Web application service system operation quality monitoring method, system, device and medium

Abstract

The invention discloses a method, a system, equipment and a medium for monitoring the operation quality of a Web application service system, belongs to the technical field of computers, and aims to solve the technical problems of how to realize comprehensive, real-time and accurate monitoring on the operation quality of the service system, timely discover potential problems and provide an effective solution. According to the technical scheme, the method comprises the following steps: data acquisition: acquiring multi-source data from a server, the service system and a user side; data storage: storing and managing the collected server performance data, business transaction data and user behavior data for subsequent processing and analysis; data preprocessing: carrying out preprocessing operations of cleaning, converting and integrating on the collected original data to obtain preprocessed data; data analysis: carrying out deep analysis of performance indexes, business transactions and user behaviors on the preprocessed data; performing early warning processing; and performing visual display.

Description

Technical Field

[0001] This invention relates to the field of computer technology, specifically to a method, system, device, and medium for monitoring the operational quality of a Web application business system. Background Technology

[0002] With the rapid development of internet technology, web applications, with their advantages of cross-platform compatibility, ease of access, and relatively low development costs, have become the core carrier for enterprises to conduct business. From product transactions on large e-commerce platforms to course learning in online education institutions, and online financial services in financial institutions, almost all industries rely heavily on web applications to realize user interaction and business processing. However, the complexity and openness of web applications also bring many severe challenges to their operation, which directly affect the operational quality of business systems.

[0003] (1) Monitoring challenges arising from the complexity of Web application architecture: Modern Web applications typically employ a multi-layered architecture, including a front-end presentation layer, a business logic layer, and a data access layer, with each layer interacting through complex interfaces. Furthermore, to improve system performance and availability, technologies such as caching, load balancing, and distributed storage are introduced. This complex architecture results in intricate dependencies between system components; a minor fault can trigger a chain reaction at multiple levels, leading to various anomalies in the business system. For example, improper database connection pool configuration can cause performance degradation in the data access layer, which in turn affects the processing speed of the business logic layer, ultimately resulting in slow front-end page response and a poor user experience. Traditional monitoring methods often only monitor single components or specific layers, making it difficult to comprehensively grasp the operational status of the entire Web application architecture and to promptly detect potential cross-layer problems.

[0004] (2) The diversity of business logic increases the difficulty of monitoring: Business systems in different industries have their own unique business logic, and even different companies within the same industry may have different business processes. Taking e-commerce systems as an example, in addition to basic business logic such as product display, shopping cart management, and order processing, they may also involve complex business scenarios such as promotional activities, membership points, and supply chain collaboration. These diverse business logics make it difficult to measure and monitor the operating status of business systems using a unified standard. Traditional monitoring methods mainly focus on general server performance indicators, such as CPU utilization and memory usage, and cannot deeply understand the operation at the business logic level. For example, if the response time of a key business function is too long, it may not be caused by server performance bottlenecks, but by insufficient optimization of database query statements, infinite loops in business code, or timeouts in external interface calls. Traditional monitoring methods are unable to accurately locate and analyze these business logic-related problems.

[0005] (3) Insufficient attention to user experience leads to business losses: In today's highly competitive market environment, user experience has become one of the key factors for business success. Users have increasingly higher requirements for the responsiveness, ease of operation, and user-friendliness of web applications. However, traditional web application monitoring methods mostly focus on server-side technical indicators and lack direct attention to user experience. For example, even if the server's performance indicators are normal, insufficient network bandwidth or poor front-end page code optimization may lead to slow page loading, affecting the user's operating experience. If problems encountered by users during use are not discovered and resolved in a timely manner, it will lead to a decline in user satisfaction, which in turn will affect the company's business volume and brand image. According to relevant research, every second increase in page loading time will lead to a significant increase in user churn rate. For businesses such as e-commerce that are highly dependent on user traffic, this will undoubtedly cause huge economic losses.

[0006] (4) Limited Monitoring Comprehensiveness Due to Single Data Source: Most existing web application monitoring systems rely on a single data source, primarily server-side log files or data collected by performance monitoring tools. While this data can reflect the basic operating status of the server, it cannot comprehensively cover all key information during the operation of the business system. For example, server logs may not accurately record users' actual operational behaviors and the specific details of business transactions, while performance monitoring tools can only provide limited performance indicator data. The lack of integration and analysis of multi-source data makes it difficult for monitoring systems to comprehensively and accurately assess the operational quality of the business system. When a business system malfunctions or experiences performance degradation, due to the limited data sources, maintenance personnel often need to spend a lot of time and effort troubleshooting and locating the problem, resulting in low problem-solving efficiency and unnecessary losses for the enterprise.

[0007] (5) Simple Data Analysis Methods Make It Difficult to Uncover Deeper Problems: Traditional Web application monitoring systems typically employ simple methods for data analysis, such as basic statistics and threshold comparisons of collected data. This approach can only detect obvious anomalies and is often ineffective for complex business system operational issues. For example, monitoring server CPU utilization thresholds can promptly identify CPU overload, but it cannot analyze the reasons for abnormal fluctuations in CPU utilization or predict future system performance trends. Furthermore, traditional data analysis methods lack the ability to uncover the relationships between business data, making it difficult to identify potential business risks and optimization opportunities. For instance, correlation analysis of user purchasing behavior data can reveal potential patterns in user purchases of certain goods, providing a basis for enterprises to formulate precise marketing strategies, but traditional monitoring systems cannot achieve this function.

[0008] In summary, existing web application monitoring methods have many shortcomings when dealing with complex and ever-changing business system operating environments. They cannot comprehensively, accurately, and in real time assess the operational quality of business systems, and are unable to meet enterprises' needs for stable operation of business systems and a good user experience.

[0009] Therefore, how to achieve comprehensive, real-time, and accurate monitoring of the operational quality of business systems, promptly identify potential problems and provide effective solutions, thereby improving the stability and reliability of business systems and enhancing user experience, is a pressing technical issue that needs to be addressed. Summary of the Invention

[0010] The technical objective of this invention is to provide a method, system, device, and medium for monitoring the operational quality of a web application business system, in order to address how to achieve comprehensive, real-time, and accurate monitoring of the operational quality of the business system, promptly identify potential problems, and provide effective solutions, thereby improving the stability and reliability of the business system and enhancing the user experience.

[0011] The technical objective of this invention is achieved as follows: a method for monitoring the operational quality of a Web application business system, the specific method of which is as follows:

[0012] Data Acquisition: Collect multi-source data from servers, business systems, and user terminals; among which, multi-source data includes server performance data, business transaction data, and user behavior data;

[0013] Data storage: The collected server performance data, business transaction data, and user behavior data are stored and managed for subsequent processing and analysis;

[0014] Data preprocessing: Preprocessing operations that clean, transform, and integrate the collected raw data to obtain preprocessed data;

[0015] Data analysis: Conduct in-depth analysis of performance indicators, business transactions, and user behavior on preprocessed data, and calculate the overall operational quality score of the business system through a comprehensive evaluation model to obtain the corresponding score results;

[0016] Early warning handling: Based on the results of data analysis, a pre-set early warning threshold is set. When the monitored data exceeds the early warning threshold, the early warning mechanism is triggered, and relevant maintenance personnel are notified via SMS, email, and instant messaging tools.

[0017] Visual presentation: Displaying the results of data analysis to users in the form of intuitive charts and reports.

[0018] As a preferred method, the data collection process is as follows:

[0019] Collect server performance data: Collect server performance metrics such as CPU utilization, memory usage, disk I / O, and network bandwidth in real time by deploying a monitoring agent on the server.

[0020] Collect business transaction data: Collect relevant information about business transactions by embedding tracking points at key nodes in the business system (such as order submission, payment success, etc.); the relevant information about business transactions includes transaction type, transaction time, transaction status, and transaction amount;

[0021] Collect user behavior data: Use front-end scripts (such as JavaScript) to embed data collection code in web pages to collect user operation behavior data on the page; among which, user operation behavior data on the page includes page views, page dwell time, click events and scroll events.

[0022] Ideally, data storage uses a combination of distributed file systems (such as Hadoop HDFS) and relational databases (such as MySQL) to classify and store different types of data.

[0023] Among them, server performance data and business transaction data are stored in a relational database, which facilitates structured querying and statistical analysis;

[0024] User behavior data is stored in a distributed file system to meet the storage needs of massive amounts of data.

[0025] More specifically, the data analysis is as follows:

[0026] Performance indicator analysis: Real-time monitoring and analysis of server performance data, calculation of statistical quantities such as average, maximum, minimum, and standard deviation of various performance indicators, plotting performance trend curves, and timely detection of abnormal performance fluctuations.

[0027] Business transaction analysis: Analyze relevant indicators such as success rate, failure rate, and average response time of business transactions, compare and analyze different types of transactions, and identify problems and bottlenecks in the transaction process; for example, by analyzing the reasons for the failure of order payment transactions, it was found that it was caused by the failure of the third-party payment interface, and relevant personnel were notified in a timely manner to fix it;

[0028] User behavior analysis: Data mining algorithms such as cluster analysis or association rule mining are used to analyze user behavior data, understand user habits and preferences, and analyze user behavior patterns and potential needs; for example, users can be divided into different groups through cluster analysis, and personalized marketing strategies can be developed for the characteristics of different groups.

[0029] Comprehensive evaluation model: Establish a comprehensive evaluation model for the operational quality of the business system, which comprehensively and weights performance indicators, business transaction indicators, and user behavior indicators to obtain an overall operational quality score for the business system; and based on the score results, classify the operational status of the business system into different levels (such as excellent, good, average, poor, and faulty) to provide intuitive reference for operation and maintenance personnel.

[0030] Construct a service importance scoring model, and calculate the formula as follows: Score = 0.3 * SLA level + 0.3 * traffic share + 0.2 * server CPU utilization + 0.2 * server memory utilization.

[0031] Ideally, the warning mechanism should include detailed information on the type of anomaly, the event in which the anomaly occurred, and the location of the anomaly, so that operations and maintenance personnel can quickly locate and resolve the problem.

[0032] Even better, during the visualization process, the key indicators and operating status of the business system are displayed on the large screen in real time; at the same time, it supports user-defined reports, generating various dimensions of analysis reports according to different needs, making it convenient for users to view data and make decisions.

[0033] The analysis reports include daily, weekly, and monthly reports across various dimensions.

[0034] A web application business system operation quality monitoring system, the system comprising:

[0035] The data acquisition module is used to collect multi-source data from servers, business systems, and user terminals; the multi-source data includes server performance data, business transaction data, and user behavior data.

[0036] The data storage module is used to store and manage the collected server performance data, business transaction data, and user behavior data for subsequent processing and analysis.

[0037] The data preprocessing module is used to perform preprocessing operations such as cleaning, transformation and integration on the collected raw data to obtain the preprocessed data;

[0038] The data analysis module is used to perform in-depth analysis of performance indicators, business transactions, and user behavior on the preprocessed data, and to calculate the overall operational quality score of the business system through a comprehensive evaluation model to obtain the corresponding score results.

[0039] The early warning processing module is used to preset early warning thresholds based on the results of data analysis. When the monitored data exceeds the early warning threshold, the early warning mechanism is triggered, and relevant operation and maintenance personnel are notified via SMS, email, and instant messaging tools. The early warning content of the early warning mechanism includes detailed information on the anomaly type, the event in which the anomaly occurred, and the location of the anomaly.

[0040] The visualization module is used to present the results of data analysis to users in the form of intuitive charts and reports on a real-time monitoring screen, displaying key indicators and operational status of the business system. It also supports user-defined reports, generating various dimensions of analysis reports according to different needs, making it convenient for users to view data and make decisions.

[0041] Preferably, the data acquisition module includes:

[0042] The server performance data acquisition submodule is used to collect server performance metrics such as CPU utilization, memory usage, disk I / O, and network bandwidth in real time by deploying a monitoring agent program on the server.

[0043] The business transaction data collection submodule is used to collect relevant information about business transactions by embedding data points at key nodes in the business system (such as order submission, payment success, etc.). The relevant information about business transactions includes transaction type, transaction time, transaction status, and transaction amount.

[0044] The user behavior data collection submodule is used to embed data collection code in web pages using front-end scripts (such as JavaScript) to collect user operation behavior data on the page; among which, user operation behavior data on the page includes page views, page dwell time, click events and scroll events;

[0045] The data storage module uses a combination of distributed file systems (such as Hadoop HDFS) and relational databases (such as MySQL) to classify and store different types of data. Server performance data and business transaction data are stored in relational databases to facilitate structured queries and statistical analysis, while user behavior data is stored in distributed file systems to meet the storage needs of massive amounts of data.

[0046] The data analysis module includes:

[0047] The performance index analysis submodule is used to monitor and analyze server performance data in real time, calculate the average, maximum, minimum, and standard deviation of various performance indicators, draw performance trend curves, and promptly detect abnormal performance fluctuations.

[0048] The business transaction analysis submodule is used to analyze relevant indicators such as the success rate, failure rate, and average response time of business transactions. It compares and analyzes different types of transactions to identify problems and bottlenecks in the transaction process. For example, by analyzing the reasons for the failure of order payment transactions, it was found that the failure was caused by a third-party payment interface failure, and relevant personnel were notified in a timely manner to fix it.

[0049] User behavior analysis: Data mining algorithms such as cluster analysis or association rule mining are used to analyze user behavior data, understand user habits and preferences, and analyze user behavior patterns and potential needs; for example, users can be divided into different groups through cluster analysis, and personalized marketing strategies can be developed for the characteristics of different groups.

[0050] The comprehensive evaluation model submodule is used to establish a comprehensive evaluation model for the operational quality of the business system. It performs a comprehensive weighted calculation of performance indicators, business transaction indicators, and user behavior indicators to obtain an overall operational quality score for the business system. Based on the score results, the operational status of the business system is divided into different levels (such as excellent, good, average, poor, and faulty), providing an intuitive reference for operations and maintenance personnel.

[0051] The scoring model construction submodule is used to build a service importance scoring model. The calculation formula is: Score = 0.3 * SLA level + 0.3 * traffic share + 0.2 * server CPU utilization + 0.2 * server memory utilization.

[0052] An electronic device includes: a memory and at least one processor;

[0053] The memory stores computer-executed instructions;

[0054] The at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute the Web application business system operation quality monitoring method described above.

[0055] A computer-readable storage medium storing computer-executable instructions, wherein when a processor executes the computer-executable instructions, the above-described method for monitoring the operational quality of a web application business system is implemented.

[0056] The Web application business system operation quality monitoring method, system, device, and medium of the present invention have the following advantages:

[0057] (I) This invention achieves end-to-end quality assessment from infrastructure status to business transaction integrity through data collection, data storage, data analysis, early warning, and visualization, combined with business process identification, deep learning algorithms, and big data analysis technology. It adopts a distributed data collection architecture to support real-time performance monitoring, abnormal behavior detection, historical trend analysis, and automated fault response. Through multi-dimensional correlation analysis, it locates the root cause and provides data-driven decision support for system optimization, effectively improving fault location efficiency and reducing business interruption risk. It is suitable for quality assurance of Web applications in high-concurrency scenarios such as finance, e-commerce, and government affairs, and is used to analyze the operational quality of business systems in real time and accurately, ensuring the stable and efficient operation of business systems.

[0058] (II) This invention collects multi-source data, including server performance data, business transaction data, user behavior data, etc., and uses advanced data analysis algorithms to deeply mine and analyze these data, thereby achieving comprehensive, real-time, and accurate monitoring of the operational quality of the business system, timely detection of potential problems and provision of effective solutions, thereby improving the stability and reliability of the business system, enhancing user experience, and providing strong support for the smooth operation of enterprise business. It solves the problem of the existing technology having a single monitoring method and being unable to comprehensively and accurately assess the operational quality of the business system.

[0059] (III) This invention collects multi-source data, including server performance data, business transaction data and user behavior data, and uses a comprehensive evaluation model to conduct a comprehensive analysis of these data. It can accurately evaluate the operational quality of Web application business systems from multiple dimensions, avoiding the limitations of traditional monitoring methods that only focus on server performance. It provides enterprises with more comprehensive and in-depth information on the operational status of business systems and achieves a comprehensive and accurate assessment of operational quality.

[0060] (iv) Through real-time monitoring and in-depth analysis of various types of data, this invention can promptly identify potential problems during the operation of business systems, such as performance bottlenecks, business logic errors, and poor user experience. The early warning mechanism can promptly notify maintenance personnel to handle the issues, resolving them in their early stages, identifying potential problems in a timely manner, preventing problems from escalating, and thus improving the stability and reliability of business systems.

[0061] (v) This invention focuses on user behavior data. By analyzing users’ usage habits and preferences, it can discover the problems and needs that users encounter in actual use. Enterprises can optimize and improve their business systems based on these analysis results, improve the smoothness and convenience of user operations, enhance user experience, and thus increase user satisfaction and loyalty to the business system.

[0062] (vi) This invention presents data analysis results to managers in the form of intuitive charts and reports, providing strong support for corporate decision-making. Managers can rationally adjust resource allocation, optimize business processes, and formulate marketing strategies based on the operational quality scores of the business system and the analysis results of various indicators, thereby improving the company's operational efficiency and competitiveness and supporting decision analysis. Attached Figure Description

[0063] The invention will be further described below with reference to the accompanying drawings.

[0064] Appendix Figure 1 A flowchart illustrating the process of implementing quality monitoring methods for web application business systems. Detailed Implementation

[0065] The following detailed description of the Web application business system operation quality monitoring method, system, device, and medium of the present invention, with reference to the accompanying drawings and specific embodiments, is provided.

[0066] Example 1:

[0067] As attached Figure 1 As shown in the figure, this embodiment provides a method for monitoring the operational quality of a web application business system. The method is as follows:

[0068] S1. Data Acquisition: Collect multi-source data from servers, business systems, and user terminals; among which, multi-source data includes server performance data, business transaction data, and user behavior data;

[0069] S2. Data storage: Store and manage the collected server performance data, business transaction data, and user behavior data for subsequent processing and analysis;

[0070] S3. Data Preprocessing: Preprocessing operations such as cleaning, transforming and integrating the collected raw data to obtain preprocessed data;

[0071] S4. Data Analysis: Conduct in-depth analysis of performance indicators, business transactions, and user behavior on the preprocessed data, and calculate the overall operational quality score of the business system through a comprehensive evaluation model to obtain the corresponding score results;

[0072] S5. Early Warning Handling: Based on the results of data analysis, a pre-set early warning threshold is set. When the monitored data exceeds the early warning threshold, the early warning mechanism is triggered, and relevant maintenance personnel are notified via SMS, email, and instant messaging tools.

[0073] S6. Visualization: Present the results of data analysis to users in the form of intuitive charts and reports.

[0074] The data acquisition in step S1 of this embodiment is as follows:

[0075] S101. Collect server performance data: Collect server performance metrics such as CPU utilization, memory usage, disk I / O, and network bandwidth in real time by deploying a monitoring agent on the server.

[0076] S102. Collect business transaction data: Collect relevant information about business transactions by embedding data points at key nodes in the business system (such as order submission, payment success, etc.); among which, the relevant information about business transactions includes transaction type, transaction time, transaction status and transaction amount;

[0077] S103. Collect user behavior data: Use front-end scripts (such as JavaScript) to embed data collection code in the web page to collect user operation behavior data on the page; among which, user operation behavior data on the page includes page views, page dwell time, click events and scroll events.

[0078] In step S2 of this embodiment, data storage adopts a combination of distributed file system (such as Hadoop HDFS) and relational database (such as MySQL) to classify and store different types of data.

[0079] Among them, server performance data and business transaction data are stored in a relational database, which facilitates structured querying and statistical analysis;

[0080] User behavior data is stored in a distributed file system to meet the storage needs of massive amounts of data.

[0081] The data analysis in step S4 of this embodiment is as follows:

[0082] S401 Performance Index Analysis: Real-time monitoring and analysis of server performance data, calculation of statistical quantities such as average, maximum, minimum, and standard deviation of various performance indicators, plotting performance trend curves, and timely detection of abnormal performance fluctuations.

[0083] S402. Business Transaction Analysis: Analyze relevant indicators such as success rate, failure rate, and average response time of business transactions; conduct comparative analysis of different types of transactions; and identify problems and bottlenecks in the transaction process. For example, by analyzing the reasons for the failure of order payment transactions, it was found that the failure was due to a third-party payment interface failure, and relevant personnel were promptly notified to fix it.

[0084] S403. User Behavior Analysis: Using data mining algorithms such as cluster analysis or association rule mining to analyze user behavior data, understand users' usage habits and preferences, and analyze user behavior patterns and potential needs; for example, by using cluster analysis to divide users into different groups, personalized marketing strategies can be developed for the characteristics of different groups.

[0085] S404. Comprehensive Evaluation Model: Establish a comprehensive evaluation model for the operational quality of the business system, which comprehensively and weights performance indicators, business transaction indicators, and user behavior indicators to obtain an overall operational quality score for the business system; and based on the score results, classify the operational status of the business system into different levels (such as excellent, good, average, poor, and faulty) to provide intuitive reference for operation and maintenance personnel.

[0086] S405. Construct a service importance scoring model, with the following formula: Score = 0.3 * SLA level + 0.3 * traffic share + 0.2 * server CPU utilization + 0.2 * server memory utilization.

[0087] The warning content of the warning mechanism in step S5 of this embodiment includes detailed information on the anomaly type, the event in which the anomaly occurred, and the location of the anomaly, so that maintenance personnel can quickly locate and resolve the problem.

[0088] In the visualization process of step S6 of this embodiment, the key indicators and operating status of the business system are displayed on the large screen in real time; at the same time, it supports user-defined reports and generates analysis reports of various dimensions according to different needs, so as to facilitate users to view data and make decisions.

[0089] The analysis reports include daily, weekly, and monthly reports across various dimensions.

[0090] Example 2:

[0091] This embodiment provides a web application business system operation quality monitoring system, which includes:

[0092] The data acquisition module is used to collect multi-source data from servers, business systems, and user terminals; the multi-source data includes server performance data, business transaction data, and user behavior data.

[0093] The data storage module is used to store and manage the collected server performance data, business transaction data, and user behavior data for subsequent processing and analysis.

[0094] The data preprocessing module is used to perform preprocessing operations such as cleaning, transformation and integration on the collected raw data to obtain the preprocessed data;

[0095] The data analysis module is used to perform in-depth analysis of performance indicators, business transactions, and user behavior on the preprocessed data, and to calculate the overall operational quality score of the business system through a comprehensive evaluation model to obtain the corresponding score results.

[0096] The early warning processing module is used to preset early warning thresholds based on the results of data analysis. When the monitored data exceeds the early warning threshold, the early warning mechanism is triggered, and relevant operation and maintenance personnel are notified via SMS, email, and instant messaging tools. The early warning content of the early warning mechanism includes detailed information on the anomaly type, the event in which the anomaly occurred, and the location of the anomaly.

[0097] The visualization module is used to present the results of data analysis to users in the form of intuitive charts and reports on a real-time monitoring screen, displaying key indicators and operational status of the business system. It also supports user-defined reports, generating various dimensions of analysis reports according to different needs, making it convenient for users to view data and make decisions.

[0098] The data acquisition module in this embodiment includes:

[0099] The server performance data acquisition submodule is used to collect server performance metrics such as CPU utilization, memory usage, disk I / O, and network bandwidth in real time by deploying a monitoring agent program on the server.

[0100] The business transaction data collection submodule is used to collect relevant information about business transactions by embedding data points at key nodes in the business system (such as order submission, payment success, etc.). The relevant information about business transactions includes transaction type, transaction time, transaction status, and transaction amount.

[0101] The user behavior data collection submodule is used to embed data collection code in web pages using front-end scripts (such as JavaScript) to collect user behavior data on the page; among which, user behavior data on the page includes page views, page dwell time, click events, and scroll events.

[0102] In this embodiment, the data storage module uses a combination of a distributed file system (such as Hadoop HDFS) and a relational database (such as MySQL) to classify and store different types of data. Server performance data and business transaction data are stored in the relational database to facilitate structured queries and statistical analysis. User behavior data is stored in the distributed file system to meet the storage needs of massive amounts of data.

[0103] The data analysis module in this embodiment includes:

[0104] The performance index analysis submodule is used to monitor and analyze server performance data in real time, calculate the average, maximum, minimum, and standard deviation of various performance indicators, draw performance trend curves, and promptly detect abnormal performance fluctuations.

[0105] The business transaction analysis submodule is used to analyze relevant indicators such as the success rate, failure rate, and average response time of business transactions. It compares and analyzes different types of transactions to identify problems and bottlenecks in the transaction process. For example, by analyzing the reasons for the failure of order payment transactions, it was found that the failure was caused by a third-party payment interface failure, and relevant personnel were notified in a timely manner to fix it.

[0106] User behavior analysis: Data mining algorithms such as cluster analysis or association rule mining are used to analyze user behavior data, understand user habits and preferences, and analyze user behavior patterns and potential needs; for example, users can be divided into different groups through cluster analysis, and personalized marketing strategies can be developed for the characteristics of different groups.

[0107] The comprehensive evaluation model submodule is used to establish a comprehensive evaluation model for the operational quality of the business system. It performs a comprehensive weighted calculation of performance indicators, business transaction indicators, and user behavior indicators to obtain an overall operational quality score for the business system. Based on the score results, the operational status of the business system is divided into different levels (such as excellent, good, average, poor, and faulty), providing an intuitive reference for operations and maintenance personnel.

[0108] The scoring model construction submodule is used to build a service importance scoring model. The calculation formula is: Score = 0.3 * SLA level + 0.3 * traffic share + 0.2 * server CPU utilization + 0.2 * server memory utilization.

[0109] Example 3:

[0110] This embodiment also provides an electronic device, including: a memory and at least one processor;

[0111] The memory stores computer-executed instructions;

[0112] The at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute the Web application business system operation quality monitoring method according to any one of the present invention.

[0113] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can be a microprocessor or any conventional processor.

[0114] Memory is used to store computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, at least one application program required for a function, etc.; the data storage area can store data created based on the use of the terminal, etc. In addition, memory can also include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart memory cards (SMC), secure digital cards (SD cards), flash memory cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0115] Example 4:

[0116] This embodiment also provides a computer-readable storage medium storing multiple instructions, which are loaded by a processor to cause the processor to execute the Web application business system operation quality monitoring method in any embodiment of the present invention. Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the above embodiments is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0117] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0118] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0119] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0120] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion unit connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion unit execute some and all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring the operational quality of a web application business system, characterized in that, The method is as follows: Data Acquisition: Collect multi-source data from servers, business systems, and user terminals; among which, multi-source data includes server performance data, business transaction data, and user behavior data; Data storage: The collected server performance data, business transaction data, and user behavior data are stored and managed for subsequent processing and analysis; Data preprocessing: Preprocessing operations that clean, transform, and integrate the collected raw data to obtain preprocessed data; Data analysis: Conduct in-depth analysis of performance indicators, business transactions, and user behavior on preprocessed data, and calculate the overall operational quality score of the business system through a comprehensive evaluation model to obtain the corresponding score results; Early warning handling: Based on the results of data analysis, a pre-set early warning threshold is set. When the monitored data exceeds the early warning threshold, the early warning mechanism is triggered, and relevant maintenance personnel are notified via SMS, email, and instant messaging tools. Visual presentation: Displaying the results of data analysis to users in the form of intuitive charts and reports.

2. The method for monitoring the operational quality of a Web application business system according to claim 1, characterized in that, The data collection process is as follows: Collect server performance data: Collect server performance metrics such as CPU utilization, memory usage, disk I / O, and network bandwidth in real time by deploying a monitoring agent on the server. Collect business transaction data: Collect relevant information about business transactions by embedding data points at key nodes in the business system; the relevant information about business transactions includes transaction type, transaction time, transaction status and transaction amount; Collect user behavior data: Use front-end scripts to embed data collection code in web pages to collect user operation behavior data on the page; among which, user operation behavior data on the page includes page views, page dwell time, click events and scroll events.

3. The method for monitoring the operational quality of a Web application business system according to claim 1 or 2, characterized in that, Data storage employs a combination of distributed file systems and relational databases to categorize and store different types of data. Among them, server performance data and business transaction data are stored in a relational database, which facilitates structured querying and statistical analysis; User behavior data is stored in a distributed file system to meet the storage needs of massive amounts of data.

4. The method for monitoring the operational quality of a Web application business system according to claim 3, characterized in that, The data analysis is as follows: Performance indicator analysis: Real-time monitoring and analysis of server performance data, calculation of statistical quantities such as average, maximum, minimum, and standard deviation of various performance indicators, plotting performance trend curves, and timely detection of abnormal performance fluctuations. Business transaction analysis: Analyze relevant indicators such as success rate, failure rate and average response time of business transactions, compare and analyze different types of transactions, and identify problems and bottlenecks in the transaction process; User behavior analysis: Data mining algorithms such as clustering analysis or association rule mining are used to analyze user behavior data, understand users' usage habits and preferences, and analyze user behavior patterns and potential needs; Comprehensive evaluation model: Establish a comprehensive evaluation model for the operational quality of the business system, which comprehensively and weights performance indicators, business transaction indicators, and user behavior indicators to obtain an overall operational quality score for the business system; and based on the score results, classify the operational status of the business system into different levels to provide intuitive reference for operation and maintenance personnel; Construct a service importance scoring model, and calculate the formula as follows: Score = 0.3 * SLA level + 0.3 * traffic share + 0.2 * server CPU utilization + 0.2 * server memory utilization.

5. The method for monitoring the operational quality of a Web application business system according to claim 4, characterized in that, The early warning mechanism includes detailed information on the type of anomaly, the event in which the anomaly occurred, and the location of the anomaly, so that operations and maintenance personnel can quickly locate and resolve the problem.

6. The method for monitoring the operational quality of a Web application business system according to claim 5, characterized in that, During the visualization process, key indicators and operational status of the business system are displayed on a large screen in real time; at the same time, user-defined reports are supported, generating various dimensions of analytical reports according to different needs, making it convenient for users to view data and make decisions. The analysis reports include daily, weekly, and monthly reports across various dimensions.

7. A web application business system operation quality monitoring system, characterized in that, The system includes: The data acquisition module is used to collect multi-source data from servers, business systems, and user terminals; the multi-source data includes server performance data, business transaction data, and user behavior data. The data storage module is used to store and manage the collected server performance data, business transaction data, and user behavior data for subsequent processing and analysis. The data preprocessing module is used to perform preprocessing operations such as cleaning, transformation and integration on the collected raw data to obtain the preprocessed data; The data analysis module is used to perform in-depth analysis of performance indicators, business transactions, and user behavior on the preprocessed data, and to calculate the overall operational quality score of the business system through a comprehensive evaluation model to obtain the corresponding score results. The early warning processing module is used to preset early warning thresholds based on the results of data analysis. When the monitored data exceeds the early warning threshold, the early warning mechanism is triggered, and relevant operation and maintenance personnel are notified via SMS, email, and instant messaging tools. The early warning content of the early warning mechanism includes detailed information on the anomaly type, the event in which the anomaly occurred, and the location of the anomaly. The visualization module is used to present the results of data analysis to users in the form of intuitive charts and reports on a real-time monitoring screen, displaying key indicators and operational status of the business system. It also supports user-defined reports, generating various dimensions of analysis reports according to different needs, making it convenient for users to view data and make decisions.

8. The Web application business system operation quality monitoring system according to claim 7, characterized in that, The data acquisition module includes: The server performance data acquisition submodule is used to collect server performance metrics such as CPU utilization, memory usage, disk I / O, and network bandwidth in real time by deploying a monitoring agent program on the server. The business transaction data collection submodule is used to collect relevant information about business transactions by embedding data points at key nodes in the business system. The relevant information about business transactions includes transaction type, transaction time, transaction status, and transaction amount. The user behavior data collection submodule is used to embed data collection code in web pages using front-end scripts to collect user operation behavior data on the page; among which, user operation behavior data on the page includes page views, page dwell time, click events and scroll events; The data storage module uses a combination of a distributed file system and a relational database to classify and store different types of data. Server performance data and business transaction data are stored in the relational database for easy structured querying and statistical analysis, while user behavior data is stored in the distributed file system to meet the storage needs of massive amounts of data. The data analysis module includes: The performance index analysis submodule is used to monitor and analyze server performance data in real time, calculate the average, maximum, minimum, and standard deviation of various performance indicators, draw performance trend curves, and promptly detect abnormal performance fluctuations. The business transaction analysis submodule is used to analyze relevant indicators such as the success rate, failure rate, and average response time of business transactions, compare and analyze different types of transactions, and identify problems and bottlenecks in the transaction process. User behavior analysis: Data mining algorithms such as clustering analysis or association rule mining are used to analyze user behavior data, understand users' usage habits and preferences, and analyze user behavior patterns and potential needs; The comprehensive evaluation model submodule is used to establish a comprehensive evaluation model for the operational quality of the business system. It performs a comprehensive weighted calculation of performance indicators, business transaction indicators, and user behavior indicators to obtain an overall operational quality score for the business system. Based on the score results, the operational status of the business system is divided into different levels, providing an intuitive reference for operations and maintenance personnel. The scoring model construction submodule is used to build a service importance scoring model. The calculation formula is: Score = 0.3 * SLA level + 0.3 * traffic share + 0.2 * server CPU utilization + 0.2 * server memory utilization.

9. An electronic device, characterized in that, include: Memory and at least one processor; The memory stores computer-executed instructions; The at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to perform the Web application business system operation quality monitoring method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the Web application business system operation quality monitoring method as described in any one of claims 1 to 6.

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