CN117827616A - Method, device, equipment and storage medium for monitoring system performance data - Google Patents
Method, device, equipment and storage medium for monitoring system performance data Download PDFInfo
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Abstract
The application provides a method, a device, equipment and a storage medium for monitoring system performance data. The method comprises the following steps: analyzing a key index label carried by the data to be analyzed, and screening and obtaining a plurality of data to be analyzed belonging to the same key index label; respectively carrying out time sequence analysis, trend analysis, grouping analysis and anomaly detection analysis on a plurality of data to be analyzed belonging to the same key index to obtain a time sequence analysis result, a trend analysis result, a grouping analysis result and an anomaly detection analysis result; and according to the corresponding visual display rules, the time sequence analysis result, the trend analysis result, the grouping analysis result and the abnormality detection analysis result are visually presented. According to the method, the key index label is analyzed, so that the data is classified rapidly; through the multidimensional analysis method, the comprehensive understanding of the system performance data is realized, and the complex analysis result is displayed in an intuitive and easy-to-understand form, so that the system can be comprehensively analyzed.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for monitoring system performance data.
Background
With the rapid development of computer technology, computer systems have been widely used in various fields including scientific research, industrial production, commercial operations, and the like. In these fields, with the increasing amount of data and the increasing complexity of service content, ever-increasing demands are placed on the performance of computer systems, including processing speed, data throughput, response time, reliability, and the like. In terms of improving the performance of a system, hardware upgrades (e.g., increasing the number of cores of a CPU, improving memory capacity, etc.) or software optimizations (e.g., optimizing algorithms, improving programming techniques, etc.) are generally involved. However, whichever way to improve system performance is adopted, the current system performance is fully understood, and the discovery and resolution of performance problems is the basis for improving computer system performance.
At present, in the existing semi-automatic data acquisition method, although data information of system performance can be acquired relatively comprehensively, the analysis of data and the presentation mode of the data are usually single. For example, the data may simply be averaged or summed and then presented in the form of a text log or simple chart. Because the system performance is a multi-aspect and multi-level problem, it involves multiple levels of hardware, operating systems, applications, etc., and this single data analysis mode cannot meet the requirement for deep understanding of the system performance. At the same time, the simple data representation makes understanding the system performance costly in terms of time and effort. For example, an administrator may need to extract key information from a simple graph, integrate information based on the key information, understand system performance, and discover problems with system performance. Moreover, single data analysis and presentation modes often have difficulty in intuitively knowing the trend of variation and potential problems of system performance. When the system has performance problems, an administrator needs to manually analyze the reasons of the problems and take corresponding optimization measures according to analysis results. And if the administrator's understanding of the system is not deep enough, or the judgment of the problem is inaccurate, their optimization measures may not effectively solve the problem.
Based on this, the existing system performance monitoring cannot effectively reflect the system performance problem, and may also cause the system performance to be reduced, or even may cause a new problem or system crash, thereby causing a great trouble to the maintenance of the computer system.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for monitoring system performance data, which are used for solving the technical problem that the existing system performance monitoring is difficult to effectively reflect the system performance problem.
In a first aspect, the present application provides a method for monitoring system performance data, including:
acquiring data to be analyzed of system performance in real time, and analyzing and acquiring a key index label carried by the data to be analyzed so as to screen and acquire a plurality of data to be analyzed belonging to the same key index label;
based on a plurality of the key indexes, respectively performing time sequence analysis, trend analysis, grouping analysis and anomaly detection analysis on the plurality of data to be analyzed belonging to the same key index to obtain a time sequence analysis result, a trend analysis result, a grouping analysis result and an anomaly detection analysis result;
and respectively inquiring and acquiring visual display rules corresponding to the time sequence analysis result, the trend analysis result, the grouping analysis result and the abnormality detection analysis result, and visually presenting the time sequence analysis result, the trend analysis result, the grouping analysis result and the abnormality detection analysis result on a human-computer interaction interface according to the corresponding visual display rules so as to realize monitoring of the system performance data.
Optionally, the method as described above, the method further comprises:
based on a plurality of data to be analyzed of the system performance, carrying out data relationship analysis on a plurality of key indexes by adopting a preset data relationship analysis algorithm so as to obtain a data relationship analysis result among the key indexes;
inquiring to obtain a visual display rule corresponding to the data relation analysis result according to the data relation analysis result, wherein the visual display rule adopts a three-dimensional visual form;
and when the visual display rule adopts a three-dimensional visual form, visually presenting the data relationship analysis result on a man-machine interaction interface so as to realize monitoring of the system performance data.
Optionally, according to the method described above, the performing time series analysis, trend analysis, grouping analysis and anomaly detection analysis on the plurality of data to be analyzed belonging to the same key index based on a plurality of the key indexes to obtain a time series analysis result, a trend analysis result, a grouping analysis result and an anomaly detection analysis result respectively includes:
grouping the plurality of data to be analyzed belonging to the same key index according to a preset grouping standard to obtain a plurality of data groups to be analyzed;
And calculating the data to be analyzed belonging to the same key index in each data group to be analyzed by adopting a preset statistical mode, and obtaining a grouping analysis result.
Optionally, in the method as described above, the performing, based on a plurality of the criticality indexes, time-series analysis, trend analysis, packet analysis, and anomaly detection analysis on the plurality of data to be analyzed that belong to the same criticality index to obtain a time-series analysis result, a trend analysis result, a packet analysis result, and an anomaly detection analysis result, respectively, further includes:
and respectively adopting a preset autoregressive model and an isolated forest algorithm according to the plurality of data to be analyzed belonging to the same key index to acquire the time sequence analysis result and the abnormality detection analysis result.
Optionally, the method described above, the acquiring data to be analyzed of system performance includes:
acquiring original system performance data related to each key index according to a plurality of preset key indexes;
and carrying out data preprocessing for removing noise, removing abnormal values and normalizing the data on the original system performance data so as to obtain the data to be analyzed of the system performance.
Optionally, in the method as described above, the querying respectively obtains a visual display rule corresponding to the time series analysis result, the trend analysis result, the grouping analysis result and the anomaly detection analysis result, including:
inquiring to obtain a visual display rule corresponding to the time sequence analysis result, wherein the visual display rule adopts an interactive animation form;
inquiring to obtain a visual display rule corresponding to the trend analysis result, wherein the visual display rule adopts a line graph form;
inquiring to obtain a visual display rule corresponding to the grouping analysis result, wherein the visual display rule adopts a histogram form;
and inquiring to acquire a visual display rule corresponding to the abnormal detection analysis result, wherein the visual display rule adopts a basic chart form.
Optionally, the method described above, the key indicator includes a processor utilization, a memory utilization, a disk input output, a network bandwidth utilization, and a system load.
In a second aspect, the present application provides a system performance data monitoring apparatus, including:
the data screening module is used for acquiring data to be analyzed of system performance in real time, reading a key index tag carried by the data to be analyzed, and screening and acquiring a plurality of data to be analyzed belonging to the same key index;
The data analysis module is used for respectively carrying out time sequence analysis, trend analysis, grouping analysis and anomaly detection analysis on the plurality of data to be analyzed belonging to the same key index based on the plurality of key indexes so as to obtain a time sequence analysis result, a trend analysis result, a grouping analysis result and an anomaly detection analysis result;
and the analysis result display module is used for respectively inquiring and acquiring visual display rules corresponding to the time sequence analysis result, the trend analysis result, the grouping analysis result and the abnormality detection analysis result, and visually presenting the time sequence analysis result, the trend analysis result, the grouping analysis result and the abnormality detection analysis result on a human-computer interaction interface according to the corresponding visual display rules so as to realize visual monitoring of the system performance data.
In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes the computer-executable instructions stored in the memory to implement the monitoring method described above.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, are configured to implement the above-described monitoring method.
According to the method, the device, the equipment and the storage medium for monitoring the system performance data, the system performance data are acquired and analyzed in real time, and the system running condition is monitored in real time. And by adopting an efficient data acquisition and data screening processing algorithm, system performance data are captured in real time, and a large amount of data are classified and preprocessed rapidly by analyzing the index labels, so that a foundation is provided for further deep analysis. And then, through a multidimensional analysis method, comprehensive understanding of system performance data is realized, wherein the comprehensive understanding comprises dynamic trend, long-term trend of performance change, performance comparison among different groups and potential abnormal conditions, and detailed data support is provided for system optimization and problem solving. By presenting complex analysis results in an intuitive, easily understood form, efficient communication of data and fast decision support is achieved. Not only is the data easier to understand, but also the interactivity of the data analysis and the participation of the manager are improved, thereby supporting a rapid and effective monitoring and decision-making process.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a flow chart of a method for monitoring system performance data according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for obtaining and visualizing data relationship analysis results of key indicators according to an embodiment of the present disclosure;
FIG. 3 is a flow chart of a method for performing packet analysis according to one embodiment of the present application;
FIG. 4 is a flowchart of a method for obtaining data to be analyzed of system performance according to an embodiment of the present application;
FIG. 5 is a flowchart of a method for obtaining a visual display rule corresponding to an analysis result according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a monitoring device for system performance data according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
In the prior art, although the semi-automatic data acquisition method can acquire the data information of the system performance relatively comprehensively, certain limitations exist in the analysis and presentation modes of the data. These methods typically process the collected data by basic statistical means, such as calculating averages or summing, and then presenting the results in the form of text logs or simple charts. Such a single data analysis and presentation, while providing basic performance information, often fails to provide deep disclosure of multiple aspects and levels of system performance, particularly when multiple levels of hardware, operating systems, and applications are involved. For example, a system administrator may need to extract key information from a simple chart and then integrate the information to understand the overall performance of the system and potential problems when using these tools. This process is not only time and effort consuming, but also relies on the deep understanding and accurate judgment of the system by the administrator. If the administrator's understanding of the system is not deep enough, or the diagnosis of the problem is inaccurate, the optimization measures may be poorly effective. Furthermore, the prior art lacks in-depth analysis of data, e.g., is unable to effectively detect trends in performance and potential problems. The inability to provide sufficient insight for administrators to guide performance optimization and resource allocation not only increases the difficulty of solving the problem, but may delay the processing of the problem, thereby affecting the efficiency and stability of the overall system.
Based on the above technical problems in the prior art and the prior art, the inventive concept of the present application is as follows: how to improve the convenience of system maintenance. In the aspect of data acquisition, the traditional performance index is covered, various auxiliary data influencing the system performance are also included, and the comprehensiveness and the fineness of data collection are ensured. In order to mine and understand these data in depth, by means of analysis methods based on machine learning and big data techniques, it is possible to extract key insights from complex data, so that bottlenecks in system performance and predictive potential problems can be identified more accurately. In addition, in order to enable system administrators to more intuitively and easily understand and analyze these complex performance data, data analysis and understanding is also made more intuitive and easy to operate by visualization means such as dashboards, three-dimensional charts, and thermodynamic diagrams that are updated in real time. In this way, the administrator can quickly and accurately respond to the visualized content without having to go deep into each detail. Finally, an abnormality detection algorithm is integrated, the performance abnormality is recognized in time, and a response is made quickly. This mechanism ensures that the system administrator can take action at the beginning of the problem, effectively avoiding further exacerbations of the problem. In general, efficiency and accuracy of system performance monitoring are improved, and meanwhile dependence on the technical level of an administrator is reduced, so that stability and reliability of the whole system are improved.
The method and the device are suitable for scenes of environments with high requirements on system performance monitoring and management. For example, in the field of cloud computing and data centers, it can monitor large-scale servers and storage systems, helping administrators optimize resource allocation, ensuring stability of services. For enterprise IT infrastructure, the system and the method can monitor key network equipment and application programs, and assist IT departments in improving system efficiency. The manufacturing and industrial automation fields may also benefit from the present application, which enables monitoring of the automation equipment of the production line, optimizing the production process, reducing malfunctions and downtime. In addition, in smart city and internet of things applications, the present application is capable of managing various smart devices and systems, improving the efficiency of city infrastructure and public services. In general, the scheme of the invention meets the requirements of various industries on efficient and reliable system performance monitoring and management by providing comprehensive data acquisition, deep analysis, visual visualization and timely response mechanisms.
The application provides a system performance data monitoring method, which aims to solve the technical problems in the prior art.
The following describes the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for monitoring system performance data according to an embodiment of the present application, as shown in fig. 1, including steps S11-S13:
s11, acquiring data to be analyzed of system performance in real time, and analyzing and acquiring a key index label carried by the data to be analyzed so as to screen and acquire a plurality of data to be analyzed belonging to the same key index label.
In this embodiment, data to be analyzed of system performance is obtained in real time. The real-time acquisition of these data requires not only an efficient and reliable data acquisition mechanism, but also a system capable of handling high frequency and substantial amounts of data streams. In actual operation, agent software or monitoring tools would be deployed in the target system to collect performance data in a periodic or event-driven manner. The data is then sent to a data processing site for further analysis and processing. Once the data to be analyzed are collected, the next step is to analyze the key index tags carried in the data to be analyzed. The key index tag is an identification for identifying and classifying performance data, helping to understand the nature and importance of the data. For example, a data point for CPU utilization may carry a "CPU utilization" tag, while network traffic data may carry a "network bandwidth utilization" tag. The presence of these tags allows for automatic classification and screening of the data, thereby improving the efficiency and accuracy of the data processing.
Then, screening a plurality of data to be analyzed belonging to the same key index label. This process involves extracting an associated data set from a large amount of performance data. For example, if the load condition of the CPU is to be analyzed, all data points with a "CPU utilization" tag will be screened from the collected data. In this way, data from different sources and at different time points can be aggregated, providing a good basis for further subsequent analysis. In conclusion, the data to be analyzed of the system performance are obtained in real time, and the key index labels in the data are analyzed and screened, so that the basis for monitoring the system performance is formed. This involves efficient, accurate data collection, providing support for continuous optimization of system performance.
S12, based on a plurality of key indexes, respectively performing time sequence analysis, trend analysis, grouping analysis and anomaly detection analysis on a plurality of data to be analyzed belonging to the same key index to obtain a time sequence analysis result, a trend analysis result, a grouping analysis result and an anomaly detection analysis result.
In this embodiment, based on the data to be analyzed of the system performance corresponding to the plurality of key indexes, analysis is a key for ensuring the operation efficiency and stability of the system. For a plurality of data to be analyzed belonging to the same key index, time sequence analysis, trend analysis, grouping analysis and anomaly detection analysis are respectively carried out, so that a comprehensive view angle can be provided for understanding and optimizing the system performance. First, time series analysis focuses on analyzing the change of data over time, revealing the dynamic trend of performance indicators over time. Such analysis may reveal periodic patterns, long-term trends, and even predict future performance changes. For example, in time series analysis, high load periods and potential performance problems can be identified by analyzing CPU utilization over time. In addition, time series analysis helps understand the behavior patterns of the system after certain events (e.g., software deployment or configuration changes). Next, trend analysis further explores long-term trends in the data. This includes not only simple rising or falling trends, but also more complex trends such as seasonal fluctuations or traffic cycle variations. For example, by analyzing trends in memory usage, it is possible to predict when a system may be running out of memory resources, thereby taking steps in advance to avoid a system crash. Grouping analysis is to group data in different dimensions or categories and then analyze each group individually. Such analysis helps to compare performance differences between different groups, thereby identifying which particular conditions or configurations may result in performance degradation. For example, grouping data by application type can help identify which applications are most efficient in utilizing system resources. Finally, anomaly detection analysis aims at identifying abnormal patterns or abnormal data in the data, which may indicate that there is a potential problem with the system. By using various algorithms, such as statistical methods or machine learning models, outliers in the data can be effectively identified. For example, a sudden CPU utilization spike may indicate an overloaded process or unexpected system behavior.
Combining these analysis results can provide deep insight to system administrators, helping them make more intelligent decisions. The time series analysis results reveal the change of the performance index along with time, the trend analysis results show long-term performance trend, the grouping analysis results reveal the performance difference under different conditions, and the abnormality detection analysis results highlight the potential problems needing to be concerned. In summary, by performing these comprehensive analyses on multiple data to be analyzed for the same key indicator, system performance can be effectively monitored and managed. The method not only improves the accuracy of problem diagnosis, but also optimizes the resource allocation, and finally ensures the stable and efficient operation of the system.
S13, respectively inquiring and obtaining visual display rules corresponding to the time sequence analysis result, the trend analysis result, the grouping analysis result and the abnormality detection analysis result, and visually presenting the time sequence analysis result, the trend analysis result, the grouping analysis result and the abnormality detection analysis result on a man-machine interaction interface according to the corresponding visual display rules so as to realize monitoring of system performance data.
In this embodiment, the visual display rules corresponding to the time-series analysis result, the trend analysis result, the grouping analysis result and the anomaly detection analysis result are respectively queried and obtained, and accordingly comprehensive visual display is performed on the man-machine interaction interface, so that the analysis result is effectively visually presented. First, a corresponding visual display rule is queried for each type of analysis result. These rules define how complex data and analysis results are converted into visual formats that are intuitive and easy to understand. For the time sequence analysis result, the corresponding visual display rule is that the change of the performance index along with time can be clearly displayed. The visual display rule of the trend analysis result is that the long-term development direction and trend of the performance index are intuitively displayed. The visual display rule of the grouping analysis results is that performance comparisons under different groups or categories are effectively displayed. And for the abnormality detection analysis result, the visual display rule is to highlight an abnormal point or abnormal pattern in the data.
Once the appropriate visual display rules are determined, various analysis results will be presented on the human-machine interaction interface in accordance with these rules. This visual presentation not only makes complex data easier to understand, but also helps system administrators quickly identify and respond to performance problems. For example, visual presentation of time series analysis may allow administrators to see performance fluctuations at a glance, and visual trend analysis may help them identify long-term performance trends. Through visual presentation of the packet analysis, an administrator may compare performance differences between different applications or services. The visualization of the anomaly detection analysis can quickly attract the attention of an administrator and point to possible performance problems. In addition, such visual presentations also support interactive operations, such as zooming, dragging, and clicking, further enhancing the use experience. The administrator can search data deeply through interactive operation to acquire more details, so that more accurate decisions can be made. In conclusion, the analysis results of time sequence analysis, trend analysis, grouping analysis and anomaly detection are visually displayed in an intuitive and interactive mode, so that the effect of monitoring the system performance data is remarkably improved. The system not only facilitates the understanding of the performance data, but also improves the monitoring efficiency and accuracy, and provides powerful support for optimizing and maintaining the system performance.
By acquiring and analyzing the system performance data in real time, the method and the system realize the instant monitoring of the running condition of the system. And by adopting an efficient data acquisition and data screening processing algorithm, system performance data are captured in real time, and a large amount of data are classified and preprocessed rapidly by analyzing the index labels, so that a foundation is provided for further deep analysis. And then, through a multidimensional analysis method, comprehensive understanding of system performance data is realized, wherein the comprehensive understanding comprises dynamic trend, long-term trend of performance change, performance comparison among different groups and potential abnormal conditions, and detailed data support is provided for system optimization and problem solving. By presenting complex analysis results in an intuitive, easily understood form, efficient communication of data and fast decision support is achieved. Not only is the data easier to understand, but also the interactivity and participation of the data analysis are improved, thereby supporting a rapid and effective monitoring and decision-making process.
In a specific embodiment, an exemplary description is made herein of the key indicators mentioned in the above embodiment. The key indicators include processor utilization, memory usage, disk input and output, network bandwidth usage, and system load.
In this embodiment, the system's critical indicators are set in order to effectively monitor, analyze, and optimize the performance of the computer system. By means of these critical indicators, it is possible to quickly identify whether the system is operating properly and whether there are any upcoming problems or problems that have occurred. These criticality indicators can help quickly locate the root of a problem when the system is problematic. For example, if CPU utilization is abnormally high, it may indicate a performance bottleneck or improper allocation of resources. And by continually tracking the criticality index, an administrator can identify opportunities for performance improvement, such as optimizing resource allocation, upgrading hardware, or adjusting system configuration. Meanwhile, the key index can also be used for long-term system planning, including resource demand prediction and expansion strategies. For example, by observing memory usage trends, it can be predicted when additional memory needs to be added. On this basis, it is critical for the system providing the service to keep the criticality index within ideal limits to ensure a good experience. For example, ensuring short response times and high availability are important factors in improving satisfaction.
Specifically, the processor utilization is a key indicator for measuring the performance of the CPU. It shows the workload of the processor at a specific time, which can reflect the efficiency of the use of the system's processing power. High processor utilization may mean that the CPU is running a large number of tasks, which may be an indicator of efficient system operation, but a sustained high load may also be indicative of a performance bottleneck or overload condition. Memory usage provides important information about the use of system memory resources. Memory is one of the key resources in a computer, and excessive memory usage may cause the system to run slowly or even crash. Monitoring memory usage helps ensure that memory resources are reasonably allocated and used, especially in multi-tasking and resource-intensive application scenarios. Disk Input/Output (I/O) reflects how frequently a disk is active. High disk I/O may indicate that a large amount of data is being read from and written to disk, which may be due to data intensive operations or insufficient memory resulting in frequent data exchanges. Effectively monitoring disk I/O helps to identify storage performance issues and ensures data access efficiency. Network bandwidth utilization is a key indicator for measuring network performance, particularly in distributed systems and cloud computing environments. Monitoring network bandwidth utilization may reveal bottlenecks in data transmission, help optimize network configuration and reduce latency, thereby ensuring smooth operation of applications and services. Finally, the system load is a comprehensive indicator, typically representing the number of processes that are running and waiting to run in a certain period of time. It provides a quick overview of the overall performance state of the system. A high system load value may mean that too many processes compete for resources, possibly resulting in reduced performance. By continuously monitoring these critical indicators, a system administrator can discover and solve performance problems in time, optimizing resource allocation, and thereby improving overall performance and reliability of the system.
Fig. 2 is a flow chart of a method for obtaining and visualizing a data relationship analysis result of a key indicator according to an embodiment of the present application. On the basis of the above embodiment, as shown in fig. 2, steps S21 to S23 are included:
s21, based on the data to be analyzed, carrying out data relationship analysis on a plurality of key indexes by adopting a preset data relationship analysis algorithm so as to obtain a data relationship analysis result among the key indexes;
s22, inquiring to obtain a visual display rule corresponding to the data relation analysis result according to the data relation analysis result, wherein the visual display rule adopts a three-dimensional visual form;
s23, when the visual display rule is in a three-dimensional visual form, the data relationship analysis result is visually presented on a man-machine interaction interface so as to realize monitoring of system performance data.
In this embodiment, a preset data relationship analysis algorithm is utilized to explore the correlations and effects among multiple key indicators. Such as correlation analysis, causal relationship analysis, or complex network analysis methods, to identify relationships between different performance criticality indicators. For example, the correlation between CPU utilization and memory usage may be analyzed, or the correlation between network bandwidth consumption and application activity may be explored. Through such analysis, deep patterns and trends hidden under surface data can be revealed, providing a more comprehensive view of system performance. Based on these complex data relationship analysis results, corresponding visualization display rules, such as three-dimensional visualization rules, are then queried and obtained. This method of three-dimensional visualization is suitable for exposing complex data relationships and schemas because it is capable of exposing data in multiple dimensions, providing a manager with more rich and intuitive information. For example, a three-dimensional graph may display time in one dimension and different performance metrics in the other two dimensions, thereby allowing an administrator to intuitively see the changing relationships of the different metrics over time.
Finally, the data relation results obtained through analysis are converted into a three-dimensional visual form and displayed on a human-computer interaction interface. The visual presentation not only makes the complex data relationship easy to understand, but also improves the interaction experience of the administrator. An administrator may view data from different angles and levels in three-dimensional space, and may even explore different aspects of the data through interactive operations (e.g., rotation, scaling, etc.). The visual display mode greatly enhances the readability of the data and the participation of the administrator, so that the monitoring of the system performance becomes more efficient and visual. In general, from analysis of complex data relationships to visual three-dimensional visual display, not only are deep system performance insights provided, but also through the visualization technology, the insights are easier to understand and utilize by managers, so that real-time monitoring and management of system performance are effectively supported.
Illustratively, taking a data relationship showing key indexes of disk IO and memory utilization as an example, and the influence of the indexes on CPU utilization. Here is described how the data presentation is performed in a three-dimensional visualization. A scatter plot may be employed first: in a three-dimensional coordinate system, a scatter diagram shows disk IO and memory usage. The X-axis represents disk IO, the Y-axis represents memory usage, and the Z-axis may represent the point in time of the data. The different colored dots are used to distinguish samples of high disk IO and low disk IO, while the different shaped dots are used to distinguish samples of high memory usage and low memory usage. Such a representation enables an intuitive observation of the variation and distribution of different performance indicators in the time dimension.
Second, a three-dimensional histogram may be employed: disk IO, memory usage, and sample data quantity are represented in three-dimensional space using a three-dimensional histogram. The X axis, the Y axis and the Z axis correspond to the disk IO, the memory usage rate and the sample data quantity respectively. The columns of different colors or heights represent different disk IO and memory usage rates so that an administrator can clearly see the contrast between different indexes. Curved surface drawings may also be employed: the disk IO and memory usage are set to the X-axis and Y-axis, and the impact result index (e.g., CPU utilization) is set to the Z-axis. Different curved surfaces are used to represent high disk IO and low disk IO, high memory usage and low memory usage. The shape and the height change of the curved surface diagram intuitively show the interaction between different performance indexes. When a three-dimensional fan-shaped diagram is used: and a three-dimensional sector graph is used for showing the proportional relation between the IO and memory utilization rate of the magnetic disk. The angle and the color of the sector are set according to the usage rate of the disk IO and the memory, so that an administrator can clearly see the proportion difference between the performance indexes. By these three-dimensional visualization methods, complex system performance data is converted into visual representations that are intuitive and easy to understand, thereby effectively helping administrators identify performance trends, compare different performance metrics, and discover potential performance problems.
In one embodiment, fig. 3 is a flow chart of a method for performing packet analysis according to one embodiment of the present application. Is a specific limitation on one implementation of the packet analysis in step S12 described above. On the basis of the above embodiment, as shown in fig. 3, steps S31 to S32 are included:
s31, grouping a plurality of data to be analyzed which belong to the same key index according to a preset grouping standard to obtain a plurality of data groups to be analyzed;
s32, calculating the data to be analyzed belonging to the same key index in each data group to be analyzed by adopting a preset statistical mode, and obtaining a grouping analysis result.
In this embodiment, the collected data to be analyzed is grouped according to a preset grouping standard, so as to more precisely understand and analyze various aspects of the system performance. This grouping process is based on different characteristics and attributes of the key performance indicators, e.g., grouping can be based on time periods (e.g., peak hours and off-peak hours), different types of user behavior, or application types. For example, system performance data may be grouped by weekday versus weekend, traffic type, or different geographic location. This grouping approach enables data analysis to provide more targeted insight into specific scenarios or conditions, thereby helping administrators understand the performance of the system in different situations. And then, carrying out calculation analysis on the data in each group by adopting a preset statistical mode so as to obtain a group analysis result. This includes calculating statistics of average, median, standard deviation, maximum and minimum values for each packet. For example, as for the performance index of CPU utilization, average CPU utilization during weekday peak hours and off-peak hours can be calculated, thereby comparing performance differences in different time periods. Likewise, different types of applications or services may be grouped to analyze their use of system resources. These statistics not only help identify performance bottlenecks and optimization points, but also reveal potential performance improvement room. Through such grouping and calculation, an administrator can be provided with detailed and in-depth analysis reports on system performance. The administrator may make more appropriate decisions based on these analysis results, such as reallocation of resources, adjustment of priorities, or optimization of a particular application. This process not only improves the efficiency of system performance management, but also helps to ensure optimal utilization of system resources, thereby improving overall system performance.
In another embodiment, time series analysis and anomaly detection analysis are performed on a plurality of data to be analyzed belonging to the same key index, respectively, to obtain a time series analysis result and an anomaly detection analysis result. A specific explanation is given of one implementation of the time series analysis and the abnormality detection analysis in step S12 described above. On the basis of the above embodiment, step S121 is included:
s121, respectively adopting a preset autoregressive model and an isolated forest algorithm according to a plurality of data to be analyzed belonging to the same key index to acquire a time sequence analysis result and an abnormality detection analysis result.
In this embodiment, two analysis methods will be applied to a plurality of data to be analyzed that belong to the same key index: the autoregressive model is used for time sequence analysis, and the isolated forest algorithm is used for anomaly detection analysis. The combined use of these two approaches aims at in-depth analysis of system performance data from different angles to gain more comprehensive and accurate insight. First, for time series analysis, an autoregressive model is employed to process and analyze the data. An autoregressive model is a statistical model that assumes that current observations are associated with observations at several points in time. When the model is applied, the change rule of specific performance indexes such as CPU utilization rate, memory usage amount or network flow and the like along with time is analyzed. In this way, time dependence and dynamic trends of performance indicators can be revealed, for example, identifying patterns of increase or decrease in system load over a particular period of time. In addition, the autoregressive model can be used for predicting future performance trends, and provides references for capacity planning and performance optimization of the system.
Then, an isolated forest algorithm is used to perform anomaly detection analysis. An isolated forest is an algorithm based on a tree structure, and is particularly suitable for processing high-dimensional data. It isolates the observation points by randomly selecting features and randomly selecting segmentation values, and can effectively identify outliers or outliers in the data. In a scenario of system performance monitoring, an orphan forest algorithm may identify data points that do not conform to a normal performance pattern, such as a sudden performance drop or an abnormal peak in resource usage. Such anomaly detection is not only critical to timely discovery of system failures, but also helps to prevent potential system crashes or performance degradation. By applying the autoregressive model and the isolated forest algorithm to the performance data analysis, system performance can be comprehensively analyzed and monitored. The results of the time series analysis provide insight into the change in performance indicators over time, helping administrators understand and predict the behavior patterns of the system. The anomaly detection analysis reveals any potential performance problems in time, so that an administrator can quickly take measures to repair or optimize. By integrating the two methods, the efficiency and the accuracy of system performance monitoring are obviously improved, and the stable operation and the optimal management of the system are ensured. It should be further noted herein that the algorithms or models employed in the exemplary listing herein are not limiting of the present application with respect to time series analysis and anomaly detection analysis. The person skilled in the art can select an appropriate algorithm model according to specific system configuration, so as to achieve the purposes of time sequence analysis and anomaly detection analysis.
In one embodiment, fig. 4 is a flowchart of a method for acquiring data to be analyzed of system performance according to one embodiment of the present application. A specific description will be given of one implementation of the data acquisition in step S11. On the basis of the above embodiment, as shown in fig. 4, steps S41 to S42 are included:
s41, acquiring and acquiring original system performance data related to each key index according to a plurality of preset key indexes;
s42, carrying out data preprocessing of removing noise, removing abnormal values and normalizing data on the original system performance data so as to obtain data to be analyzed of the system performance.
In this embodiment, the data collection is performed for a plurality of preset critical indicators, such as CPU utilization, memory usage, network bandwidth usage, and disk IO. This step is the basis for system performance monitoring and aims to collect enough data to reflect the actual operating state of the system. The collection of these raw system performance data is typically automated, involving the monitoring of various aspects of the system in real time to ensure the integrity and accuracy of the data. For example, the system may collect CPU and memory usage in real time by deploying a monitoring agent on a server, or track network traffic and bandwidth usage through a network monitoring tool. Such real-time data acquisition provides a solid basis for subsequent analysis.
Then, necessary data preprocessing work is performed on the original system performance data to improve the accuracy and efficiency of data analysis. The main tasks of data preprocessing include removing noise from the data, filtering out outliers, and performing standardized processing of the data. Noise is removed to ensure accuracy of data analysis, as noisy data may interfere with the analysis results, leading to misinterpretation. Outliers are removed because they may result from abnormal system conditions or temporary system failures, which may distort the analysis results. The data normalization process is to convert data from different sources and different scales into a unified standard, such as normalizing all performance metrics to the same range or unit, which facilitates subsequent data comparison and analysis. For example, the amount of memory and disk usage may need to be converted to the same unit to facilitate cross-index comparative analysis. By these preprocessing steps, it is possible to ensure that the system performance data to be analyzed has higher quality and consistency. This not only improves the accuracy of the data analysis, but also provides a solid data basis for monitoring and optimizing the system performance. The preprocessed data will be more suitable for subsequent in-depth analysis, thereby helping to more efficiently understand and manage system performance.
Illustratively, raw system performance data that needs to be taken for a key indicator is illustrated herein. Regarding this key indicator of CPU utilization, raw system performance data that may be collected includes, but is not limited to, total CPU time (usage time and idle time), utilization of each core (usage of each core is collected for a multi-core processor), CPU time of processes and threads, and number of context switches (frequency of system switches between different processes or threads). Regarding memory usage criticality index, raw data collected includes, but is not limited to: total memory capacity (total amount of memory available in the system), amount of used memory (amount of memory currently used by the operating system and applications), amount of free memory (amount of memory not used in the system), and cache and buffer memory (amount of memory used by the operating system as cache and buffer). Regarding network bandwidth usage criticality index, the raw data collected includes, but is not limited to, upstream and downstream data traffic (amount of data sent and received), number of network connections (number of active network connections), network delay and packet loss rate (delay time of data transmission and rate of lost data packets), and bandwidth occupancy (usage of network bandwidth).
In one embodiment, fig. 5 is a flowchart of a method for obtaining a visual display rule corresponding to an analysis result by using a query according to one embodiment of the present application. Is a specific description of one implementation of the visual display rule in step S13. On the basis of the above embodiment, as shown in fig. 5, steps S51 to S54 are included:
s51, inquiring to acquire a visual display rule corresponding to the time sequence analysis result, wherein the visual display rule adopts an interactive animation form;
s52, inquiring to acquire a visual display rule corresponding to the trend analysis result, wherein the visual display rule adopts a line graph form;
s53, inquiring to acquire a visual display rule corresponding to the grouping analysis result, wherein the visual display rule is in a histogram form;
s54, query acquisition and visual display rules corresponding to the abnormal detection analysis result adopt a basic chart form.
In the present embodiment, in order to make the data analysis result more intuitive and easy to understand, the visual display rules corresponding to the time series analysis, trend analysis, grouping analysis, and abnormality detection analysis results will be respectively queried and acquired. The method ensures that different types of analysis results can be displayed in the most suitable mode, thereby improving the readability and interactive experience of the data. For time series analysis results, the visual display rule obtained by inquiring is in an interactive animation form. Interactive animation forms include, but are not limited to, real-time monitoring dashboards including animation effects and interactive elements, time and resource oriented thermodynamic diagrams, interactive scatter diagrams, and dynamic network topology diagrams. This form can dynamically show the change of performance index with time, so that the administrator can not only see the static state of the data, but also observe the change trend and mode thereof. For example, the change track of the CPU utilization or the memory usage can be vividly displayed in an animation form, and an administrator can observe performance data of a specific time point or time interval through interactive operations, such as dragging a time axis. The trend analysis results are set to be displayed in the form of a line graph. The line graph is a classical way of showing the trend of data, and can clearly reveal the rising or falling trend of data with time. By the form, long-term performance changes such as the utilization trend of network bandwidth or the load change trend of disk IO can be intuitively displayed. Or in a simple and clear chart form, with the aim of highlighting emphasis and contrast.
For the grouping analysis result, a histogram is selected as a visual display rule. The histogram can effectively compare the data differences of different groupings or categories. For example, a histogram may be used to compare usage of system resources by different applications or to compare system loading conditions for different periods of time. This form of graph allows an administrator to see the performance differences between different packets at a glance. The visual display rule of the abnormality detection analysis result is set to be in the form of a basic chart. This may include scatter plots, heat maps, etc., which can effectively highlight outlier data points, helping administrators quickly identify potential problems in the system. For example, a scatter plot may be used to display outliers in a system performance index, while a heat plot may be used to represent an overall distribution of system performance highlighting areas of performance anomalies. In addition to the above-mentioned visualization forms, it can be extended to other types of visualization presentations as desired. For example, a radar chart may be used to simultaneously demonstrate multiple performance metrics, thereby providing a comprehensive performance view; the box plot may be used to show the distribution of data and potential outliers. In addition, advanced visualization techniques, such as Virtual Reality (VR) or Augmented Reality (AR) techniques, may also be used to provide a more immersive and intuitive data analysis experience. These advanced visualization techniques not only improve the readability of the data, but also enhance the engagement and interaction experience of the administrator, thereby improving the effectiveness and efficiency of data analysis. On the basis, an administrator is allowed to customize the display modes of the charts and the indexes according to the requirements and the attention points. The administrator can choose to display the data in a specific time period, pay attention to specific indexes, change of abnormal values and the like so as to meet personalized requirements and handle abnormal conditions.
According to the method and the device, the system performance data are obtained in real time, the key index labels are analyzed, the system running state is monitored in real time and understood in depth, and a precise and comprehensive data basis is provided for subsequent detailed data analysis. Through multidimensional data analysis, comprehensive analysis of system performance is provided, including changes in performance over time (time series analysis), long-term development trends (trend analysis), performance comparison under different conditions (grouping analysis), and timely identification of potential problems (anomaly detection analysis). These analysis results help administrators understand system performance in depth and make efficient optimization decisions. By displaying the complex analysis results in an easily understood visual format, the readability and engagement of the data is improved. Different visualization forms provide intuitive data understanding and quick problem diagnosis capabilities for different analysis types. By monitoring key performance indicators, comprehensive information about resource utilization and system health can be provided. Such comprehensive monitoring helps ensure efficient and stable operation of the system and timely discovers and handles potential performance problems. In a comprehensive view, through comprehensive data collection, deep multidimensional analysis and visual display, the accuracy of monitoring data and the depth of analysis are improved, and the understanding and response capability of an administrator to the performance state of the system are enhanced.
Fig. 6 is a schematic diagram of a monitoring device for system performance data according to an embodiment of the present application. As shown in fig. 6, the apparatus 60 includes:
the data screening module 601 is configured to obtain data to be analyzed of system performance in real time, read a key index tag carried by the data to be analyzed, and screen and obtain a plurality of data to be analyzed belonging to the same key index;
the data analysis module 602 is configured to perform time sequence analysis, trend analysis, grouping analysis and anomaly detection analysis on a plurality of data to be analyzed belonging to the same key index based on a plurality of key indexes, so as to obtain a time sequence analysis result, a trend analysis result, a grouping analysis result and an anomaly detection analysis result;
the analysis result display module 603 is configured to query and obtain visual display rules corresponding to the time sequence analysis result, the trend analysis result, the grouping analysis result and the anomaly detection analysis result, and visually present the time sequence analysis result, the trend analysis result, the grouping analysis result and the anomaly detection analysis result on the human-computer interaction interface according to the corresponding visual display rules, so as to realize visual monitoring of system performance data.
The monitoring device provided in this embodiment may perform the above method of the above embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
In the foregoing detailed description, the modules may be implemented as a processor, which may execute computer-executable instructions stored in a memory, such that the processor performs the methods described above.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 7, the electronic device 70 includes: at least one processor 701 and a memory 702. The electronic device 70 further comprises communication means 703. Wherein the processor 701, the memory 702 and the communication means 703 are connected by a bus 704.
In a specific implementation, at least one processor 701 executes computer-executable instructions stored in a memory 702, such that the at least one processor 701 performs the above-described method performed on the electronic device side as described above.
The specific implementation process of the processor 701 can be referred to the above method embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
In the above embodiment, it should be understood that the processor may be a central processing unit (english: central Processing Unit, abbreviated as CPU), or may be other general purpose processors, digital signal processors (english: digital Signal Processor, abbreviated as DSP), application specific integrated circuits (english: application Specific Integrated Circuit, abbreviated as ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
The memory may comprise high speed RAM memory or may further comprise non-volatile storage NVM, such as at least one disk memory.
The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or one type of bus.
The scheme provided by the embodiment of the invention is introduced aiming at the functions realized by the electronic equipment and the main control equipment. It will be appreciated that the electronic device or the master device, in order to implement the above-described functions, includes corresponding hardware structures and/or software modules that perform the respective functions. The present embodiments can be implemented in hardware or a combination of hardware and computer software in combination with the various exemplary elements and algorithm steps described in connection with the embodiments disclosed in the embodiments of the present invention. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Those skilled in the art may implement the described functionality using different approaches for each particular application, but such implementation is not to be considered as beyond the scope of the embodiments of the present invention.
The present application also provides a computer readable storage medium having stored therein computer executable instructions that when executed by a processor implement a method as described above.
The computer readable storage medium described above may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. A readable storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.
An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). The processor and the readable storage medium may reside as discrete components in an electronic device or a master device.
The present application also provides a computer program product comprising: a computer program stored in a readable storage medium, from which at least one processor of an electronic device can read, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any one of the embodiments described above.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required in the present application.
It should be further noted that, although the steps in the flowchart are sequentially shown as indicated by arrows, the steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps in the flowcharts may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order in which the sub-steps or stages are performed is not necessarily sequential, and may be performed in turn or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
It should be understood that the above-described device embodiments are merely illustrative, and that the device of the present application may be implemented in other ways. For example, the division of the units/modules in the above embodiments is merely a logic function division, and there may be another division manner in actual implementation. For example, multiple units, modules, or components may be combined, or may be integrated into another system, or some features may be omitted or not performed.
In addition, each functional unit/module in each embodiment of the present application may be integrated into one unit/module, or each unit/module may exist alone physically, or two or more units/modules may be integrated together, unless otherwise specified. The integrated units/modules described above may be implemented either in hardware or in software program modules.
The integrated units/modules, if implemented in hardware, may be digital circuits, analog circuits, etc. Physical implementations of hardware structures include, but are not limited to, transistors, memristors, and the like. The processor may be any suitable hardware processor, such as CPU, GPU, FPGA, DSP and ASIC, etc., unless otherwise specified. Unless otherwise indicated, the storage elements may be any suitable magnetic or magneto-optical storage medium, such as resistive Random Access Memory RRAM (Resistive Random Access Memory), dynamic Random Access Memory DRAM (Dynamic Random Access Memory), static Random Access Memory SRAM (Static Random-Access Memory), enhanced dynamic Random Access Memory EDRAM (Enhanced Dynamic Random Access Memory), high-Bandwidth Memory HBM (High-Bandwidth Memory), hybrid Memory cube HMC (Hybrid Memory Cube), etc.
The integrated units/modules may be stored in a computer readable memory if implemented in the form of software program modules and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments. The technical features of the foregoing embodiments may be arbitrarily combined, and for brevity, all of the possible combinations of the technical features of the foregoing embodiments are not described, however, all of the combinations of the technical features should be considered as being within the scope of the disclosure.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method for monitoring system performance data, comprising:
acquiring data to be analyzed of system performance in real time, and analyzing and acquiring a key index label carried by the data to be analyzed so as to screen and acquire a plurality of data to be analyzed belonging to the same key index label;
based on a plurality of the key indexes, respectively performing time sequence analysis, trend analysis, grouping analysis and anomaly detection analysis on the plurality of data to be analyzed belonging to the same key index to obtain a time sequence analysis result, a trend analysis result, a grouping analysis result and an anomaly detection analysis result;
And respectively inquiring and acquiring visual display rules corresponding to the time sequence analysis result, the trend analysis result, the grouping analysis result and the abnormality detection analysis result, and visually presenting the time sequence analysis result, the trend analysis result, the grouping analysis result and the abnormality detection analysis result on a human-computer interaction interface according to the corresponding visual display rules so as to realize monitoring of the system performance data.
2. The method according to claim 1, wherein the method further comprises:
based on a plurality of data to be analyzed of the system performance, carrying out data relationship analysis on a plurality of key indexes by adopting a preset data relationship analysis algorithm so as to obtain a data relationship analysis result among the key indexes;
inquiring to obtain a visual display rule corresponding to the data relation analysis result according to the data relation analysis result, wherein the visual display rule adopts a three-dimensional visual form;
and when the visual display rule adopts a three-dimensional visual form, visually presenting the data relationship analysis result on a man-machine interaction interface so as to realize monitoring of the system performance data.
3. The method according to claim 1, wherein the performing time series analysis, trend analysis, grouping analysis, and anomaly detection analysis on the plurality of data to be analyzed belonging to the same key index based on the plurality of key indexes to obtain time series analysis results, trend analysis results, grouping analysis results, and anomaly detection analysis results, respectively, includes:
grouping the plurality of data to be analyzed belonging to the same key index according to a preset grouping standard to obtain a plurality of data groups to be analyzed;
and calculating the data to be analyzed belonging to the same key index in each data group to be analyzed by adopting a preset statistical mode, and obtaining a grouping analysis result.
4. The method according to claim 1, wherein the performing time series analysis, trend analysis, grouping analysis, and anomaly detection analysis on the plurality of data to be analyzed belonging to the same key index based on the plurality of key indexes to obtain time series analysis results, trend analysis results, grouping analysis results, and anomaly detection analysis results, respectively, further comprises:
and respectively adopting a preset autoregressive model and an isolated forest algorithm according to the plurality of data to be analyzed belonging to the same key index to acquire the time sequence analysis result and the abnormality detection analysis result.
5. The method of claim 1, wherein the acquiring data to be analyzed of system performance comprises:
acquiring original system performance data related to each key index according to a plurality of preset key indexes;
and carrying out data preprocessing for removing noise, removing abnormal values and normalizing the data on the original system performance data so as to obtain the data to be analyzed of the system performance.
6. The method of any one of claims 1-5, wherein the querying separately obtains a visual display rule corresponding to the time series analysis result, trend analysis result, grouping analysis result, and anomaly detection analysis result, comprising:
inquiring to obtain a visual display rule corresponding to the time sequence analysis result, wherein the visual display rule adopts an interactive animation form;
inquiring to obtain a visual display rule corresponding to the trend analysis result, wherein the visual display rule adopts a line graph form;
inquiring to obtain a visual display rule corresponding to the grouping analysis result, wherein the visual display rule adopts a histogram form;
and inquiring to acquire a visual display rule corresponding to the abnormal detection analysis result, wherein the visual display rule adopts a basic chart form.
7. The method of any of claims 1-5, wherein the criticality index comprises processor utilization, memory usage, disk input output, network bandwidth usage, and system load.
8. A system performance data monitoring apparatus, comprising:
the data screening module is used for acquiring data to be analyzed of system performance in real time, reading a key index tag carried by the data to be analyzed, and screening and acquiring a plurality of data to be analyzed belonging to the same key index;
the data analysis module is used for respectively carrying out time sequence analysis, trend analysis, grouping analysis and anomaly detection analysis on the plurality of data to be analyzed belonging to the same key index based on the plurality of key indexes so as to obtain a time sequence analysis result, a trend analysis result, a grouping analysis result and an anomaly detection analysis result;
and the analysis result display module is used for respectively inquiring and acquiring visual display rules corresponding to the time sequence analysis result, the trend analysis result, the grouping analysis result and the abnormality detection analysis result, and visually presenting the time sequence analysis result, the trend analysis result, the grouping analysis result and the abnormality detection analysis result on a human-computer interaction interface according to the corresponding visual display rules so as to realize visual monitoring of the system performance data.
9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 7.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1 to 7.
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