Real-time monitoring method for lean operation of mobile application

Abstract

The invention relates to a real-time monitoring method for lean operation of a mobile application. The real-time monitoring method comprises the following steps: accessing application data needing to be integrated on a mobile application platform; integrating an SDK (Software Development Kit) on a front-end application side and customizing a burying point event according to a service demand and a data analysis demand; when a user triggers a point burying event, collecting corresponding point burying data through JS, sending the point burying data to a point burying gateway through a packaged SDK method, setting a point burying interface in a rear-end monitoring platform, receiving the point burying data sent by a front end, then sending the point burying data to a Kafka message queue, completing data analysis and cleaning through a point burying analysis program, and sending the point burying data to the Kafka message queue; and triggering a timed task through xxljob, cleaning, analyzing and counting the data again, and displaying the deep analysis data of the front-end application in various chart forms. The method has the advantages that data are collected in real time through the point burying service, and user type and application type data can be checked in real time according to different dimensions.

Description

Technical Field

[0001] The present invention relates to the field of big data monitoring and analysis, and in particular to a real-time monitoring method for lean operation of mobile applications. Background Art

[0002] As the global digital wave continues to advance, mobile application construction is a key carrier of digital transformation. It is necessary and advantageous to unify related mobile applications on a mobile platform. This unification can not only improve the performance and stability of the overall system, but also bring greater value to users and enterprises. However, the existing mobile application operation monitoring system can usually only provide regular monitoring reports and cannot monitor the operation status of the application in real time, resulting in the inability of operators to adjust the operation strategy in time, low efficiency of mobile application operation work and low resource utilization. Summary of the invention

[0003] In order to solve the above problems, the purpose of the present invention is to provide a real-time monitoring method for lean operation of mobile applications. By deploying data tracking services on the front-end application side, tracking data can be obtained in real time, so that the monitoring platform can view application and user data in real time according to different dimensions, give full play to the value of data, improve operational efficiency, and optimize resource utilization.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A real-time monitoring method for lean operation of mobile applications, comprising the following steps:

[0006] Step 1: Access the application data to be integrated by creating a new application on the mobile application platform;

[0007] Step 2: Integrate the SDK on the front-end application side and customize the tracking events according to business needs and data analysis needs, obtain the tracking data, and then send it to the back-end;

[0008] Step 3: In the backend monitoring platform, set up a tracking point interface, which is exposed to the frontend through HTTP. The tracking point data sent by the frontend is received, and the tracking point parsing program completes data parsing and cleaning. Different strategies are executed according to different configurations, and the cleaned data is stored in the database.

[0009] Step 4: Trigger the scheduled task through the XXL-JOB scheduler to clean, analyze and count the parsed data in step 3 again, and re-enter the database and cache it after the analysis is completed;

[0010] Step 5: The monitoring platform reads the data in the cache and displays the in-depth analysis data of the front-end application in the form of various charts.

[0011] Preferably, in step 2, the process of obtaining the tracking data is as follows: the front-end page monitors user behavior through JavaScript, and when the user triggers a tracking event, the corresponding tracking data is collected through JavaScript, and the tracking data is sent to the tracking gateway through the packaged SDK. The gateway distinguishes the legitimacy of the tracking and distributes the data to different back-end services according to the data type of the tracking.

[0012] Preferably, the user's interactive events are monitored through JavaScript, and a function for collecting data is defined to collect the monitored data.

[0013] Preferably, the step of executing different strategies according to different configurations and storing the cleaned data in the database is: utilizing the newly added scheduling configuration of the task manager of the XXL-JOB scheduler to perform cleaning statistics on the received tracking data according to rules of different time lengths.

[0014] Preferably, on the monitoring platform, the vue framework is used to develop interactive reports, and the customized development of personalized reports is completed based on the Apache ECharts report component.

[0015] Preferably, the monitoring platform uses a distributed cache system and combines it with JVM-level cache to speed up data filtering and cleaning.

[0016] The present invention has the following beneficial effects:

[0017] 1. The present invention is a real-time monitoring method for lean operation of mobile applications. By deploying a tracking service on the front-end application side, the tracking data of application and user types are collected in real time from different dimensions. The monitoring and analysis service adopts a multi-level cleaning preprocessing program, distributes the preprocessed data to the next level program for storage, and deeply analyzes and calculates the tracking data through different types of algorithms, aggregates and generates reports, stores and caches the aggregated data, and finally displays the data in a visual manner.

[0018] 2. The present invention provides diversified data collection and personalized custom analysis, real-time analysis of user behavior and application data change trends, and achieves full-link and all-round monitoring of applications on mobile platforms, thereby better supporting business operations and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the present invention.

[0020] Figure 2 It is a data processing flow framework diagram of the present invention. DETAILED DESCRIPTION

[0021] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0022] See also Figure 1 and Figure 2 , a real-time monitoring method for lean operation of mobile applications, comprising the following steps:

[0023] Step 1: Create a new application on the mobile application platform to access the application data to be integrated. The application types that support collection and access include H5 applications and mini-program applications.

[0024] Step 2. First, integrate the SDK on the front-end application side and customize the tracking events according to business needs and data analysis needs. In mobile applications, the full Chinese name of SDK (Software Development Kit) is software development kit. SDK provides predefined functions that developers can use to quickly implement specific needs. Integrating SDK can greatly simplify code writing, reduce development time and improve efficiency. SDK can automatically collect user behavior data, application performance data, etc., help developers analyze user usage, provide real-time monitoring dashboards, and display key indicators such as the number of active users and crash rates. For the JS component to be called, introduce the SDK JS file to the page that needs to be collected and initialize the SDK. To add tracking events to collect data in the business logic, you can record specific events through function calls, or directly use the SDK's tracking method to capture user behavior.

[0025] The tracking events include user behavior events and application usage. User behavior events are tracked, including core indicator behavior events such as registered users, online users, active users, number of opens, number of shares, and length of stay, as well as user login behavior, user login time, device used when the user logs in, user logout time, user online time, user organization, user information, user region, user operating system, user device model, operator and other behaviors. Tracking of application usage includes tracking of application type, application major, number of people entering the application, number of clicks on the application, time of entering the application, information about people entering the application, organization, number of messages pushed to the user side by the application, message push time, application type, number of authorized people for the application, application major, time of application launch and launch, and other application behavior data, so as to support calculations from multiple time dimensions to support operational monitoring data statistics functions.

[0026] Then, the front-end page monitors user behavior through JavaScript. When the user triggers a tracking event, the corresponding tracking data is collected through JavaScript. Specifically, JavaScript can be used to monitor user interaction events (such as clicks, scrolling, input, etc.), define a function to collect data, such as TrackSDK.appLogin, TrackSDK.appPage, collect the monitored data, and use a framework or library (such as analytics.is or Tealium) to automatically track data on the application side.

[0027] Finally, the tracking data is sent to the tracking gateway through the packaged SDK. The gateway distinguishes the legitimacy of the tracking and is responsible for distributing the data to different backend services based on the data type of the tracking (such as login events, access events).

[0028] Step 3. In the backend, set up the tracking point interface, which is exposed to the frontend through HTTP and receives the tracking point data sent by the frontend. The tracking point parsing program completes the data parsing verification, which verifies the legality, standardization, and integrity of the original tracking point data. For example, perform simple processing on the original tracking point data, including data verification, decryption, format conversion, etc., to ensure the correctness and consistency of the data, and then execute different strategies according to different configurations to store the cleaned data in the database. For example, use the newly added scheduling configuration of the task manager of the XXL-JOB scheduler to clean and count the login event data and access event data of the tracking point according to different time rules (today, yesterday, past 7 days, 30 days).

[0029] Step 4. Trigger the scheduled task through the XXL-JOB scheduler to clean, analyze and count the parsed data in step 3 again, and re-store and cache it after the analysis is completed. Writing to the cache can quickly respond to user requests and reduce database query delays. In the case of high concurrency among multiple users, it can provide fast data for data statistics front-end display, real-time analysis dashboards, etc. The cache can effectively reduce the pressure on the database and significantly improve the user experience in real-time applications.

[0030] The XXL-JOB scheduler is a distributed task scheduling framework, which is mainly used for the management and execution of scheduled tasks, and monitors the execution results of each scheduled task, including success, failure, timeout and other states. The data cleaning task regularly executed by the XXL-JOB scheduler is to clean the display and analysis of the original data, which is convenient for data query, statistics and analysis. The cleaned login event data and access event data are used for the home page data display of the operation platform management end (overall dashboard, function analysis, exception monitoring, operation indicators and other report displays), user information statistics (user behavior management, analysis, user preferences and other report displays), application information statistics (application overview, application indicators, application analysis, application anomalies and other report displays)

[0031] Step 5: Read the data in the cache and display the in-depth analysis data of the front-end application in a variety of charts. The interactive report of the monitoring platform is developed using the vue framework, and the customized development of personalized reports can be completed based on the Apache ECharts report component.

[0032] The real-time monitoring platform of the present invention adopts the Lambda architecture concept for overall design, uses a distributed cache system + JVM-level cache to accelerate data filtering and cleaning, has massive data processing capabilities, can quickly analyze and process massive data from the client, and has high TPS output capabilities.

[0033] The present invention is a real-time monitoring method for lean operation of mobile applications. By setting a point-of-sale service at the front end, point-of-sale data of application and user classes are collected in real time from different dimensions. The monitoring and analysis service adopts a multi-level cleaning preprocessing program, distributes the preprocessing data to the next level program for storage, and performs in-depth analysis and calculation of the point-of-sale data through different types of algorithms, aggregates and generates reports, and stores and caches the aggregated data, and finally displays the data in a visual way. The present invention provides diversified data collection and personalized custom analysis, analyzes the changing trends of user behavior and application data in real time, and realizes full-link and all-round monitoring of applications on mobile platforms, so as to better support business operations and decision-making.

[0034] The above description is only a specific implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent structural transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A real-time monitoring method for lean operation of mobile applications, characterized by: The steps include: Step 1: Access the application data to be integrated by creating a new application on the mobile application platform; Step 2: Integrate the SDK on the front-end application side and customize the tracking events according to business needs and data analysis needs, obtain the tracking data, and then send it to the back-end; Step 3: In the backend monitoring platform, set up a tracking point interface, which is exposed to the frontend through HTTP. The tracking point data sent by the frontend is received, and the tracking point parsing program completes data parsing and cleaning. Different strategies are executed according to different configurations, and the cleaned data is stored in the database. Step 4: Trigger the scheduled task through the XXL-JOB scheduler to clean, analyze and count the parsed data in step 3 again, and re-enter the database and cache it after the analysis is completed; Step 5: The monitoring platform reads the data in the cache and displays the in-depth analysis data of the front-end application in the form of various charts.

2. A real-time monitoring method for lean operation of mobile applications according to claim 1, characterized in that: In step 2, the process of obtaining the buried data is as follows: the front-end page monitors user behavior through JavaScript. When the user triggers a buried event, the corresponding buried data is collected through JavaScript, and the buried data is sent to the buried gateway through the packaged SDK. The gateway distinguishes the legitimacy of the buried data and distributes the data to different back-end services according to the data type of the buried data. According to claim 2, a real-time monitoring method for lean operation of mobile applications is characterized by: monitoring user interaction events through JavaScript, defining a function for collecting data, collecting the monitored data, and using a framework or library to automatically bury data on the application side.

3. A real-time monitoring method for lean operation of mobile applications according to claim 1, characterized in that: The steps of executing different strategies according to different configurations and storing the cleaned data in the database are as follows: using the newly added scheduling configuration of the task manager of the XXL-JOB scheduler, the received tracking data are cleaned and counted according to the rules of different time lengths.

4. The real-time monitoring method for lean operation of mobile applications according to claim 1, characterized in that: On the monitoring platform, the vue framework is used to develop interactive reports, and the customized development of personalized reports is completed based on the Apache ECharts report component.

5. The real-time monitoring method for lean operation of mobile applications according to claim 1, characterized in that: The monitoring platform uses a distributed cache system and combines it with JVM-level cache to speed up data filtering and cleaning.

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