CN119201601A

CN119201601A - A method and system for monitoring operation of multiple applications

Info

Publication number: CN119201601A
Application number: CN202411209501.0A
Authority: CN
Inventors: 俞月赟
Original assignee: Shangrao Antang Network Technology Co ltd
Current assignee: Shangrao Antang Network Technology Co ltd
Priority date: 2024-08-30
Filing date: 2024-08-30
Publication date: 2024-12-27

Abstract

The present invention proposes a method and system for monitoring the operation of multiple applications. The method for monitoring the operation of multiple applications includes: monitoring the operation status of each application in real time, and obtaining a first operation status evaluation parameter and a second operation status evaluation parameter in combination with the operation status data of each application; using a monitoring strategy in combination with the first operation status evaluation parameter and the second operation status evaluation parameter to monitor the operation of each application, and judging whether to issue an abnormal operation warning according to the monitoring result of each application. The system includes modules corresponding to the steps of the method.

Description

Operation monitoring method and system for multiple application programs

Technical Field

The invention provides a multi-application operation monitoring method and system, and belongs to the technical field of application operation monitoring.

Background

With the rapid development of information technology, various application programs (Apps) play an increasingly important role in daily life and work of people. These applications cover multiple areas of social, entertainment, office, finance, etc., greatly enriching the user experience and improving the work efficiency. However, with the rapid increase of the number of applications, how to ensure the stable running of these applications, discover and solve the potential running problems in time becomes a technical problem to be solved. Traditional application monitoring methods rely primarily on developers manually setting up journaling points (i.e., "burial points") and evaluating the running state of the application by collecting and analyzing such journaling information. However, the method has the defects that firstly, the workload of manually setting buried points is large, all possible operation scenes are difficult to cover, secondly, the accuracy and the integrity of log information depend on experience and fineness of developers, omission or misjudgment easily occurs, and finally, the traditional monitoring method is often only capable of being analyzed afterwards, real-time early warning cannot be achieved, and problems are found and processing is delayed. In addition, with the wide application of technologies such as cloud computing and big data, concurrent running of multiple applications has become a normal state. There may be complex interactions between these applications, and any abnormal operation of any one application may affect the stability and security of the overall system. Therefore, for multi-application operation monitoring, a more efficient, accurate, real-time monitoring method is needed.

Disclosure of Invention

The invention provides a multi-application operation monitoring method and a system, which are used for solving the technical problems in the prior art, and the adopted technical scheme is as follows:

A multi-application operation monitoring method, the multi-application operation monitoring method comprising:

Monitoring the running state of each application program in real time, and acquiring a first running state evaluation parameter and a second running state evaluation parameter by combining the running state data of each application program;

and carrying out operation monitoring on each application program by combining the first operation state evaluation parameter and the second operation state evaluation parameter through a monitoring strategy, and judging whether to carry out abnormal operation early warning according to the monitoring result of each application program.

Further, monitoring the running state of each application program in real time, and acquiring a first running state evaluation parameter and a second running state evaluation parameter in combination with the running state data of each application program, including:

Monitoring the running state of each application program in real time, and acquiring first running state data of each application program, wherein the first running state data comprises network request response time and running interruption times;

Acquiring a first running state evaluation parameter corresponding to each application program according to the first running state data of each application program;

The method comprises the steps of monitoring the running state of each application program in real time, and obtaining second running state data of each application program, wherein the second running state data comprise CPU utilization rate, memory occupancy rate, the number of processing request instructions in unit time and the number of users accessing the application program in unit time;

And acquiring a second running state evaluation parameter corresponding to each application program according to the second running state data of each application program.

Further, according to the first running state data of each application program, a first running state evaluation parameter corresponding to each application program is obtained, including:

extracting first running state data of each application program;

acquiring a floating coefficient corresponding to the first running state data by utilizing the floating data of the first running state data;

And acquiring a first running state evaluation parameter corresponding to each application program by combining the floating coefficient with the first running state data.

Further, according to the second running state data of each application program, obtaining a second running state evaluation parameter corresponding to each application program includes:

extracting second running state data of each application program;

constructing a state data vector using the second operational state data;

And acquiring a second running state evaluation parameter corresponding to each application program by using the state data vector.

Further, the operation monitoring of each application program is performed by combining the first operation state evaluation parameter and the second operation state evaluation parameter by using a monitoring strategy, and whether the abnormal operation early warning is performed is judged according to the monitoring result of each application program, including:

extracting the first operation state evaluation parameter and the second operation state evaluation parameter;

Performing operation monitoring on each application program by combining the first operation state evaluation parameter and the second operation state evaluation parameter by using a monitoring strategy;

When the first operation state evaluation parameter and the second operation state evaluation parameter of each application program do not accord with the operation requirement in the monitoring strategy, judging that the application program has abnormal operation, and carrying out abnormal alarm;

wherein the monitoring strategy is as follows:

comparing the first operation state evaluation parameter with a preset first operation state threshold value, judging that the application program has abnormal operation when the first operation state evaluation parameter is not lower than the preset first operation state threshold value, and carrying out abnormal alarm;

Comparing the second operation state evaluation parameter with a preset second operation state threshold value, and judging that the application program has abnormal operation and carrying out abnormal alarm when the second operation state evaluation parameter is lower than the preset second operation state threshold value;

When the first operation state evaluation parameter and the second operation state evaluation parameter are not lower than the corresponding operation state threshold values, acquiring a comprehensive operation state evaluation parameter by using the first operation state evaluation parameter and the second operation state evaluation parameter;

comparing the comprehensive operation state evaluation parameter with a preset comprehensive operation state threshold value;

And when the comprehensive operation state evaluation parameter is lower than a preset comprehensive operation state threshold value, judging that the application program has abnormal operation, and carrying out abnormal alarm.

A multi-application operation monitoring system, the multi-application operation monitoring system comprising:

the real-time monitoring module is used for monitoring the running state of each application program in real time and acquiring a first running state evaluation parameter and a second running state evaluation parameter by combining the running state data of each application program;

And the abnormality monitoring alarm module is used for carrying out operation monitoring on each application program by utilizing a monitoring strategy and combining the first operation state evaluation parameter and the second operation state evaluation parameter, and judging whether to carry out operation abnormality early warning according to the monitoring result of each application program.

Further, the real-time monitoring module includes:

The system comprises a first running state data acquisition module, a second running state data acquisition module and a control module, wherein the first running state data acquisition module is used for monitoring the running state of each application program in real time and acquiring first running state data of each application program, and the first running state data comprises network request response time and running interruption times;

The first running state evaluation parameter acquisition module is used for acquiring first running state evaluation parameters corresponding to each application program according to the first running state data of each application program;

The system comprises a first running state data acquisition module, a second running state data processing module and a control module, wherein the first running state data acquisition module is used for monitoring the running state of each application program in real time and acquiring first running state data of each application program, and the first running state data comprises CPU utilization rate, memory occupancy rate, the number of processing request instructions in unit time and the number of users accessing the application program in unit time;

and the second running state evaluation parameter acquisition module is used for acquiring the second running state evaluation parameters corresponding to each application program according to the second running state data of each application program.

Further, the first operation state evaluation parameter acquisition module includes:

The first running state data extraction module is used for extracting the first running state data of each application program;

The floating coefficient acquisition module is used for acquiring a floating coefficient corresponding to the first running state data by utilizing the floating data of the first running state data;

and the first running state evaluation parameter acquisition module is used for acquiring the first running state evaluation parameters corresponding to each application program by combining the floating coefficient with the first running state data.

Further, the second operation state evaluation parameter acquisition module includes:

The second running state data extraction module is used for extracting the second running state data of each application program;

a state data vector construction module for constructing a state data vector using the second operational state data;

And the second running state evaluation parameter acquisition execution module is used for acquiring the second running state evaluation parameters corresponding to each application program by using the state data vector.

Further, the abnormality monitoring alarm module includes:

the running state data extraction module is used for extracting the first running state evaluation parameter and the second running state evaluation parameter;

The operation monitoring execution module is used for carrying out operation monitoring on each application program by combining the first operation state evaluation parameter and the second operation state evaluation parameter by utilizing a monitoring strategy;

the abnormal alarm execution module is used for judging that the application program has abnormal operation and carrying out abnormal alarm when the first operation state evaluation parameter and the second operation state evaluation parameter of each application program do not accord with the operation requirement in the monitoring strategy;

wherein the monitoring strategy is as follows:

The invention has the beneficial effects that:

The running monitoring method and system for the multiple application programs provided by the invention can timely find out running abnormality of the application programs through real-time monitoring, and avoid more serious consequences caused by problem accumulation. By combining a plurality of operation state evaluation parameters for monitoring, the operation state of the application program can be evaluated more comprehensively, and the possibility of false alarm and missing report is reduced. By using a preset monitoring strategy and algorithm model, automatic monitoring and abnormal early warning are realized, the burden of manual monitoring is reduced, and the monitoring efficiency and accuracy are improved. The monitoring strategy and the early warning rule can be adjusted and optimized according to actual requirements so as to adapt to the characteristics of different application programs and environments. By timely finding and solving the abnormal running of the application program, the stability and usability of the application program are guaranteed, and the experience satisfaction degree of the user is further improved.

Drawings

FIG. 1 is a flow chart of the method of the present invention;

Fig. 2 is a system block diagram of the system of the present invention.

Detailed Description

The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.

The embodiment of the invention provides a multi-application operation monitoring method, as shown in fig. 1, comprising the following steps:

S1, monitoring the running state of each application program in real time, and acquiring a first running state evaluation parameter and a second running state evaluation parameter by combining the running state data of each application program;

and S2, performing operation monitoring on each application program by combining the first operation state evaluation parameter and the second operation state evaluation parameter by using a monitoring strategy, and judging whether to perform abnormal operation early warning according to the monitoring result of each application program.

The technical scheme has the working principle that the running state data of each application program are collected in real time through a monitoring probe deployed in the application program environment or a monitoring code integrated into the application program. Such data may include various performance metrics such as CPU utilization, memory usage, network request response time, database operation times, etc. Based on the collected operation state data, two key evaluation parameters, namely a first operation state evaluation parameter and a second operation state evaluation parameter, are calculated by using a preset algorithm or model. These two parameters are intended to evaluate the running state of the application from different dimensions, possibly representing aspects of performance stability and resource utilization efficiency, respectively.

According to a predefined monitoring strategy, the first operation state data and the second operation state data are taken as input, and each application program is comprehensively monitored through methods such as logic judgment, threshold comparison, trend analysis and the like. The monitoring policy may include sensitivity settings for data changes over different time periods, identification rules for specific performance index outliers, etc. And according to the monitoring result, if the running state data of a certain application program exceeds a preset normal range or accords with a specific abnormal mode, judging that the application program has running abnormal risk. At this time, the system determines whether to trigger the abnormal early warning mechanism according to a preset rule.

The technical scheme has the advantages that through real-time monitoring, abnormal running of the application program can be found in time, and serious consequences caused by problem accumulation are avoided. By combining a plurality of operation state evaluation parameters for monitoring, the operation state of the application program can be evaluated more comprehensively, and the possibility of false alarm and missing report is reduced. By using a preset monitoring strategy and algorithm model, automatic monitoring and abnormal early warning are realized, the burden of manual monitoring is reduced, and the monitoring efficiency and accuracy are improved. The monitoring strategy and the early warning rule can be adjusted and optimized according to actual requirements so as to adapt to the characteristics of different application programs and environments. By timely finding and solving the abnormal running of the application program, the stability and usability of the application program are guaranteed, and the experience satisfaction degree of the user is further improved.

In summary, the operation monitoring method of the multi-application program effectively improves the operation stability and maintenance efficiency of the application program through means of real-time monitoring, comprehensive evaluation, intelligent early warning and the like.

In one embodiment of the present invention, monitoring an operation state of each application program in real time, and acquiring a first operation state evaluation parameter and a second operation state evaluation parameter in combination with operation state data of each application program includes:

S101, monitoring the running state of each application program in real time, and acquiring first running state data of each application program, wherein the first running state data comprises network request response time and running interruption times;

S102, acquiring first running state evaluation parameters corresponding to each application program according to the first running state data of each application program;

s103, monitoring the running state of each application program in real time, and acquiring second running state data of each application program, wherein the second running state data comprises CPU utilization rate, memory occupancy rate, the number of processing request instructions in unit time and the number of users accessing the application program in unit time;

S104, acquiring second running state evaluation parameters corresponding to each application program according to the second running state data of each application program.

The technical scheme has the working principle that key performance indexes of each application program are collected in real time through a monitoring agent deployed in an application program environment or a monitoring code integrated into the application program. The important points are two indexes of network request response time and operation interruption times. The network request response time reflects the efficiency of the application in handling user requests, while the number of interruptions in running is directly related to the stability and availability of the application.

And calculating a first operation state evaluation parameter by using a preset algorithm or model (such as weighted average, threshold judgment and the like) based on the acquired network request response time and operation interruption times. The parameter comprehensively reflects the performance of the application program in terms of network response stability and running continuity. The second operation state data acquisition comprises CPU utilization rate, memory occupancy rate, the number of processing request instructions in unit time and the number of users accessing the application program in unit time. These data provide an important basis for evaluating the resource usage efficiency and load bearing capacity of an application.

And calculating a second running state evaluation parameter through a corresponding algorithm or model (such as a resource utilization rate threshold value, a load capacity evaluation model and the like) based on the resource utilization indexes such as the CPU utilization rate and the memory occupancy rate, the load indexes such as the number of processing request instructions and the number of access users. The parameter comprehensively evaluates the resource utilization efficiency and the load processing capacity of the application program.

The technical scheme has the advantages that through monitoring two types of key operation state data (network response and resource utilization/load) in real time, the comprehensive monitoring of the operation state of the application program is realized, and the potential problem can be found timely. The running state evaluation parameters calculated based on the real-time data can accurately reflect the running states of the application program in different dimensions, and powerful support is provided for subsequent abnormal early warning and performance optimization. The real-time monitoring mechanism ensures that the system can respond to the change of the running state of the application program rapidly, and once an abnormality or a potential problem is found, the early warning mechanism can be triggered immediately so as to take measures to intervene in time. By monitoring the resource utilization efficiency and the load condition, the problems of uneven resource allocation, overhigh load and the like are found and solved, so that the resource utilization is optimized, and the overall performance of an application program is improved. By ensuring stable operation and efficient response of the application program, user experience and satisfaction are directly improved. Meanwhile, timely abnormal early warning and performance optimization are also beneficial to reducing user complaints and negative feedback caused by application program faults.

According to one embodiment of the present invention, according to the first running state data of each application program, obtaining a first running state evaluation parameter corresponding to each application program includes:

S1021, extracting first running state data of each application program, wherein the first running state data comprises network request response time and running interruption times;

S1022, obtaining a floating coefficient corresponding to the first running state data by using the floating data of the first running state data, wherein the floating coefficient corresponding to the first running state data is obtained through the following formula:

Wherein F ₀₁ represents a floating coefficient corresponding to the first running state data, n represents the number of unit time of running the application program, T _i represents an average value of network request response time corresponding to the ith unit time, T _c represents an average value of network request response theoretical time corresponding to the ith unit time, Z _i represents the number of interruption times corresponding to the ith unit time, Z _c represents a preset interruption time reference value, T _max and T _min represent maximum and minimum values of network request response time appearing in running the application program, T _p represents an average value of network request response time appearing in running the application program, Z _max and Z _min represent maximum and minimum values of network request response time appearing in unit time of running the application program, and Z _p represents an average value of network request response time appearing in unit time of running the application program;

S1023, acquiring first running state evaluation parameters corresponding to each application program by combining the floating coefficient with the first running state data. The first operation state evaluation parameter is obtained through the following formula:

Wherein E ₀₁ represents a first operating state evaluation parameter, F ₀₁ represents a floating coefficient corresponding to the first operating state data, T _max represents a network request response time maximum value occurring during an application program operation, T _z represents a network request response time intermediate value occurring during the application program operation, T _p represents a network request response time average value occurring during the application program operation, Z _max represents a network request response time maximum value occurring during a unit time during the application program operation, Z _p represents a network request response time average value occurring during a unit time during the application program operation, and Z _z represents a network request response time intermediate value occurring during a unit time during the application program operation.

The technical scheme has the working principle that key data such as network request response time and operation interruption times of each application program are extracted from a real-time monitoring system. These data are the basis for evaluating the stability of the application network response and the continuity of operation. Analyzing historical data of network request response time and operation interruption times, and identifying floating parts in the data. The floating portion may be caused by a variety of factors, such as network fluctuations, server load changes, etc. And calculating the floating coefficient of the first running state data of each application program according to the characteristics of the floating data and preset rules (such as standard deviation, variation coefficient and other statistical methods). The floating coefficient reflects the degree and rule of data fluctuation and is an important basis for the calculation of subsequent evaluation parameters.

And combining the floating coefficient with first operation state data such as the current network request response time, the operation interruption times and the like, and calculating a first operation state evaluation parameter of each application program through a preset algorithm or model (such as weighted summation, exponential smoothing and the like). The evaluation parameters not only consider the current actual running state, but also integrate the fluctuation characteristic of the historical data, so that the evaluation is more comprehensive and accurate.

The technical scheme has the advantages that the fluctuation condition of the running state of the application program can be dynamically reflected by the evaluation parameters through introducing the floating coefficient, so that the evaluation result is more in line with the actual condition. The network response stability and the operation continuity of the application program can be reflected more accurately by combining the floating coefficient and the evaluation parameter calculated by the real-time data compared with the single use of the real-time data or the historical data. Because the fluctuation characteristic of the data is considered, when the running state of the application program has abnormal fluctuation, the evaluation parameter can capture the change more sensitively, so that an early warning signal is sent in advance, and the problem can be found and solved in time. The evaluation parameters provide visual and quantitative reference bases for operation and maintenance personnel, are helpful for the operation and maintenance personnel to more scientifically formulate an operation and maintenance strategy and an optimization scheme, and improve the operation efficiency and stability of the application program.

On the other hand, by extracting the first running state data of the application program, including the network request response time and the running interruption times, the actual running condition of the application program can be comprehensively reflected. The floating coefficient is utilized to process the first running state data, so that the evaluation of the running state of the application program is further refined, and the accuracy and reliability of the evaluation are improved. By defining a specific calculation formula, the running state data of the application program is converted into specific evaluation parameters, so that the quantitative evaluation of the running state is realized. The quantitative evaluation method enables the running states of different application programs to be directly compared, and facilitates identification and optimization of the application programs with lower running efficiency. By analyzing the floating condition of the response time of the network request and the running interruption times, the abnormal and potential faults in the running of the application program can be timely discovered. This helps take precautions in advance, avoid serious failures of the application program, and improve the stability and reliability of the system. By accurate evaluation of the running state of the application, system resources can be allocated and managed more reasonably. The resource requirements of the application programs with good running state and great influence on the user are preferentially ensured, and the overall service quality and the user experience are improved.

In summary, the technical scheme effectively improves the running efficiency of the application program and the stability of the system by accurately evaluating the running state of the application program, quantifying the running state evaluation, enhancing the fault early warning and diagnosis capability and optimizing the resource allocation and management.

In summary, according to the technical scheme, the floating coefficient is introduced and the first running state evaluation parameter is calculated by combining the real-time data, so that comprehensive and accurate evaluation of the response stability and the running continuity of the application program network is realized, and powerful support is provided for operation and maintenance decision.

According to one embodiment of the present invention, according to the second running state data of each application program, obtaining a second running state evaluation parameter corresponding to each application program includes:

s1041, extracting second running state data of each application program;

s1042, constructing a state data vector by using the second operation state data, wherein the structure of the state data vector is as follows:

P _c、P_m、C_r and C _u respectively represent the CPU utilization rate, the memory occupancy rate, the number of processing request instructions in unit time and the data value corresponding to the number of users accessing the application program in unit time;

S1043, acquiring a second running state evaluation parameter corresponding to each application program by using the state data vector. The second operation state evaluation parameter is obtained through the following formula:

The method comprises the steps of E ₀₂, S, P _c、P_m、C_r and C _u, wherein the E ₀₂ is a second running state evaluation parameter, the S is a state data vector, the P _c、P_m、C_r and the C _u are respectively data values corresponding to the CPU utilization rate, the memory occupancy rate, the number of processing request instructions in unit time and the number of users accessing an application program in unit time, x is a first nonlinear adjustment parameter, the value range is 0.5-5;y is a second nonlinear adjustment parameter, the value range is 0.1-3;z is an index adjustment coefficient, the value range is 0.1-10, and P _h is a difference threshold value between the CPU utilization rate and the memory occupancy rate under the condition that the running state of the preset application program is good.

The technical scheme has the working principle that key performance indexes such as CPU utilization rate, memory occupancy rate, number of processing request instructions in unit time, number of users accessing the application programs in unit time and the like of each application program are extracted from the real-time monitoring system. These data directly reflect the resource usage and load conditions of the application.

The extracted second operational state data is converted into a vector form, i.e. a state data vector. Each vector element corresponds to a particular performance index value. The construction of the state data vector is helpful for integrating multidimensional performance index data into a unified form, and is convenient for subsequent processing and analysis. In order to eliminate the effects of dimension and numerical ranges between different performance metrics, it may be necessary to normalize the state data vectors. The normalization processing can ensure that the weight of each performance index in the evaluation parameter calculation is equal, and avoid that some indexes with larger numerical values lead the evaluation result.

Based on the constructed state data vector, a second operating state evaluation parameter of each application program is calculated by using a preset algorithm or model (such as weighted summation, principal component analysis, machine learning model and the like). The evaluation parameters comprehensively reflect the performance of the application program in terms of resource utilization efficiency and load processing capacity. In order to more intuitively evaluate the running state of the application, a series of thresholds may be preset, and the calculated evaluation parameters may be compared with these thresholds, so as to determine whether the running state of the application is normal, near saturation, or has been overloaded.

The technical scheme has the advantages that the multidimensional performance index data is integrated into a unified form by constructing the state data vector, so that subsequent processing and analysis are facilitated. This helps to fully understand the running state of the application and the resource utilization. And the evaluation parameters are calculated by using a preset algorithm or model, so that subjectivity and uncertainty of manual judgment are avoided. The evaluation parameters can objectively and accurately reflect the performance of the application program in terms of resource utilization efficiency and load processing capacity. Through presetting a threshold value and comparing with the evaluation parameters, the change trend of the running state of the application program can be found in time. Once the evaluation parameter approaches or exceeds a preset threshold, the system can immediately send out an early warning signal to prompt operation and maintenance personnel to take corresponding measures. The evaluation parameters provide visual and quantitative reference bases for operation and maintenance personnel, and are helpful for the operation and maintenance personnel to more scientifically formulate an operation and maintenance strategy and an optimization scheme. By continuously optimizing the resource configuration and load management of the application program, the running efficiency and stability of the application program can be improved.

Meanwhile, the quantitative evaluation of the running state of the application program is realized by constructing a state data vector and calculating a second running state evaluation parameter of each application program by utilizing a specific formula. The evaluation of the running state is more objective and accurate, and the subsequent analysis and processing are convenient. When the second running state evaluation parameter is calculated, the first nonlinear adjustment parameter, the second nonlinear adjustment parameter and the index adjustment coefficient are introduced, so that the evaluation parameter can reflect the actual running state of the application program more flexibly, and the evaluation requirements of different application programs and different scenes are met. The state data vector contains data of multiple dimensions such as CPU utilization rate, memory occupancy rate, the number of processing request instructions in unit time, the number of users accessing the application program in unit time and the like, so that the evaluation parameters can reflect the running state of the application program more comprehensively, and the accuracy and the comprehensiveness of evaluation are improved. When the evaluation parameters are calculated, a difference threshold value between the CPU utilization rate and the memory occupancy rate under the condition that the running state of the preset application program is good is introduced, so that the evaluation parameters can better reflect whether the application program is in the good running state, and powerful support is provided for subsequent monitoring and management.

In summary, the technical scheme realizes comprehensive, accurate and flexible evaluation of the running state of the application program by means of quantitative evaluation, nonlinear adjustment, application considering multidimensional factors and preset thresholds and the like, and provides powerful technical support for monitoring and management of the application program. Meanwhile, the technical scheme realizes comprehensive and objective evaluation of the utilization efficiency of the application program resources and the load processing capacity by constructing the state data vector and calculating the second running state evaluation parameter, and provides powerful support for operation and maintenance decision.

In one embodiment of the present invention, the monitoring strategy is used to combine the first operation state evaluation parameter and the second operation state evaluation parameter to perform operation monitoring on each application program, and whether to perform abnormal operation early warning is determined according to the monitoring result of each application program, including:

S201, extracting the first operation state evaluation parameter and the second operation state evaluation parameter;

S202, performing operation monitoring on each application program by combining the first operation state evaluation parameter and the second operation state evaluation parameter by using a monitoring strategy;

S203, when the first operation state evaluation parameter and the second operation state evaluation parameter of each application program do not accord with the operation requirement in the monitoring strategy, judging that the application program has abnormal operation, and carrying out abnormal alarm;

wherein the monitoring strategy is as follows:

wherein, the comprehensive operation state evaluation parameter is obtained by the following formula:

Wherein E represents a comprehensive operation state evaluation parameter, E ₀₁ represents a first operation state evaluation parameter, E ₀₂ represents a second operation state evaluation parameter, and E ₀₁ and E ₀₂ represent weight values corresponding to the first operation state evaluation parameter and the second operation state evaluation parameter;

The working principle of the technical scheme is that first, the first running state evaluation parameter and the second running state evaluation parameter of each application program are extracted from the system. The two parameters are respectively based on different monitoring dimensions and evaluation standards, and comprehensively reflect the running state of the application program.

And (3) monitoring independent parameters, namely comparing the extracted first operation state evaluation parameter with a preset first operation state threshold value, if the first operation state evaluation parameter is lower than the threshold value, judging that the application program has operation abnormality in the dimension, and triggering an abnormality alarm. Likewise, a similar operation is performed for the second operation state evaluation parameter.

And (3) comprehensive parameter monitoring, namely if the two independent operation state evaluation parameters are not lower than the corresponding threshold values, further calculating the comprehensive operation state evaluation parameters. The parameter integrates information of two separate parameters to more fully evaluate the running state of the application. And then, comparing the comprehensive operation state evaluation parameter with a preset comprehensive operation state threshold value to judge whether the overall operation abnormality exists in the application program.

And (3) abnormality judgment and alarm, namely triggering an abnormality alarm mechanism according to the result of the monitoring strategy if the application program is judged to have abnormal operation, so as to timely inform related personnel to process.

The technical scheme has the advantages that the operation state of the application program can be reflected more comprehensively by combining the first operation state evaluation parameter and the second operation state evaluation parameter for monitoring, so that the accuracy of monitoring is improved. Meanwhile, the preset threshold value enables the abnormal judgment to have a clear standard, and the possibility of misjudgment and missed judgment is reduced. The monitoring strategy not only considers the situation of the individual parameters, but also evaluates the overall running state of the application program by calculating the comprehensive running state evaluation parameters. This approach makes the monitoring strategy more flexible and adaptable to different application scenarios and requirements. An exception alert mechanism is triggered immediately upon determining that an application has an operational exception. This helps the relevant personnel respond to and handle the problem in time, avoids problem expansion to cause bigger influence on the system. Through continuous monitoring and abnormal early warning of the running state of the application program, resources can be reasonably allocated according to actual conditions, and the overall performance and stability of the system are optimized. For example, when an application program is found to frequently run abnormally, measures such as increasing the resource quota of the application program or performing performance optimization can be considered.

In summary, the technical scheme realizes comprehensive, accurate and timely monitoring and abnormal early warning of the running state of the application program by comprehensively considering the running state evaluation parameters of multiple dimensions and a flexible monitoring strategy, and is beneficial to improving the stability and usability of the system.

The embodiment of the invention provides a multi-application operation monitoring system, as shown in fig. 2, comprising:

In one embodiment of the present invention, the real-time monitoring module includes:

In one embodiment of the present invention, the first operation state evaluation parameter acquisition module includes:

In one embodiment of the present invention, the second operation state evaluation parameter acquisition module includes:

In summary, the technical scheme realizes comprehensive and objective evaluation of the utilization efficiency of the application program resources and the load processing capacity by constructing the state data vector and calculating the second operation state evaluation parameter, and provides powerful support for operation and maintenance decision.

In one embodiment of the present invention, the abnormality monitoring alarm module includes:

wherein the monitoring strategy is as follows:

It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. A multi-application operation monitoring method, characterized in that the multi-application operation monitoring method comprises:

2. The method for monitoring the operation of multiple applications according to claim 1, wherein monitoring the operation state of each application in real time and acquiring the first operation state evaluation parameter and the second operation state evaluation parameter in combination with the operation state data of each application comprises:

3. The method for monitoring operation of multiple applications according to claim 2, wherein obtaining the first operation state evaluation parameter corresponding to each application according to the first operation state data of each application comprises:

extracting first running state data of each application program;

4. The method for monitoring operation of multiple applications according to claim 2, wherein obtaining the second operation state evaluation parameter corresponding to each application according to the second operation state data of each application comprises:

extracting second running state data of each application program;

constructing a state data vector using the second operational state data;

5. The method for monitoring operation of multiple applications according to claim 1, wherein the operation of each application is monitored by using a monitoring policy in combination with the first operation state evaluation parameter and the second operation state evaluation parameter, and whether to perform abnormal operation early warning is determined according to the monitoring result of each application, comprising:

wherein the monitoring strategy is as follows:

6. A multi-application operation monitoring system, the multi-application operation monitoring system comprising:

7. The multi-application operation monitoring system of claim 6, wherein the real-time monitoring module comprises:

8. The multi-application operation monitoring system according to claim 7, wherein the first operation state evaluation parameter acquisition module includes:

9. The multi-application operation monitoring system according to claim 7, wherein the second operation state evaluation parameter acquisition module includes:

10. The multi-application operation monitoring system according to claim 6, wherein the abnormality monitoring alarm module comprises:

wherein the monitoring strategy is as follows: