

[0155] Step 1: Obtain a target performance indicator value currently generated by the target system. For example, the target performance indicator value could be 30% (CPU utilization).

[0156] Step 2: Determine the business scenario pattern corresponding to the target performance index value using a pre-trained random forest model;

[0157] Step 3: In the preset threshold management rule library, determine the threshold type that matches the business scenario pattern. If the threshold type is a fixed threshold, determine the performance indicator threshold corresponding to the target performance indicator from multiple pre-stored fixed thresholds. If the threshold type is a dynamic threshold, analyze the historical indicator values ​​corresponding to the target performance indicator through time series data smoothing technology to obtain the performance indicator threshold corresponding to the target performance indicator.

[0158] Step 4: Determine the performance of the target system based on the comparison results between the target performance index value and the performance index threshold.

[0159] This application proposes a novel threshold management mechanism that cleverly integrates fixed and dynamic thresholds, achieving more accurate alarms. This new threshold management mechanism possesses at least the following technical advantages:

[0160] (1) It avoids the high error rate that occurs when only a fixed threshold is used to evaluate the performance of the target system (which cannot fully adapt to the load changes of the target system and the setting of the fixed threshold depends on the experience of the operation and maintenance personnel).

[0161] (2) It avoids the high cost that exists when using only dynamic thresholds to evaluate the performance of the target system.

[0162] (3) Both dynamic thresholds and fixed thresholds use pre-processed historical data, which can ensure data quality, improve the accuracy of performance index thresholds, and avoid unstable performance monitoring results.

[0163] (4) In this application, by setting threshold management rules to determine the threshold type corresponding to each target performance indicator, the most suitable performance indicator threshold for the current business state of the target system can be obtained, thereby accurately evaluating the current performance of the target system and enhancing the reliability and stability of the target system. The following describes a system performance monitoring device provided in this application. The system performance monitoring device described below can be referred to in correspondence with the system performance monitoring method described above.

[0164] Figure 2 This is a structural block diagram of a system performance monitoring device according to an embodiment of this application. (Refer to...) Figure 2 The system performance monitoring device 200 of this application includes:

[0165] The first acquisition module 201 is used to acquire the target performance index value to be processed generated by the target system;

[0166] The first determining module 202 is used to determine the business scenario mode corresponding to the target performance index value, wherein the business scenario mode represents the business state presented by the target system during operation;

[0167] The second determining module 203 is used to determine the threshold type that matches the business scenario mode in a preset threshold management rule library based on the target performance index and the business scenario mode. The threshold management rule library includes threshold types corresponding to different performance indices under different business scenario modes, and the different performance indices include the target performance index.

[0168] The second acquisition module 204 is used to acquire the performance index threshold corresponding to the target performance index according to the threshold type.

[0169] The third determining module 205 is used to determine the performance of the target system based on the comparison result between the target performance index value and the performance index threshold.

[0170] According to the system performance monitoring device 200 provided in this application, the threshold type includes fixed threshold and dynamic threshold; the second acquisition module 204 includes:

[0171] The first acquisition submodule is used to determine the performance index threshold corresponding to the target performance index from a plurality of pre-stored fixed thresholds if the threshold type is a fixed threshold.

[0172] The second acquisition submodule is used to analyze the historical index values ​​corresponding to the target performance index using time series data smoothing technology if the threshold type is a dynamic threshold, so as to obtain the performance index threshold corresponding to the target performance index.

[0173] According to the system performance monitoring device 200 provided in this application, the second acquisition submodule includes:

[0174] The third acquisition submodule is used to acquire a preset number of historical indicator values ​​whose timestamps are within a preset time range from all historical indicator values ​​corresponding to the target performance indicator, based on the timestamps carried by the historical indicator values.

[0175] The first determining submodule is used to determine the average value of the preset number of historical indicator values;

[0176] The second determining submodule is used to determine the performance index threshold corresponding to the target performance index based on the average value.

[0177] According to the system performance monitoring device 200 provided in this application, the third acquisition submodule includes:

[0178] The fourth acquisition submodule is used to acquire historical indicator values ​​whose timestamps are within the preset time range from all historical indicator values ​​corresponding to the target performance indicator after preprocessing, based on the timestamps carried by the historical indicator values.

[0179] The fifth acquisition submodule is used to acquire the latest preset number of historical indicator values ​​from the historical indicator values ​​whose timestamps are within the preset time range.

[0180] According to the system performance monitoring device 200 provided in this application, the first determining module 201 includes:

[0181] The third determination submodule is used to input the target performance indicator, the target performance indicator value and the timestamp corresponding to the target performance indicator value into a preset random forest model, and determine the business scenario mode corresponding to the target performance indicator value based on the output result of the random forest model.

[0182] The random forest model is obtained by pre-training an initial random forest model based on the historical index values ​​corresponding to the target performance index and the timestamps corresponding to each historical index value. The random forest model is used to identify business scenario patterns corresponding to different performance index values.

[0183] According to the system performance monitoring device 200 provided in this application, it further includes:

[0184] The third acquisition module is used to acquire the historical index values ​​corresponding to the target performance index and sort the historical index values ​​in ascending order.

[0185] The fourth determining module is used to divide the historical index values ​​after ascending order into multiple different intervals, the multiple different intervals including a first interval, a second interval and a third interval, and to determine a first historical index value in the first interval, a second historical index value in the second interval, and a third historical index value in the third interval.

[0186] The fifth determining module is used to determine a fixed threshold corresponding to the target performance indicator based on the first historical indicator value, the second historical indicator value, and the third historical indicator value.

[0187] According to the system performance monitoring device 200 provided in this application, it further includes:

[0188] The generation module is used to generate alarm information based on the target performance index value and the performance index threshold corresponding to the target performance index value if the performance of the target system does not meet the preset requirements after the performance of the target system is determined.

[0189] The output module is used to output the alarm information.

[0190] Figure 3 This is a schematic diagram of the physical structure of an electronic device according to an embodiment of this application, as shown below. Figure 3 As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communications interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute a system performance monitoring method, which includes:

[0191] Obtain the target performance index values ​​to be processed generated by the target system;

[0192] Determine the business scenario mode corresponding to the target performance index value, wherein the business scenario mode represents the business state presented by the target system during operation;

[0193] Based on the target performance metric and the business scenario mode, a threshold type matching the business scenario mode is determined from a preset threshold management rule library. The threshold management rule library includes threshold types corresponding to different performance metrics under different business scenario modes, and the different performance metrics include the target performance metric.

[0194] Based on the threshold type, obtain the performance index threshold corresponding to the target performance index;

[0195] The performance of the target system is determined based on the comparison between the target performance index value and the performance index threshold.

[0196] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0197] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a system performance monitoring method provided by the above methods, which includes: obtaining the target performance index value to be processed generated by the target system.

[0198] Determine the business scenario mode corresponding to the target performance index value, wherein the business scenario mode represents the business state presented by the target system during operation;

[0199] Based on the target performance metric and the business scenario mode, a threshold type matching the business scenario mode is determined from a preset threshold management rule library. The threshold management rule library includes threshold types corresponding to different performance metrics under different business scenario modes, and the different performance metrics include the target performance metric.

[0200] Based on the threshold type, obtain the performance index threshold corresponding to the target performance index;

[0201] The performance of the target system is determined based on the comparison between the target performance index value and the performance index threshold.

[0202] In another aspect, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform a system performance monitoring method provided by the methods described above, the method comprising: acquiring a target performance index value to be processed generated by the target system;

[0203] Determine the business scenario mode corresponding to the target performance index value, wherein the business scenario mode represents the business state presented by the target system during operation;

[0204] Based on the target performance metric and the business scenario mode, a threshold type matching the business scenario mode is determined from a preset threshold management rule library. The threshold management rule library includes threshold types corresponding to different performance metrics under different business scenario modes, and the different performance metrics include the target performance metric.

[0205] Based on the threshold type, obtain the performance index threshold corresponding to the target performance index;

[0206] The performance of the target system is determined based on the comparison between the target performance index value and the performance index threshold.

[0207] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0208] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0209] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A system performance monitoring method, characterized in that, include: Obtain the target performance index values ​​to be processed generated by the target system; Determine the business scenario mode corresponding to the target performance index value. The business scenario mode represents the business state presented by the target system during operation. The business state includes normal operation state, peak period, maintenance window, and abnormal state. Based on the target performance metric and the business scenario mode, a threshold type matching the business scenario mode is determined from a preset threshold management rule library. The threshold management rule library includes threshold types corresponding to different performance metrics under different business scenario modes, and the different performance metrics include the target performance metric. Based on the threshold type, obtain the performance index threshold corresponding to the target performance index; The performance of the target system is determined based on the comparison between the target performance index value and the performance index threshold. The threshold types include fixed thresholds and dynamic thresholds. Obtaining the performance index threshold corresponding to the target performance index based on the threshold type includes: If the threshold type is a fixed threshold, the performance index threshold corresponding to the target performance index is determined from a plurality of pre-stored fixed thresholds. The fixed threshold corresponding to the target performance index is determined by the following steps: obtaining the historical index values ​​corresponding to the target performance index and sorting the historical index values ​​in ascending order; dividing the ascendingly sorted historical index values ​​into a plurality of different intervals, the plurality of different intervals including a first interval, a second interval, and a third interval, and determining a first historical index value in the first interval, a second historical index value in the second interval, and a third historical index value in the third interval; determining the fixed threshold corresponding to the target performance index based on the first historical index value, the second historical index value, and the third historical index value. If the threshold type is a dynamic threshold, the historical index values ​​corresponding to the target performance index are analyzed using time series data smoothing technology to obtain the performance index threshold corresponding to the target performance index.

2. The system performance monitoring method according to claim 1, characterized in that, The step of analyzing historical index values ​​corresponding to the target performance index using time series data smoothing techniques to obtain the performance index threshold corresponding to the target performance index includes: Based on the timestamps carried by the historical indicator values, a preset number of historical indicator values ​​with timestamps within a preset time range are obtained from all historical indicator values ​​corresponding to the target performance indicator. Determine the average value of the preset number of historical indicator values; The performance index threshold corresponding to the target performance index is determined based on the average value.

3. The system performance monitoring method according to claim 2, characterized in that, The step of obtaining a preset number of historical indicator values ​​whose timestamps fall within a preset time range from all historical indicator values ​​corresponding to the target performance indicator based on the timestamps carried by the historical indicator values ​​includes: Based on the timestamp carried by the historical indicator value, obtain the historical indicator value whose timestamp is within the preset time range from all historical indicator values ​​corresponding to the target performance indicator after preprocessing. Among the historical indicator values ​​whose timestamps fall within the preset time range, obtain the latest preset number of historical indicator values.

4. The system performance monitoring method according to claim 1, characterized in that, The step of determining the business scenario mode corresponding to the target performance index value includes: The target performance metric, the target performance metric value, and the timestamp corresponding to the target performance metric value are input into a preset random forest model, and the business scenario mode corresponding to the target performance metric value is determined based on the output of the random forest model. The random forest model is obtained by pre-training an initial random forest model based on the historical index values ​​corresponding to the target performance index and the timestamps corresponding to each historical index value. The random forest model is used to identify business scenario patterns corresponding to different performance index values.

5. The system performance monitoring method according to claim 1, characterized in that, After determining the performance of the target system, the method further includes: If it is determined that the performance of the target system does not meet the preset requirements, an alarm message is generated based on the target performance index value and the performance index threshold corresponding to the target performance index value. Output the alarm information.

6. A system performance monitoring device, characterized in that, include: The first acquisition module is used to acquire the target performance index values ​​to be processed generated by the target system. The first determining module is used to determine the business scenario mode corresponding to the target performance index value, wherein the business scenario mode represents the business state presented by the target system during operation; The second determining module is used to determine the threshold type that matches the business scenario mode in a preset threshold management rule library based on the target performance index and the business scenario mode. The threshold management rule library includes threshold types corresponding to different performance indices under different business scenario modes, and the different performance indices include the target performance index. The second acquisition module is used to acquire the performance index threshold corresponding to the target performance index according to the threshold type. The third determining module is used to determine the performance of the target system based on the comparison result between the target performance index value and the performance index threshold. The threshold types include fixed thresholds and dynamic thresholds, and the second acquisition module includes: The first acquisition submodule is used to determine the performance index threshold corresponding to the target performance index from a plurality of pre-stored fixed thresholds if the threshold type is a fixed threshold. The second acquisition submodule is used to analyze the historical index values ​​corresponding to the target performance index through time series data smoothing technology if the threshold type is a dynamic threshold, so as to obtain the performance index threshold corresponding to the target performance index. The system performance monitoring device also includes: The third acquisition module is used to acquire the historical index values ​​corresponding to the target performance index and sort the historical index values ​​in ascending order. The fourth determining module is used to divide the historical index values ​​after ascending order into multiple different intervals, the multiple different intervals including a first interval, a second interval and a third interval, and to determine a first historical index value in the first interval, a second historical index value in the second interval, and a third historical index value in the third interval. The fifth determining module is used to determine a fixed threshold corresponding to the target performance indicator based on the first historical indicator value, the second historical indicator value, and the third historical indicator value.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements a system performance monitoring method as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a system performance monitoring method as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements a system performance monitoring method as described in any one of claims 1 to 5.

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