Running monitoring method and system for multiple application programs

Abstract

The invention provides an operation monitoring method and system for multiple application programs. The running monitoring method for the multiple application programs comprises the steps that the running state of each application program is monitored in real time, and a first running state evaluation parameter and a second running state evaluation parameter are obtained in combination with running state data of each application program; and performing operation monitoring on each application program by using a monitoring strategy in combination with the first operation state evaluation parameter and the second operation state evaluation parameter, and judging whether to perform abnormal operation early warning or not according to a monitoring result of each application program. The system comprises modules corresponding to the steps of the method.

Description

Technical Field

[0001] The invention provides a method and system for monitoring the operation of multiple applications, belonging to the technical field of application operation monitoring. Background Art

[0002] With the rapid development of information technology, various applications (Apps) play an increasingly important role in people's daily life and work. These applications cover multiple fields such as social, entertainment, office, and finance, greatly enriching user experience and improving work efficiency. However, with the rapid increase in the number of applications, how to ensure the stable operation of these applications and timely discover and solve potential operation problems has become a technical problem that needs to be solved urgently. Traditional application monitoring methods mainly rely on developers to manually set log recording points (i.e., "tracking points") and evaluate the operation status of applications by collecting and analyzing these log information. However, this method has many limitations: first, the manual setting of tracking points is labor-intensive and it is difficult to cover all possible operation scenarios; second, the accuracy and completeness of log information depends on the experience and meticulousness of developers, which is prone to omissions or misjudgments; finally, traditional monitoring methods can often only be analyzed after the fact, and real-time warnings cannot be achieved, resulting in delayed problem discovery and processing. In addition, with the widespread application of technologies such as cloud computing and big data, the concurrent operation of multiple applications has become the norm. There may be complex interactions between these applications, and the abnormal operation of any application may affect the stability and security of the entire system. Therefore, a more efficient, accurate and real-time monitoring method is needed for the operation monitoring of multiple applications. Summary of the invention

[0003] The present invention provides a method and system for monitoring the operation of multiple applications to solve the technical problems existing in the above-mentioned prior art. The technical solutions adopted are as follows:

[0004] A method for monitoring the operation of multiple applications, the method comprising:

[0005] Monitor the running status of each application in real time, and obtain a first running status evaluation parameter and a second running status evaluation parameter in combination with the running status data of each application;

[0006] The operation of each application is monitored by using the monitoring strategy in combination with the first operation status evaluation parameter and the second operation status evaluation parameter, and it is determined whether to issue an abnormal operation warning according to the monitoring result of each application.

[0007] Furthermore, the running state of each application is monitored in real time, and a first running state evaluation parameter and a second running state evaluation parameter are obtained in combination with the running state data of each application, including:

[0008] Monitor the running status of each application in real time, and obtain first running status data of each application; wherein the first running status data includes network request response time and number of running interruptions;

[0009] According to the first running state data of each application program, obtaining a first running state evaluation parameter corresponding to each application program;

[0010] Monitor the running status of each application in real time and obtain second running status data of each application; wherein the second running status data includes CPU utilization, memory occupancy, the number of request instructions processed per unit time, and the number of users accessing the application per unit time;

[0011] According to the second running state data of each application program, a second running state evaluation parameter corresponding to each application program is obtained.

[0012] Further, according to the first running state data of each application, obtaining a first running state evaluation parameter corresponding to each application includes:

[0013] Extracting first running status data of each application program;

[0014] Using the floating data of the first running status data, obtaining a floating coefficient corresponding to the first running status data;

[0015] The first running state evaluation parameter corresponding to each application is obtained by utilizing the floating coefficient in combination with the first running state data.

[0016] Further, according to the second running state data of each application, obtaining the second running state evaluation parameter corresponding to each application includes:

[0017] Extracting the second running status data of each application program;

[0018] constructing a state data vector using the second operating state data;

[0019] The state data vector is used to obtain a second running state evaluation parameter corresponding to each application program.

[0020] Further, the operation of each application is monitored by combining the first operation status evaluation parameter and the second operation status evaluation parameter using the monitoring strategy, and whether to issue an abnormal operation warning is determined according to the monitoring result of each application, including:

[0021] extracting the first operating state evaluation parameter and the second operating state evaluation parameter;

[0022] Performing operation monitoring on each application program using a monitoring strategy in combination with the first operation status evaluation parameter and the second operation status evaluation parameter;

[0023] When the first operation status evaluation parameter and the second operation status evaluation parameter of each application do not meet the operation requirements in the monitoring strategy, it is determined that the application has an operation abnormality and an abnormality alarm is issued;

[0024] The monitoring strategy is as follows:

[0025] Compare the first operation status evaluation parameter with a preset first operation status threshold, and when the first operation status evaluation parameter is not lower than the preset first operation status threshold, determine that the application has an operation abnormality, and issue an abnormality alarm;

[0026] Compare the second operation status evaluation parameter with a preset second operation status threshold value, and when the second operation status evaluation parameter is lower than the preset second operation status threshold value, determine that the application program has an operation abnormality, and issue an abnormality alarm;

[0027] When both the first operating state evaluation parameter and the second operating state evaluation parameter are not lower than their corresponding operating state thresholds, obtaining a comprehensive operating state evaluation parameter by using the first operating state evaluation parameter and the second operating state evaluation parameter;

[0028] Comparing the comprehensive operating status evaluation parameter with a preset comprehensive operating status threshold;

[0029] When the comprehensive operation status evaluation parameter is lower than a preset comprehensive operation status threshold, it is determined that the application program has an operation abnormality and an abnormality alarm is issued.

[0030] A multi-application operation monitoring system, the multi-application operation monitoring system comprising:

[0031] A real-time monitoring module, used to monitor the running status of each application in real time, and obtain a first running status evaluation parameter and a second running status evaluation parameter in combination with the running status data of each application;

[0032] The abnormal monitoring and alarm module is used to use the monitoring strategy in combination with the first operating status evaluation parameter and the second operating status evaluation parameter to monitor the operation of each application, and determine whether to issue an abnormal operation warning based on the monitoring result of each application.

[0033] Furthermore, the real-time monitoring module includes:

[0034] A first operation status data acquisition module, used to monitor the operation status of each application in real time and acquire first operation status data of each application; wherein the first operation status data includes network request response time and operation interruption times;

[0035] A first operation status evaluation parameter acquisition module, used to acquire a first operation status evaluation parameter corresponding to each application according to the first operation status data of each application;

[0036] A second operation status data acquisition module is used to monitor the operation status of each application in real time and acquire second operation status data of each application; wherein the second operation status data includes CPU utilization, memory occupancy, the number of request instructions processed per unit time, and the number of users accessing the application per unit time;

[0037] The second running state evaluation parameter acquisition module is used to acquire the second running state evaluation parameter corresponding to each application according to the second running state data of each application.

[0038] Furthermore, the first operating status evaluation parameter acquisition module includes:

[0039] A first running status data extraction module, used to extract the first running status data of each application;

[0040] A floating coefficient acquisition module, used to acquire a floating coefficient corresponding to the first operating state data by using the floating data of the first operating state data;

[0041] The first operation status evaluation parameter acquisition module is used to use the floating coefficient in combination with the first operation status data to acquire the first operation status evaluation parameter corresponding to each application program.

[0042] Furthermore, the second operating status evaluation parameter acquisition module includes:

[0043] A second running status data extraction module, used to extract the second running status data of each application;

[0044] A state data vector construction module, configured to construct a state data vector using the second operation state data;

[0045] The second running state evaluation parameter acquisition execution module is used to use the state data vector to acquire the second running state evaluation parameter corresponding to each application program.

[0046] Furthermore, the abnormal monitoring and alarm module includes:

[0047] An operation status data extraction module, used to extract the first operation status evaluation parameter and the second operation status evaluation parameter;

[0048] An operation monitoring execution module, used to perform operation monitoring on each application program by using a monitoring strategy in combination with the first operation status evaluation parameter and the second operation status evaluation parameter;

[0049] An abnormality alarm execution module, used for determining that an application has an operational abnormality and issuing an abnormality alarm when the first operational status evaluation parameter and the second operational status evaluation parameter of each application do not meet the operational requirements in the monitoring strategy;

[0050] The monitoring strategy is as follows:

[0051] Compare the first operation status evaluation parameter with a preset first operation status threshold, and when the first operation status evaluation parameter is not lower than the preset first operation status threshold, determine that the application has an operation abnormality, and issue an abnormality alarm;

[0052] Compare the second operation status evaluation parameter with a preset second operation status threshold value, and when the second operation status evaluation parameter is lower than the preset second operation status threshold value, determine that the application program has an operation abnormality, and issue an abnormality alarm;

[0053] When both the first operating state evaluation parameter and the second operating state evaluation parameter are not lower than their corresponding operating state thresholds, obtaining a comprehensive operating state evaluation parameter by using the first operating state evaluation parameter and the second operating state evaluation parameter;

[0054] Comparing the comprehensive operating status evaluation parameter with a preset comprehensive operating status threshold;

[0055] When the comprehensive operation status evaluation parameter is lower than a preset comprehensive operation status threshold, it is determined that the application program has an operation abnormality and an abnormality alarm is issued.

[0056] Beneficial effects of the present invention:

[0057] The method and system for monitoring the operation of multiple applications proposed in the present invention can detect application operation anomalies in a timely manner through real-time monitoring, thereby avoiding the accumulation of problems that lead to more serious consequences. Monitoring in combination with multiple operation status evaluation parameters can more comprehensively evaluate the operation status of the application and reduce the possibility of false alarms and missed alarms. By utilizing preset monitoring strategies and algorithm models, automated monitoring and abnormal warnings are achieved, which reduces the burden of manual monitoring and improves monitoring efficiency and accuracy. Monitoring strategies and warning rules can be adjusted and optimized according to actual needs to adapt to the characteristics of different applications and environments. By promptly detecting and resolving application operation anomalies, the stability and availability of the application are guaranteed, thereby improving user experience satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of the method of the present invention;

[0059] Figure 2 The system block diagram of the system of the present invention. DETAILED DESCRIPTION

[0060] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0061] The embodiment of the present invention provides a method for monitoring the operation of multiple applications. Figure 1 As shown, the operation monitoring method of multiple applications includes:

[0062] S1. Monitor the running status of each application in real time, and obtain a first running status evaluation parameter and a second running status evaluation parameter based on the running status data of each application;

[0063] S2. Perform operation monitoring on each application program by using the monitoring strategy in combination with the first operation status evaluation parameter and the second operation status evaluation parameter, and determine whether to issue an abnormal operation warning based on the monitoring result of each application program.

[0064] The working principle of the above technical solution is: through the monitoring probe deployed in the application environment or the monitoring code integrated into the application, the running status data of each application is collected in real time. These data may include various performance indicators such as CPU usage, memory usage, network request response time, number of database operations, etc. Based on the collected running status data, two key evaluation parameters are calculated using a preset algorithm or model - the first running status evaluation parameter and the second running status evaluation parameter. These two parameters are intended to evaluate the running status of the application from different dimensions, which may represent aspects such as performance stability and resource utilization efficiency.

[0065] According to the predefined monitoring strategy, the first operating status data and the second operating status data are used as inputs, and each application is comprehensively monitored through methods such as logical judgment, threshold comparison, and trend analysis. The monitoring strategy may include setting the sensitivity of data changes in different time periods, and identifying rules for abnormal values ​​of specific performance indicators. According to the monitoring results, if the operating status data of an application exceeds the preset normal range or meets a specific abnormal pattern, it is judged that the application has an abnormal operation risk. At this time, the system will decide whether to trigger the abnormal warning mechanism based on the preset rules.

[0066] The effect of the above technical solution is: through real-time monitoring, it is possible to promptly detect application operation anomalies and avoid the accumulation of problems leading to more serious consequences. Combining multiple operation status evaluation parameters for monitoring can more comprehensively evaluate the operation status of the application and reduce the possibility of false alarms and missed alarms. By using preset monitoring strategies and algorithm models, automatic monitoring and abnormal warnings are realized, which reduces the burden of manual monitoring and improves monitoring efficiency and accuracy. Monitoring strategies and warning rules can be adjusted and optimized according to actual needs to adapt to the characteristics of different applications and environments. By promptly discovering and resolving application operation anomalies, the stability and availability of the application are guaranteed, thereby improving user experience satisfaction.

[0067] In summary, the operation monitoring method of multiple applications effectively improves the operation stability and maintenance efficiency of applications through real-time monitoring, comprehensive evaluation and intelligent early warning.

[0068] In one embodiment of the present invention, the running state of each application is monitored in real time, and a first running state evaluation parameter and a second running state evaluation parameter are acquired in combination with the running state data of each application, including:

[0069] S101, monitor the running status of each application in real time, and obtain first running status data of each application; wherein the first running status data includes network request response time and number of running interruptions;

[0070] S102, acquiring a first running status evaluation parameter corresponding to each application according to the first running status data of each application;

[0071] S103, monitor the running status of each application in real time, and obtain second running status data of each application; wherein the second running status data includes CPU utilization, memory occupancy, the number of request instructions processed per unit time, and the number of users accessing the application per unit time;

[0072] S104: Acquire a second running status evaluation parameter corresponding to each application program according to the second running status data of each application program.

[0073] The working principle of the above technical solution is: through the monitoring agent deployed in the application environment or the monitoring code integrated into the application, the key performance indicators of each application are collected in real time. The focus is on the two indicators of network request response time and the number of operation interruptions. The network request response time reflects the efficiency of the application in processing user requests, while the number of operation interruptions is directly related to the stability and availability of the application.

[0074] Based on the acquired network request response time and the number of operation interruptions, the first operation status evaluation parameter is calculated using a preset algorithm or model (such as weighted average, threshold judgment, etc.). This parameter comprehensively reflects the performance of the application in terms of network response stability and operation continuity. The second operation status data acquisition includes CPU utilization, memory occupancy, the number of request instructions processed per unit time, and the number of users accessing the application per unit time. These data provide an important basis for evaluating the resource utilization efficiency and load bearing capacity of the application.

[0075] Based on resource usage indicators such as CPU utilization and memory occupancy, as well as load indicators such as the number of processed request instructions and the number of access users, the second operating status evaluation parameter is calculated through corresponding algorithms or models (such as resource utilization thresholds, load capacity evaluation models, etc.). This parameter comprehensively evaluates the resource utilization efficiency and load processing capacity of the application.

[0076] The effect of the above technical solution is: by real-time monitoring of two types of key operating status data (network response and resource utilization / load), comprehensive monitoring of the operating status of the application is achieved, which helps to discover potential problems in a timely manner. The operating status evaluation parameters calculated based on real-time data can accurately reflect the operating status of the application in different dimensions, providing strong support for subsequent abnormal warnings and performance optimization. The real-time monitoring mechanism ensures that the system can respond quickly to changes in the operating status of the application. Once an abnormality or potential problem is found, the early warning mechanism can be triggered immediately so that timely measures can be taken to intervene. By monitoring resource utilization efficiency and load conditions, it is helpful to discover and solve problems such as uneven resource allocation and excessive load, thereby optimizing resource utilization and improving the overall performance of the application. By ensuring the stable operation and efficient response of the application, the user experience and satisfaction are directly improved. At the same time, timely abnormal warnings and performance optimization also help reduce user complaints and negative feedback caused by application failures.

[0077] In one embodiment of the present invention, obtaining the first running state evaluation parameter corresponding to each application according to the first running state data of each application includes:

[0078] S1021. Extracting first running status data of each application; the first running status data includes network request response time and number of running interruptions;

[0079] S1022. Using the floating data of the first running status data, obtain a floating coefficient corresponding to the first running status data; wherein the floating coefficient corresponding to the first running status data is obtained by the following formula:

[0080]

[0081] Among them, F 01 represents the floating coefficient corresponding to the first running state data; n represents the number of unit time experienced by the application program running; T i represents the average network request response time corresponding to the i-th unit time; T c Z represents the theoretical average time of network request response corresponding to the i-th unit time; i represents the number of interruptions corresponding to the i-th unit time; Z c Indicates the preset reference value of the number of interruptions; T max and T min Indicates the maximum and minimum network request response time that occurs during the application's operation; T p Indicates the average network request response time that occurs during the application's operation; Z max and Z min Indicates the maximum and minimum network request response time per unit time during the application running; Z p Indicates the average network request response time per unit time during the application running;

[0082] S1023, using the floating coefficient combined with the first running state data to obtain a first running state evaluation parameter corresponding to each application program. The first running state evaluation parameter is obtained by the following formula:

[0083]

[0084] Among them, E 01 represents the first operating state evaluation parameter; F 01 Indicates the floating coefficient corresponding to the first operating state data; T max Indicates the maximum network request response time that occurs during the application's operation; T zIndicates the median network request response time experienced during the application operation; T p Indicates the average network request response time that occurs during the application's operation; Z max Indicates the maximum network request response time per unit time during the application running; Z p Indicates the average network request response time per unit time during the application running; Z z Indicates the median network request response time per unit time that the application runs.

[0085] The working principle of the above technical solution is: extract key data such as network request response time and number of operation interruptions of each application from the real-time monitoring system. These data are the basis for evaluating the network response stability and operation continuity of the application. Analyze the historical data of network request response time and number of operation interruptions to identify the floating part in the data. The floating part may be caused by various factors, such as network fluctuations, server load changes, etc. According to the characteristics of floating data and preset rules (such as statistical methods such as standard deviation and coefficient of variation), calculate the floating coefficient of the first operating status data of each application. The floating coefficient reflects the degree and law of data fluctuations and is an important basis for the subsequent calculation of evaluation parameters.

[0086] The floating coefficient is combined with the first operation status data such as the current network request response time and the number of operation interruptions, and the first operation status evaluation parameter of each application is calculated through a preset algorithm or model (such as weighted summation, exponential smoothing, etc.). This evaluation parameter not only takes into account the current actual operation status, but also incorporates the fluctuation characteristics of historical data, making the evaluation more comprehensive and accurate.

[0087] The effect of the above technical solution is: by introducing a floating coefficient, the evaluation parameters can dynamically reflect the fluctuation of the application's running status, making the evaluation results more in line with the actual situation. The evaluation parameters calculated by combining the floating coefficient and real-time data can more accurately reflect the network response stability and running continuity of the application compared to using real-time data or historical data alone. Because the volatility of the data is taken into account, when the running status of the application fluctuates abnormally, the evaluation parameters can more sensitively capture such changes, thereby issuing early warning signals, which helps to discover and solve problems in a timely manner. The evaluation parameters provide an intuitive and quantitative reference for operation and maintenance personnel, helping them to formulate operation and maintenance strategies and optimization plans more scientifically, and improve the operating efficiency and stability of the application.

[0088] On the other hand, by extracting the first running status data of the application, including the network request response time and the number of operation interruptions, the actual operation status of the application can be fully reflected. The first running status data is processed by using the floating coefficient to further refine the evaluation of the application running status and improve the accuracy and reliability of the evaluation. By defining a specific calculation formula, the running status data of the application is converted into specific evaluation parameters, and the quantitative evaluation of the running status is realized. This quantitative evaluation method enables the running status of different applications to be directly compared, which is convenient for identifying and optimizing applications with low operating efficiency. By analyzing the fluctuation of the network request response time and the number of operation interruptions, anomalies and potential faults in the operation of the application can be discovered in time. This helps to take preventive measures in advance to avoid serious failures of the application and improve the stability and reliability of the system. Through the accurate evaluation of the running status of the application, system resources can be allocated and managed more reasonably. Priority is given to ensuring the resource requirements of applications with good running status and greater impact on users, and improving the overall service quality and user experience.

[0089] In summary, this technical solution effectively improves the operating efficiency of applications and the stability of the system by accurately evaluating the operating status of applications, quantifying operating status evaluation, enhancing fault warning and diagnosis capabilities, and optimizing resource allocation and management.

[0090] In summary, this technical solution achieves a comprehensive and accurate assessment of the application network response stability and operation continuity by introducing a floating coefficient and combining real-time data to calculate the first operating status evaluation parameter, providing strong support for operation and maintenance decisions.

[0091] In one embodiment of the present invention, obtaining the second operating state evaluation parameter corresponding to each application according to the second operating state data of each application includes:

[0092] S1041, extracting the second running status data of each application;

[0093] S1042. Construct a state data vector using the second running state data; wherein the structure of the state data vector is as follows:

[0094]

[0095] Where S represents the state data vector; P c , P m , C r and C u They represent the data values ​​corresponding to the CPU utilization, memory usage, the number of request instructions processed per unit time, and the number of users accessing the application per unit time;

[0096] S1043: Use the state data vector to obtain a second operation state evaluation parameter corresponding to each application program. The second operation state evaluation parameter is obtained by the following formula:

[0097]

[0098] Among them, E 02 represents the second operating state evaluation parameter; S represents the state data vector; P c , P m , C r and C u They represent the data values ​​corresponding to the CPU utilization, memory occupancy, the number of request instructions processed per unit time, and the number of users accessing the application per unit time; x represents the first nonlinear adjustment parameter, with a value range of 0.5-5; y represents the second nonlinear adjustment parameter, with a value range of 0.1-3; z represents the exponential adjustment coefficient, with a value range of 0.1-10; P h Indicates the preset threshold of the difference between the CPU usage and the memory usage when the application runs well.

[0099] The working principle of the above technical solution is to extract key performance indicators such as CPU utilization, memory usage, number of request instructions processed per unit time, and number of users accessing the application per unit time for each application from the real-time monitoring system. These data directly reflect the resource usage and load status of the application.

[0100] The extracted second operating state data is converted into a vector form, namely a state data vector. Each vector element corresponds to a specific performance indicator value. The construction of the state data vector helps to integrate the multi-dimensional performance indicator data into a unified form, which is convenient for subsequent processing and analysis. In order to eliminate the influence of the dimensions and numerical ranges between different performance indicators, it may be necessary to standardize the state data vector. Standardization can ensure that each performance indicator has equal weight in the calculation of the evaluation parameters, and avoid some indicators with larger values ​​dominating the evaluation results.

[0101] Based on the constructed state data vector, the second operating state evaluation parameter of each application is calculated using a preset algorithm or model (such as weighted summation, principal component analysis, machine learning model, etc.). This evaluation parameter comprehensively reflects the performance of the application in terms of resource utilization efficiency and load processing capability. In order to more intuitively evaluate the operating state of the application, a series of thresholds can be preset, and the calculated evaluation parameters can be compared with these thresholds to determine whether the operating state of the application is normal, close to saturation, or overloaded.

[0102] The effect of the above technical solution is: by constructing a state data vector, the multi-dimensional performance indicator data is integrated into a unified form, which is convenient for subsequent processing and analysis. This helps to fully understand the operating status and resource utilization of the application. The evaluation parameters are calculated using a preset algorithm or model to avoid the subjectivity and uncertainty of manual judgment. The evaluation parameters can objectively and accurately reflect the performance of the application in terms of resource utilization efficiency and load processing capacity. By presetting the threshold and comparing it with the evaluation parameters, the changing trend of the application's operating status can be discovered in time. Once the evaluation parameter approaches or exceeds the preset threshold, the system can immediately issue an early warning signal to prompt the operation and maintenance personnel to take corresponding measures. The evaluation parameters provide an intuitive and quantitative reference for the operation and maintenance personnel, which helps them to formulate operation and maintenance strategies and optimization plans more scientifically. By continuously optimizing the resource allocation and load management of the application, the operating efficiency and stability of the application can be improved.

[0103] At the same time, by constructing a state data vector and using a specific formula to calculate the second running state evaluation parameter of each application, a quantitative evaluation of the running state of the application is achieved. This makes the evaluation of the running state more objective and accurate, and is convenient for subsequent analysis and processing. When calculating the second running state evaluation parameter, the first nonlinear adjustment parameter, the second nonlinear adjustment parameter and the exponential adjustment coefficient are introduced, which enables the evaluation parameters to more flexibly reflect the actual running state of the application and adapt to the evaluation requirements of different applications and different scenarios. The state data vector contains data in multiple dimensions such as CPU utilization, memory occupancy, the number of request instructions processed per unit time, and the number of users accessing the application per unit time, which enables the evaluation parameters to more comprehensively reflect the running state of the application and improve the accuracy and comprehensiveness of the evaluation. When calculating the evaluation parameters, the difference threshold between the CPU utilization and the memory occupancy when the application is in good running state is introduced, which enables the evaluation parameters to better reflect whether the application is in a good running state, providing strong support for subsequent monitoring and management.

[0104] In summary, this technical solution achieves a comprehensive, accurate and flexible evaluation of the application's operating status through quantitative evaluation, nonlinear adjustment, consideration of multi-dimensional factors and the application of preset thresholds, providing strong technical support for the monitoring and management of applications. At the same time, this technical solution achieves a comprehensive and objective evaluation of the application's resource utilization efficiency and load processing capacity by constructing a state data vector and calculating the second operating status evaluation parameter, providing strong support for operation and maintenance decisions.

[0105] In one embodiment of the present invention, the operation of each application is monitored by combining the first operation status evaluation parameter and the second operation status evaluation parameter using a monitoring strategy, and judging whether to issue an abnormal operation warning according to the monitoring result of each application, including:

[0106] S201, extracting the first operating state evaluation parameter and the second operating state evaluation parameter;

[0107] S202, using a monitoring strategy in combination with the first operating status evaluation parameter and the second operating status evaluation parameter to monitor the operation of each application;

[0108] S203, when the first operation status evaluation parameter and the second operation status evaluation parameter of each application do not meet the operation requirements in the monitoring strategy, it is determined that the application has an operation abnormality, and an abnormality alarm is issued;

[0109] The monitoring strategy is as follows:

[0110] Compare the first operation status evaluation parameter with a preset first operation status threshold, and when the first operation status evaluation parameter is not lower than the preset first operation status threshold, determine that the application has an operation abnormality, and issue an abnormality alarm;

[0111] Compare the second operation status evaluation parameter with a preset second operation status threshold value, and when the second operation status evaluation parameter is lower than the preset second operation status threshold value, determine that the application program has an operation abnormality, and issue an abnormality alarm;

[0112] When both the first operating state evaluation parameter and the second operating state evaluation parameter are not lower than their corresponding operating state thresholds, obtaining a comprehensive operating state evaluation parameter by using the first operating state evaluation parameter and the second operating state evaluation parameter;

[0113] The comprehensive operating status evaluation parameter is obtained by the following formula:

[0114]

[0115] Where E represents the comprehensive operating status evaluation parameter; E 01 represents the first operating state evaluation parameter; E 02 represents the second operating state evaluation parameter; e 01 and e 02 represents the weight values ​​corresponding to the first operating state evaluation parameter and the second operating state evaluation parameter;

[0116] Comparing the comprehensive operating status evaluation parameter with a preset comprehensive operating status threshold;

[0117] When the comprehensive operation status evaluation parameter is lower than a preset comprehensive operation status threshold, it is determined that the application program has an operation abnormality and an abnormality alarm is issued.

[0118] The working principle of the above technical solution is as follows: First, extract the first running status evaluation parameter and the second running status evaluation parameter of each application from the system. These two parameters are based on different monitoring dimensions and evaluation criteria, and comprehensively reflect the running status of the application.

[0119] Individual parameter monitoring: The extracted first operating status evaluation parameter is compared with the preset first operating status threshold. If it is lower than the threshold, it is determined that the application has an operating abnormality in this dimension and an abnormality alarm is triggered. Similarly, a similar operation is performed on the second operating status evaluation parameter.

[0120] Comprehensive parameter monitoring: If both of the two individual operating status evaluation parameters are not lower than their corresponding thresholds, the comprehensive operating status evaluation parameter is further calculated. This parameter combines the information of the two individual parameters to more comprehensively evaluate the operating status of the application. Subsequently, the comprehensive operating status evaluation parameter is compared with the preset comprehensive operating status threshold to determine whether the application has any overall operating abnormalities.

[0121] Abnormal determination and alarm: Based on the results of the monitoring strategy, if it is determined that the application has an operating abnormality, the abnormal alarm mechanism will be triggered so that relevant personnel can be notified in time for processing.

[0122] The effect of the above technical solution is: by combining the first running state evaluation parameter and the second running state evaluation parameter for monitoring, the running state of the application can be more comprehensively reflected, thereby improving the accuracy of monitoring. At the same time, the preset threshold makes the abnormality judgment have clear standards, reducing the possibility of misjudgment and missed judgment. The monitoring strategy not only takes into account the situation of individual parameters, but also evaluates the overall running state of the application by calculating the comprehensive running state evaluation parameters. This method makes the monitoring strategy more flexible and adaptable to different application scenarios and needs. Once it is determined that the application has an operating abnormality, the abnormal alarm mechanism is immediately triggered. This helps relevant personnel to respond and deal with problems in a timely manner, avoiding the expansion of problems and causing greater impact on the system. Through continuous monitoring of the running state of the application and abnormal warning, resources can be reasonably allocated according to actual conditions to optimize the overall performance and stability of the system. For example, when it is found that a certain application frequently has operating abnormalities, you can consider increasing the resource quota of the application or taking measures such as performance optimization.

[0123] In summary, this technical solution achieves comprehensive, accurate and timely monitoring of the application's operating status and abnormal warning by comprehensively considering multiple dimensions of operating status evaluation parameters and flexible monitoring strategies, which helps to improve the stability and availability of the system.

[0124] The embodiment of the present invention provides a multi-application operation monitoring system, such as Figure 2 As shown, the multi-application operation monitoring system includes:

[0125] A real-time monitoring module, used to monitor the running status of each application in real time, and obtain a first running status evaluation parameter and a second running status evaluation parameter in combination with the running status data of each application;

[0126] The abnormal monitoring and alarm module is used to use the monitoring strategy in combination with the first operating status evaluation parameter and the second operating status evaluation parameter to monitor the operation of each application, and determine whether to issue an abnormal operation warning based on the monitoring result of each application.

[0127] The working principle of the above technical solution is: through the monitoring probe deployed in the application environment or the monitoring code integrated into the application, the running status data of each application is collected in real time. These data may include various performance indicators such as CPU usage, memory usage, network request response time, number of database operations, etc. Based on the collected running status data, two key evaluation parameters are calculated using a preset algorithm or model - the first running status evaluation parameter and the second running status evaluation parameter. These two parameters are intended to evaluate the running status of the application from different dimensions, which may represent aspects such as performance stability and resource utilization efficiency.

[0128] According to the predefined monitoring strategy, the first operating status data and the second operating status data are used as inputs, and each application is comprehensively monitored through methods such as logical judgment, threshold comparison, and trend analysis. The monitoring strategy may include setting the sensitivity of data changes in different time periods, and identifying rules for abnormal values ​​of specific performance indicators. According to the monitoring results, if the operating status data of an application exceeds the preset normal range or meets a specific abnormal pattern, it is judged that the application has an abnormal operation risk. At this time, the system will decide whether to trigger the abnormal warning mechanism based on the preset rules.

[0129] The effect of the above technical solution is: through real-time monitoring, it is possible to promptly detect application operation anomalies and avoid the accumulation of problems leading to more serious consequences. Combining multiple operation status evaluation parameters for monitoring can more comprehensively evaluate the operation status of the application and reduce the possibility of false alarms and missed alarms. By using preset monitoring strategies and algorithm models, automatic monitoring and abnormal warnings are realized, which reduces the burden of manual monitoring and improves monitoring efficiency and accuracy. Monitoring strategies and warning rules can be adjusted and optimized according to actual needs to adapt to the characteristics of different applications and environments. By promptly discovering and resolving application operation anomalies, the stability and availability of the application are guaranteed, thereby improving user experience satisfaction.

[0130] In summary, the operation monitoring method of multiple applications effectively improves the operation stability and maintenance efficiency of applications through real-time monitoring, comprehensive evaluation and intelligent early warning.

[0131] In one embodiment of the present invention, the real-time monitoring module includes:

[0132] A first operation status data acquisition module, used to monitor the operation status of each application in real time and acquire first operation status data of each application; wherein the first operation status data includes network request response time and operation interruption times;

[0133] A first operation status evaluation parameter acquisition module, used to acquire a first operation status evaluation parameter corresponding to each application according to the first operation status data of each application;

[0134] A second operation status data acquisition module is used to monitor the operation status of each application in real time and acquire second operation status data of each application; wherein the second operation status data includes CPU utilization, memory occupancy, the number of request instructions processed per unit time, and the number of users accessing the application per unit time;

[0135] The second running state evaluation parameter acquisition module is used to acquire the second running state evaluation parameter corresponding to each application according to the second running state data of each application.

[0136] The working principle of the above technical solution is: through the monitoring agent deployed in the application environment or the monitoring code integrated into the application, the key performance indicators of each application are collected in real time. The focus is on the two indicators of network request response time and the number of operation interruptions. The network request response time reflects the efficiency of the application in processing user requests, while the number of operation interruptions is directly related to the stability and availability of the application.

[0137] Based on the acquired network request response time and the number of operation interruptions, the first operation status evaluation parameter is calculated using a preset algorithm or model (such as weighted average, threshold judgment, etc.). This parameter comprehensively reflects the performance of the application in terms of network response stability and operation continuity. The second operation status data acquisition includes CPU utilization, memory occupancy, the number of request instructions processed per unit time, and the number of users accessing the application per unit time. These data provide an important basis for evaluating the resource utilization efficiency and load bearing capacity of the application.

[0138] Based on resource usage indicators such as CPU utilization and memory occupancy, as well as load indicators such as the number of processed request instructions and the number of access users, the second operating status evaluation parameter is calculated through corresponding algorithms or models (such as resource utilization thresholds, load capacity evaluation models, etc.). This parameter comprehensively evaluates the resource utilization efficiency and load processing capacity of the application.

[0139] The effect of the above technical solution is: by real-time monitoring of two types of key operating status data (network response and resource utilization / load), comprehensive monitoring of the operating status of the application is achieved, which helps to discover potential problems in a timely manner. The operating status evaluation parameters calculated based on real-time data can accurately reflect the operating status of the application in different dimensions, providing strong support for subsequent abnormal warnings and performance optimization. The real-time monitoring mechanism ensures that the system can respond quickly to changes in the operating status of the application. Once an abnormality or potential problem is found, the early warning mechanism can be triggered immediately so that timely measures can be taken to intervene. By monitoring resource utilization efficiency and load conditions, it is helpful to discover and solve problems such as uneven resource allocation and excessive load, thereby optimizing resource utilization and improving the overall performance of the application. By ensuring the stable operation and efficient response of the application, the user experience and satisfaction are directly improved. At the same time, timely abnormal warnings and performance optimization also help reduce user complaints and negative feedback caused by application failures.

[0140] In one embodiment of the present invention, the first operating status evaluation parameter acquisition module includes:

[0141] A first running status data extraction module, used to extract the first running status data of each application;

[0142] A floating coefficient acquisition module, used to acquire a floating coefficient corresponding to the first operating state data by using the floating data of the first operating state data;

[0143] The first operation status evaluation parameter acquisition module is used to use the floating coefficient in combination with the first operation status data to acquire the first operation status evaluation parameter corresponding to each application program.

[0144] The working principle of the above technical solution is: extract key data such as network request response time and number of operation interruptions of each application from the real-time monitoring system. These data are the basis for evaluating the network response stability and operation continuity of the application. Analyze the historical data of network request response time and number of operation interruptions to identify the floating part in the data. The floating part may be caused by various factors, such as network fluctuations, server load changes, etc. According to the characteristics of floating data and preset rules (such as statistical methods such as standard deviation and coefficient of variation), calculate the floating coefficient of the first operating status data of each application. The floating coefficient reflects the degree and law of data fluctuations and is an important basis for the subsequent calculation of evaluation parameters.

[0145] The floating coefficient is combined with the first operation status data such as the current network request response time and the number of operation interruptions, and the first operation status evaluation parameter of each application is calculated through a preset algorithm or model (such as weighted summation, exponential smoothing, etc.). This evaluation parameter not only takes into account the current actual operation status, but also incorporates the fluctuation characteristics of historical data, making the evaluation more comprehensive and accurate.

[0146] The effect of the above technical solution is: by introducing a floating coefficient, the evaluation parameters can dynamically reflect the fluctuation of the application's running status, making the evaluation results more in line with the actual situation. The evaluation parameters calculated by combining the floating coefficient and real-time data can more accurately reflect the network response stability and running continuity of the application compared to using real-time data or historical data alone. Because the volatility of the data is taken into account, when the running status of the application fluctuates abnormally, the evaluation parameters can more sensitively capture such changes, thereby issuing early warning signals, which helps to discover and solve problems in a timely manner. The evaluation parameters provide an intuitive and quantitative reference for operation and maintenance personnel, helping them to formulate operation and maintenance strategies and optimization plans more scientifically, and improve the operating efficiency and stability of the application.

[0147] In summary, this technical solution achieves a comprehensive and accurate assessment of the application network response stability and operation continuity by introducing a floating coefficient and combining real-time data to calculate the first operating status evaluation parameter, providing strong support for operation and maintenance decisions.

[0148] In one embodiment of the present invention, the second operating status evaluation parameter acquisition module includes:

[0149] A second running status data extraction module, used to extract the second running status data of each application;

[0150] A state data vector construction module, configured to construct a state data vector using the second operation state data;

[0151] The second running state evaluation parameter acquisition execution module is used to use the state data vector to acquire the second running state evaluation parameter corresponding to each application program.

[0152] The working principle of the above technical solution is to extract key performance indicators such as CPU utilization, memory usage, number of request instructions processed per unit time, and number of users accessing the application per unit time for each application from the real-time monitoring system. These data directly reflect the resource usage and load status of the application.

[0153] The extracted second operating state data is converted into a vector form, namely a state data vector. Each vector element corresponds to a specific performance indicator value. The construction of the state data vector helps to integrate the multi-dimensional performance indicator data into a unified form, which is convenient for subsequent processing and analysis. In order to eliminate the influence of the dimensions and numerical ranges between different performance indicators, it may be necessary to standardize the state data vector. Standardization can ensure that each performance indicator has equal weight in the calculation of the evaluation parameters, and avoid some indicators with larger values ​​dominating the evaluation results.

[0154] Based on the constructed state data vector, the second operating state evaluation parameter of each application is calculated using a preset algorithm or model (such as weighted summation, principal component analysis, machine learning model, etc.). This evaluation parameter comprehensively reflects the performance of the application in terms of resource utilization efficiency and load processing capability. In order to more intuitively evaluate the operating state of the application, a series of thresholds can be preset, and the calculated evaluation parameters can be compared with these thresholds to determine whether the operating state of the application is normal, close to saturation, or overloaded.

[0155] The effect of the above technical solution is: by constructing a state data vector, the multi-dimensional performance indicator data is integrated into a unified form, which is convenient for subsequent processing and analysis. This helps to fully understand the operating status and resource utilization of the application. The evaluation parameters are calculated using a preset algorithm or model to avoid the subjectivity and uncertainty of manual judgment. The evaluation parameters can objectively and accurately reflect the performance of the application in terms of resource utilization efficiency and load processing capacity. By presetting the threshold and comparing it with the evaluation parameters, the changing trend of the application's operating status can be discovered in time. Once the evaluation parameter approaches or exceeds the preset threshold, the system can immediately issue an early warning signal to prompt the operation and maintenance personnel to take corresponding measures. The evaluation parameters provide an intuitive and quantitative reference for the operation and maintenance personnel, which helps them to formulate operation and maintenance strategies and optimization plans more scientifically. By continuously optimizing the resource allocation and load management of the application, the operating efficiency and stability of the application can be improved.

[0156] In summary, this technical solution achieves a comprehensive and objective evaluation of application resource utilization efficiency and load processing capacity by constructing a state data vector and calculating the second operating state evaluation parameter, providing strong support for operation and maintenance decisions.

[0157] In one embodiment of the present invention, the abnormality monitoring and alarm module includes:

[0158] An operation status data extraction module, used to extract the first operation status evaluation parameter and the second operation status evaluation parameter;

[0159] An operation monitoring execution module, used to perform operation monitoring on each application program by using a monitoring strategy in combination with the first operation status evaluation parameter and the second operation status evaluation parameter;

[0160] An abnormality alarm execution module, used for determining that an application has an operational abnormality and issuing an abnormality alarm when the first operational status evaluation parameter and the second operational status evaluation parameter of each application do not meet the operational requirements in the monitoring strategy;

[0161] The monitoring strategy is as follows:

[0162] Compare the first operation status evaluation parameter with a preset first operation status threshold, and when the first operation status evaluation parameter is not lower than the preset first operation status threshold, determine that the application has an operation abnormality, and issue an abnormality alarm;

[0163] Compare the second operation status evaluation parameter with a preset second operation status threshold value, and when the second operation status evaluation parameter is lower than the preset second operation status threshold value, determine that the application program has an operation abnormality, and issue an abnormality alarm;

[0164] When both the first operating state evaluation parameter and the second operating state evaluation parameter are not lower than their corresponding operating state thresholds, obtaining a comprehensive operating state evaluation parameter by using the first operating state evaluation parameter and the second operating state evaluation parameter;

[0165] Comparing the comprehensive operating status evaluation parameter with a preset comprehensive operating status threshold;

[0166] When the comprehensive operation status evaluation parameter is lower than a preset comprehensive operation status threshold, it is determined that the application program has an operation abnormality and an abnormality alarm is issued.

[0167] The working principle of the above technical solution is as follows: First, extract the first running status evaluation parameter and the second running status evaluation parameter of each application from the system. These two parameters are based on different monitoring dimensions and evaluation criteria, and comprehensively reflect the running status of the application.

[0168] Individual parameter monitoring: The extracted first operating status evaluation parameter is compared with the preset first operating status threshold. If it is lower than the threshold, it is determined that the application has an operating abnormality in this dimension and an abnormality alarm is triggered. Similarly, a similar operation is performed on the second operating status evaluation parameter.

[0169] Comprehensive parameter monitoring: If both of the two individual operating status evaluation parameters are not lower than their corresponding thresholds, the comprehensive operating status evaluation parameter is further calculated. This parameter combines the information of the two individual parameters to more comprehensively evaluate the operating status of the application. Subsequently, the comprehensive operating status evaluation parameter is compared with the preset comprehensive operating status threshold to determine whether the application has any overall operating abnormalities.

[0170] Abnormal determination and alarm: Based on the results of the monitoring strategy, if it is determined that the application has an operating abnormality, the abnormal alarm mechanism will be triggered so that relevant personnel can be notified in time for processing.

[0171] The effect of the above technical solution is: by combining the first running state evaluation parameter and the second running state evaluation parameter for monitoring, the running state of the application can be more comprehensively reflected, thereby improving the accuracy of monitoring. At the same time, the preset threshold makes the abnormality judgment have clear standards, reducing the possibility of misjudgment and missed judgment. The monitoring strategy not only takes into account the situation of individual parameters, but also evaluates the overall running state of the application by calculating the comprehensive running state evaluation parameters. This method makes the monitoring strategy more flexible and adaptable to different application scenarios and needs. Once it is determined that the application has an operating abnormality, the abnormal alarm mechanism is immediately triggered. This helps relevant personnel to respond and deal with problems in a timely manner, avoiding the expansion of problems and causing greater impact on the system. Through continuous monitoring of the running state of the application and abnormal warning, resources can be reasonably allocated according to actual conditions to optimize the overall performance and stability of the system. For example, when it is found that a certain application frequently has operating abnormalities, you can consider increasing the resource quota of the application or taking measures such as performance optimization.

[0172] In summary, this technical solution achieves comprehensive, accurate and timely monitoring of the application's operating status and abnormal warning by comprehensively considering multiple dimensions of operating status evaluation parameters and flexible monitoring strategies, which helps to improve the stability and availability of the system.

[0173] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for monitoring the operation of multiple applications, characterized in that: The operation monitoring method of multiple applications includes: Monitor the running status of each application in real time, and obtain a first running status evaluation parameter and a second running status evaluation parameter in combination with the running status data of each application; The operation of each application is monitored by using the monitoring strategy in combination with the first operation status evaluation parameter and the second operation status evaluation parameter, and it is determined whether to issue an abnormal operation warning according to the monitoring result of each application.

2. The method for monitoring the operation of multiple applications according to claim 1, characterized in that: Monitoring the running status of each application in real time, and acquiring a first running status evaluation parameter and a second running status evaluation parameter in combination with the running status data of each application, including: Monitor the running status of each application in real time, and obtain first running status data of each application; wherein the first running status data includes network request response time and number of running interruptions; According to the first running state data of each application program, obtaining a first running state evaluation parameter corresponding to each application program; Monitor the running status of each application in real time and obtain second running status data of each application; wherein the second running status data includes CPU utilization, memory occupancy, the number of request instructions processed per unit time, and the number of users accessing the application per unit time; According to the second running state data of each application program, a second running state evaluation parameter corresponding to each application program is obtained.

3. The method for monitoring the operation of multiple applications according to claim 2, characterized in that: Acquiring a first running status evaluation parameter corresponding to each application according to the first running status data of each application includes: Extracting first running status data of each application program; Using the floating data of the first running status data, obtaining a floating coefficient corresponding to the first running status data; The first running state evaluation parameter corresponding to each application is obtained by utilizing the floating coefficient in combination with the first running state data.

4. The method for monitoring the operation of multiple applications according to claim 2, characterized in that: Acquiring a second operating state evaluation parameter corresponding to each application according to the second operating state data of each application includes: Extracting the second running status data of each application program; constructing a state data vector using the second operating state data; The state data vector is used to obtain a second running state evaluation parameter corresponding to each application program.

5. The method for monitoring the operation of multiple applications according to claim 1, characterized in that: Using the monitoring strategy in combination with the first operating status evaluation parameter and the second operating 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, including: extracting the first operating state evaluation parameter and the second operating state evaluation parameter; Performing operation monitoring on each application program using a monitoring strategy in combination with the first operation status evaluation parameter and the second operation status evaluation parameter; When the first operation status evaluation parameter and the second operation status evaluation parameter of each application do not meet the operation requirements in the monitoring strategy, it is determined that the application has an operation abnormality and an abnormality alarm is issued; The monitoring strategy is as follows: Compare the first operation status evaluation parameter with a preset first operation status threshold, and when the first operation status evaluation parameter is not lower than the preset first operation status threshold, determine that the application has an operation abnormality, and issue an abnormality alarm; Compare the second operation status evaluation parameter with a preset second operation status threshold value, and when the second operation status evaluation parameter is lower than the preset second operation status threshold value, determine that the application program has an operation abnormality, and issue an abnormality alarm; When both the first operating state evaluation parameter and the second operating state evaluation parameter are not lower than their corresponding operating state thresholds, obtaining a comprehensive operating state evaluation parameter by using the first operating state evaluation parameter and the second operating state evaluation parameter; Comparing the comprehensive operating status evaluation parameter with a preset comprehensive operating status threshold; When the comprehensive operation status evaluation parameter is lower than a preset comprehensive operation status threshold, it is determined that the application program has an operation abnormality and an abnormality alarm is issued.

6. A multi-application operation monitoring system, characterized in that: The multi-application operation monitoring system includes: A real-time monitoring module, used to monitor the running status of each application in real time, and obtain a first running status evaluation parameter and a second running status evaluation parameter in combination with the running status data of each application; The abnormal monitoring and alarm module is used to use the monitoring strategy in combination with the first operating status evaluation parameter and the second operating status evaluation parameter to monitor the operation of each application, and determine whether to issue an abnormal operation warning based on the monitoring result of each application.

7. The multi-application operation monitoring system according to claim 6, characterized in that: The real-time monitoring module comprises: A first operation status data acquisition module, used to monitor the operation status of each application in real time and acquire first operation status data of each application; wherein the first operation status data includes network request response time and operation interruption number; A first operation status evaluation parameter acquisition module, used to acquire a first operation status evaluation parameter corresponding to each application according to the first operation status data of each application; A second operation status data acquisition module is used to monitor the operation status of each application in real time and acquire the second operation status data of each application; wherein the second operation status data includes CPU utilization, memory occupancy, the number of request instructions processed per unit time, and the number of users accessing the application per unit time; The second running state evaluation parameter acquisition module is used to acquire the second running state evaluation parameter corresponding to each application according to the second running state data of each application.

8. The multi-application operation monitoring system according to claim 7, characterized in that: The first operating status evaluation parameter acquisition module includes: A first running status data extraction module, used to extract the first running status data of each application; A floating coefficient acquisition module, used to acquire a floating coefficient corresponding to the first operating state data by using the floating data of the first operating state data; The first operation status evaluation parameter acquisition module is used to use the floating coefficient in combination with the first operation status data to acquire the first operation status evaluation parameter corresponding to each application program.

9. The multi-application operation monitoring system according to claim 7, characterized in that: The second operating status evaluation parameter acquisition module includes: A second running status data extraction module, used to extract the second running status data of each application; A state data vector construction module, configured to construct a state data vector using the second operation state data; The second running state evaluation parameter acquisition execution module is used to use the state data vector to acquire the second running state evaluation parameter corresponding to each application program.

10. The multi-application operation monitoring system according to claim 6, characterized in that: The abnormal monitoring alarm module includes: An operation status data extraction module, used to extract the first operation status evaluation parameter and the second operation status evaluation parameter; An operation monitoring execution module, used to perform operation monitoring on each application program by using a monitoring strategy in combination with the first operation status evaluation parameter and the second operation status evaluation parameter; An abnormality alarm execution module, used for determining that an application has an operational abnormality and issuing an abnormality alarm when the first operational status evaluation parameter and the second operational status evaluation parameter of each application do not meet the operational requirements in the monitoring strategy; The monitoring strategy is as follows: Compare the first operation status evaluation parameter with a preset first operation status threshold, and when the first operation status evaluation parameter is not lower than the preset first operation status threshold, determine that the application has an operation abnormality, and issue an abnormality alarm; Compare the second operation status evaluation parameter with a preset second operation status threshold value, and when the second operation status evaluation parameter is lower than the preset second operation status threshold value, determine that the application program has an operation abnormality, and issue an abnormality alarm; When both the first operating state evaluation parameter and the second operating state evaluation parameter are not lower than their corresponding operating state thresholds, obtaining a comprehensive operating state evaluation parameter by using the first operating state evaluation parameter and the second operating state evaluation parameter; Comparing the comprehensive operating status evaluation parameter with a preset comprehensive operating status threshold; When the comprehensive operation status evaluation parameter is lower than a preset comprehensive operation status threshold, it is determined that the application program has an operation abnormality and an abnormality alarm is issued.

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