Front-end abnormity monitoring method and system, terminal and storage medium

Abstract

The invention discloses a front-end anomaly monitoring method and system, a terminal and a storage medium, and the method comprises the steps: collecting specific anomaly information through employing a global capturing mode and a single-point capturing mode when a front-end anomaly is detected, and carrying out the anomaly recording to obtain anomaly recording information; performing exception reason classification and exception level definition according to the exception specific information to obtain exception reasons and an exception priority sequence; on the basis of the abnormal recording information and the abnormal reasons, processing is carried out according to the abnormal levels from high to low, abnormal logs are obtained, and the abnormal logs are reported according to the reporting frequency; and detecting the abnormal log based on an abnormal detection algorithm, triggering an abnormal early warning notification, and generating an abnormal report according to the abnormal log. The front-end abnormity is automatically captured and reported, abnormity classification and analysis are carried out, once the front-end abnormity is detected, an alarm is given out through the robot, the front-end team repairing efficiency can be improved, and the stability and experience of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet anomaly monitoring, and in particular to a front-end anomaly monitoring method, system, terminal and computer-readable storage medium. Background Art

[0002] In daily Web application systems, front-end warnings and exceptions are particularly hidden and may cause the risk of system unavailability at any time. For example, errors may be caused by interface return data format, type changes, empty data due to interactive operations, browser compatibility, etc., which require certain specific conditions to trigger, and can only be seen through the user's browser console, making it extremely difficult to troubleshoot.

[0003] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0004] The main purpose of the present invention is to provide a front-end anomaly monitoring method, system, terminal and computer-readable storage medium, aiming to solve the problem in the prior art that the front-end anomaly is quite hidden and cannot be monitored in time, thus making it impossible to classify and analyze the front-end anomaly, resulting in system unavailability.

[0005] To achieve the above object, the present invention provides a front-end abnormality monitoring method, the front-end abnormality monitoring method comprising the following steps:

[0006] When an abnormality is detected at the front end, global capture and single-point capture are used to collect specific abnormal information, and abnormal recording is performed to obtain abnormal recording information;

[0007] According to the specific information of the exception, the cause of the exception is classified and the level of the exception is defined to obtain the cause of the exception and the priority ranking of the exception;

[0008] Based on the abnormal recording information and the abnormal cause, process according to the abnormal level from high to low, obtain the abnormal log, and log report the abnormal log according to the reporting frequency;

[0009] The abnormal log is detected based on an abnormality detection algorithm, an abnormality warning notification is triggered, and an abnormality report is generated according to the abnormal log.

[0010] Optionally, the front-end abnormality monitoring method, wherein when an abnormality is detected in the front-end, a global capture and a single-point capture method are adopted to collect specific abnormal information, and abnormal recording is performed to obtain abnormal recording information, specifically including:

[0011] When an abnormality is detected in the front end, specific abnormal information is collected. When capturing the front end abnormality, global capture and single-point capture are used. The global capture is used to capture common error problems, and the single-point capture is used to capture special situations in a targeted manner.

[0012] The user operations in the time period before and after the abnormality occurs are recorded to obtain abnormality recording information, and the abnormality recording information is used to restore the abnormality scene.

[0013] Optionally, in the front-end abnormality monitoring method, the abnormality specific information includes: user information, behavior information, abnormality information and environmental information.

[0014] Optionally, the front-end abnormality monitoring method, wherein the abnormality cause classification and abnormality level definition are performed according to the abnormality specific information to obtain the abnormality cause and abnormality priority ranking, specifically includes:

[0015] Obtain an exception code, and classify the front-end exception according to the severity of the exception code to obtain an exception type;

[0016] Classify the causes of the abnormality according to the abnormality type to obtain the causes of the abnormality;

[0017] According to the specific information of the exception, the exceptions are prioritized according to the exception levels to obtain an exception priority ranking table.

[0018] Optionally, in the front-end abnormality monitoring method, the abnormality types include: error, sluggishness, damage, pseudo-death and crash;

[0019] The causes of the exception include: logical errors, data type errors, network errors and system errors.

[0020] Optionally, the front-end abnormality monitoring method, wherein the processing is performed from high to low according to the abnormality level based on the abnormal recording information and the abnormality cause, the abnormality log is obtained, and the abnormality log is reported according to the reporting frequency, specifically including:

[0021] According to the abnormal recording information and the abnormal cause, the front-end abnormalities in the abnormal priority sorting table are processed according to the abnormality level from high to low;

[0022] Obtain an exception log and store the exception log persistently;

[0023] Report the abnormal log to the log server according to the reporting frequency, and the log server is used to review the legality and security of the content of the abnormal log;

[0024] The log reporting includes: immediate reporting, batch reporting and user-initiated submission.

[0025] Optionally, the front-end abnormality monitoring method, wherein the abnormality log is detected based on an abnormality detection algorithm, an abnormality warning notification is triggered, and an abnormality report is generated according to the abnormality log, specifically includes:

[0026] Based on the anomaly detection algorithm, log statistics and analysis are performed on the log anomalies according to different dimensions, including: user dimension, time dimension, operating environment dimension, fine-grained code tracing and scenario backtracking;

[0027] Set trigger message push rules. Once an abnormality is detected, an abnormal warning notification will be automatically triggered. The abnormal warning notification includes the default configuration warning message and the customized warning message;

[0028] Generate an exception report based on multiple exception logs within a preset period.

[0029] In addition, to achieve the above-mentioned purpose, the present invention further provides a front-end abnormality monitoring system, wherein the front-end abnormality monitoring system comprises:

[0030] The exception capture module is used to collect specific exception information by global capture and single-point capture when an exception is detected in the front end, and to record the exception to obtain the exception recording information;

[0031] An exception analysis module is used to classify the exception causes and define the exception levels according to the exception specific information, and obtain the exception causes and exception priority rankings;

[0032] An exception handling module is used to process the exception from high to low according to the exception level based on the exception recording information and the exception cause, obtain the exception log, and report the exception log according to the reporting frequency;

[0033] The abnormality warning module is used to detect the abnormality log based on the abnormality detection algorithm, trigger the abnormality warning notification, and generate an abnormality report according to the abnormality log.

[0034] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a front-end abnormality monitoring program stored in the memory and executable on the processor, and when the front-end abnormality monitoring program is executed by the processor, the steps of the front-end abnormality monitoring method as described above are implemented.

[0035] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a front-end abnormality monitoring program, and when the front-end abnormality monitoring program is executed by the processor, the steps of the front-end abnormality monitoring method as described above are implemented.

[0036] In the present invention, when an abnormality is detected in the front end, a global capture and a single-point capture method are adopted to collect specific information of the abnormality, and perform abnormal recording to obtain abnormal recording information; classify the cause of the abnormality and define the abnormal level according to the specific information of the abnormality, and obtain the cause of the abnormality and the abnormal priority ranking; based on the abnormal recording information and the cause of the abnormality, process from high to low according to the abnormal level, obtain the abnormal log, and log the abnormal log according to the reporting frequency; detect the abnormal log based on the abnormal detection algorithm, trigger the abnormal early warning notification, and generate an abnormal report according to the abnormal log. The present invention automatically captures and reports the front-end abnormality, classifies and analyzes the abnormality, and once a front-end abnormality is detected, an alarm is immediately issued through a robot to remind the front-end team to verify and repair, which can not only improve the efficiency of the front-end team's repair, but also kill most of the potential risks and improve the stability and experience of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of a preferred embodiment of the method for monitoring front-end abnormality of the present invention;

[0038] Figure 2 It is a schematic diagram of the overall process of implementing front-end abnormality monitoring and early warning in a preferred embodiment of the front-end abnormality monitoring method of the present invention;

[0039] Figure 3 It is a structural diagram of a preferred embodiment of the monitoring system for front-end abnormality of the present invention;

[0040] Figure 4 It is a structural diagram of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] The front-end abnormality monitoring method described in the preferred embodiment of the present invention is as follows: Figure 1 and Figure 2 As shown, the front-end abnormality monitoring method includes the following steps:

[0043] Step S10: When an abnormality is detected in the front end, a global capture and a single-point capture method are adopted to collect specific abnormal information, and the abnormal recording is performed to obtain abnormal recording information.

[0044] Specifically, when an abnormality is detected in the front end, specific information of the abnormality is collected, and the specific information of the abnormality includes: user information, behavior information (interface path, execution operation, etc.), abnormal information (abnormal code information) and environmental information (device model, network environment, etc.).

[0045] When capturing front-end exceptions, global capture and single-point capture are used. The global capture is used to capture common error problems, and the single-point capture is used to perform targeted capture of special situations. That is, the front-end capture of exceptions is divided into global capture and single-point capture. The global capture code is centralized and easy to manage. Single-point capture is used as a supplement to capture certain special situations, but it is scattered and not conducive to management. Therefore, the present invention adopts both global capture and single-point capture. The global capture mainly captures common error problems, and the single-point capture is mainly for some specific codes that need special attention. The error can be annotated and explained separately, which is easy to locate.

[0046] Global capture: Through the global interface, such as window.addEventListener('error'), aixos uses interceptor to intercept, encapsulates and packages global functions, and automatically captures.

[0047] Single-point capture: Wrap a single code block in the business code, such as try...catch, and write a special function to collect exception information.

[0048] At the same time, the user operations in the time period before and after the exception are recorded to obtain exception recording information, which is used to restore the exception scene. For an exception, engineers are often unable to reproduce the exception and it is difficult to find the root cause. Here, we need to introduce a concept of "exception recording" to record user operations in the time period before and after the exception occurs, so that engineers can restore the exception scene.

[0049] Step S20: classify the causes of the exceptions and define the levels of the exceptions according to the specific exception information, and obtain the causes of the exceptions and the priority ranking of the exceptions.

[0050] Specifically, an exception code is obtained, and the front-end exception is classified according to the consequence degree of the exception code to obtain an exception type; the exception type includes: error, sluggishness, damage, pseudo-death and crash.

[0051] For example, common errors on the front end are:

[0052] Script error: JavaScript runtime error; resource loading error: such as failure to load images, styles or script files; network error: such as cross-domain request failure; Promise error: uncaught Promise error; console error: error generated by methods such as console.error; different exception errors can be monitored.

[0053] The causes of the exceptions are classified according to the exception types to obtain the causes of the exceptions; the causes of the exceptions include: logic errors (such as script errors), data type errors, network errors (such as failure to load images, styles or script files, and cross-domain request failures) and system errors (such as console errors); according to the specific information of the exceptions, the exceptions are prioritized according to the exception levels to obtain an exception priority ranking table, for example, the levels of collected information are divided into info (normal information), warn (warning, false death), error (error, crash), etc., and the priorities for processing by level are sorted, with the error level being processed first and the warn level being processed next.

[0054] Step S30: Based on the abnormal recording information and the abnormal cause, process according to the abnormal level from high to low, obtain the abnormal log, and report the abnormal log according to the reporting frequency.

[0055] Specifically, according to the exception recording information and the exception cause, the front-end exceptions in the exception priority sorting table are processed according to the exception level from high to low; the exception log is obtained and the exception log is persistently stored. The exception log needs to be persistently stored. Currently, IndexedDB can be used for storage, which has the advantages of large capacity and asynchrony (normal programs need to run in sequence from top to bottom, and the need to be asynchronous step by step means that it can run its code independently without affecting the running of the following code, and the following code can continue to run without waiting for it to finish running).

[0056] The abnormal log is reported to the log server according to the reporting frequency, wherein the log reporting includes: immediate reporting, batch reporting and user active submission, namely the following three types:

[0057] Immediate reporting: After the logs are collected, the reporting function is triggered immediately, which is generally used for the most urgent error exceptions.

[0058] Batch reporting: The collected logs are stored locally. When a certain number of logs are collected, they are packaged and reported at one time to reduce the pressure on the log server.

[0059] User-initiated submission: Provide a button on the interface for users to proactively submit bug reports, which helps strengthen interaction with users.

[0060] Generally, an independent log server is provided to receive client logs. During the receiving process, the log server is used to review the legality and security of the content of the abnormal log to prevent attacks. In addition, since the volume of general logs is very large, open source log storage systems such as ELK can be used for standardized log management and search.

[0061] Step S40: Detect the abnormal log based on an abnormality detection algorithm, trigger an abnormality warning notification, and generate an abnormality report based on the abnormal log.

[0062] Specifically, log statistics and analysis are performed on the log anomalies according to different dimensions based on an anomaly detection algorithm, and the dimensions include: user dimension, time dimension, operating environment dimension, fine-grained code tracing and scenario backtracking;

[0063] User dimension: Design a unique request id (equivalent to a unique identifier, similar to username + email address + device number) for a series of user operations. The same user can also be distinguished when performing operations on different terminals. The reason for the need for user dimension analysis is that the front end uses many devices, which may be used on different computers, mobile devices, etc., and abnormalities may occur on certain specific devices or specific browser versions.

[0064] Time dimension: How an exception occurs requires connecting the story lines before and after the abnormal operation (the story line is equivalent to a series of operation processes) to observe. It does not only involve a user's operation, or even limited to a certain page, but is the final result of a series of events.

[0065] Operating environment dimension: the environment in which applications and services run, such as the network environment in which the application is located, the operating system, device hardware information, etc.

[0066] Fine-grained code tracing: You can trace the code source through SourceMap (a toolkit) and find the location of the abnormal code.

[0067] Scenario backtracking: By connecting the user logs related to the exception, the process of the exception is output in a dynamic effect.

[0068] Set the trigger message push rules. Once an abnormality is detected, the abnormal warning notification will be automatically triggered. The abnormal warning notification includes the default configuration warning message and the customized warning message (that is, in addition to the system default configuration, the administrator can customize the notification conditions and the content of the notification); the warning notification can be pushed through multiple channels, such as email, SMS, etc. Generate an abnormal report based on multiple abnormal logs within a preset period. For example, for the push of log statistical information (user information, request id, abnormal time, abnormal information, etc.), it can automatically generate daily, weekly, monthly, and annual reports and send them to relevant groups by email.

[0069] In the face of an increasingly large user base and different operating scenarios, the present invention introduces intelligent monitoring and analysis, which can automatically capture and report front-end anomalies while the system is running, and perform anomaly classification and analysis. Once a front-end anomaly is detected, the system immediately issues an alarm through a robot to remind the front-end team to verify and repair it, which can not only improve the efficiency of the front-end team's repair, but also eliminate most potential risks, improve the stability and experience of the system, and can automatically monitor front-end abnormal errors, and automatically report and send alarms to developers, providing developers with source code tracing and anomaly classification and analysis functions.

[0070] Furthermore, if Figure 3 As shown, based on the above-mentioned front-end abnormality monitoring method, the present invention also provides a front-end abnormality monitoring system, wherein the front-end abnormality monitoring system includes:

[0071] The abnormality capture module 51 is used to collect specific abnormality information by global capture and single-point capture when an abnormality is detected in the front end, and to perform abnormality recording to obtain abnormality recording information;

[0072] The abnormality analysis module 52 is used to classify the abnormality causes and define the abnormality levels according to the abnormality specific information, and obtain the abnormality causes and abnormality priority rankings;

[0073] The exception processing module 53 is used to process the exception from high to low according to the exception level based on the exception recording information and the exception cause, obtain the exception log, and report the exception log according to the reporting frequency;

[0074] The abnormality warning module 54 is used to detect the abnormality log based on the abnormality detection algorithm, trigger the abnormality warning notification, and generate an abnormality report according to the abnormality log.

[0075] Furthermore, if Figure 4 As shown, based on the above-mentioned front-end abnormality monitoring method and system, the present invention also provides a terminal accordingly, and the terminal includes a processor 10, a memory 20 and a display 30. Figure 4Only some components of the terminal are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0076] The memory 20 may be an internal storage unit of the terminal in some embodiments, such as a hard disk or memory of the terminal. The memory 20 may also be an external storage device of the terminal in other embodiments, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed in the terminal, such as the program code of the installation terminal, etc. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a monitoring program 40 for front-end abnormality is stored on the memory 20, and the monitoring program 40 for front-end abnormality can be executed by the processor 10, thereby realizing the monitoring method for front-end abnormality in the present application.

[0077] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing the front-end abnormality monitoring method.

[0078] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface. The processor 10, the memory 20, and the display 30 of the terminal communicate with each other via a system bus.

[0079] In one embodiment, when the processor 10 executes the front-end abnormality monitoring program 40 in the memory 20, the following steps are implemented:

[0080] When an abnormality is detected at the front end, global capture and single-point capture are used to collect specific abnormal information, and abnormal recording is performed to obtain abnormal recording information;

[0081] According to the specific information of the exception, the cause of the exception is classified and the level of the exception is defined to obtain the cause of the exception and the priority ranking of the exception;

[0082] Based on the abnormal recording information and the abnormal cause, process according to the abnormal level from high to low, obtain the abnormal log, and log report the abnormal log according to the reporting frequency;

[0083] The abnormal log is detected based on an abnormality detection algorithm, an abnormality warning notification is triggered, and an abnormality report is generated according to the abnormal log.

[0084] When an abnormality is detected in the front end, a global capture and a single-point capture method are adopted to collect specific abnormal information, and abnormal recording is performed to obtain abnormal recording information, which specifically includes:

[0085] When an abnormality is detected in the front end, specific abnormal information is collected. When capturing the front end abnormality, global capture and single-point capture are used. The global capture is used to capture common error problems, and the single-point capture is used to capture special situations in a targeted manner.

[0086] The user operations in the time period before and after the abnormality occurs are recorded to obtain abnormality recording information, and the abnormality recording information is used to restore the abnormality scene.

[0087] The abnormal specific information includes: user information, behavior information, abnormal information and environmental information.

[0088] The abnormal cause classification and abnormal level definition are performed according to the abnormal specific information to obtain the abnormal cause and abnormal priority ranking, specifically including:

[0089] Obtain an exception code, and classify the front-end exception according to the severity of the exception code to obtain an exception type;

[0090] Classify the causes of the abnormality according to the abnormality type to obtain the causes of the abnormality;

[0091] According to the specific information of the exception, the exceptions are prioritized according to the exception levels to obtain an exception priority ranking table.

[0092] The abnormal types include: error, sluggishness, damage, pseudo-death and crash;

[0093] The causes of the exception include: logical errors, data type errors, network errors and system errors.

[0094] The processing based on the abnormal recording information and the abnormal cause is performed from high to low according to the abnormal level, the abnormal log is obtained, and the abnormal log is reported according to the reporting frequency, specifically including:

[0095] According to the abnormal recording information and the abnormal cause, the front-end abnormalities in the abnormal priority sorting table are processed according to the abnormality level from high to low;

[0096] Obtain an exception log and store the exception log persistently;

[0097] Report the abnormal log to the log server according to the reporting frequency, and the log server is used to review the legality and security of the content of the abnormal log;

[0098] The log reporting includes: immediate reporting, batch reporting and user-initiated submission.

[0099] The detecting of the abnormal log based on the abnormal detection algorithm, triggering the abnormal warning notification, and generating the abnormal report according to the abnormal log specifically includes:

[0100] Based on the anomaly detection algorithm, log statistics and analysis are performed on the log anomalies according to different dimensions, including: user dimension, time dimension, operating environment dimension, fine-grained code tracing and scenario backtracking;

[0101] Set trigger message push rules. Once an abnormality is detected, an abnormal warning notification will be automatically triggered. The abnormal warning notification includes the default configuration warning message and the customized warning message;

[0102] Generate an exception report based on multiple exception logs within a preset period.

[0103] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a front-end abnormality monitoring program, and when the front-end abnormality monitoring program is executed by a processor, the steps of the front-end abnormality monitoring method as described above are implemented.

[0104] In summary, the present invention provides a monitoring method, system, terminal and computer-readable storage medium for front-end anomalies, the method comprising: when an anomaly is detected in the front end, using global capture and single-point capture methods to collect specific information of the anomaly, and recording the anomaly to obtain the anomaly recording information; classifying the cause of the anomaly and defining the level of the anomaly according to the specific information of the anomaly, obtaining the cause of the anomaly and the priority of the anomaly; based on the anomaly recording information and the cause of the anomaly, processing from high to low according to the level of the anomaly, obtaining the anomaly log, and reporting the anomaly log according to the reporting frequency; detecting the anomaly log based on the anomaly detection algorithm, triggering an anomaly warning notification, and generating an anomaly report according to the anomaly log. The present invention automatically captures and reports the front-end anomaly, classifies and analyzes the anomaly, and once a front-end anomaly is detected, an alarm is immediately issued through a robot to remind the front-end team to verify and repair, which can not only improve the efficiency of the front-end team's repair, but also kill most of the potential risks and improve the stability and experience of the system.

[0105] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or terminal including the element.

[0106] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium that can be read by a computer, and the program can include the processes of the above-mentioned method embodiments when executed. The computer-readable storage medium can be a memory, a disk, an optical disk, etc.

[0107] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A front-end abnormality monitoring method, characterized in that: The front-end abnormality monitoring method includes: When an abnormality is detected at the front end, global capture and single-point capture are used to collect specific abnormal information, and abnormal recording is performed to obtain abnormal recording information; According to the specific information of the exception, the cause of the exception is classified and the level of the exception is defined to obtain the cause of the exception and the priority ranking of the exception; Based on the abnormal recording information and the abnormal cause, process according to the abnormal level from high to low, obtain the abnormal log, and log report the abnormal log according to the reporting frequency; The abnormal log is detected based on an abnormality detection algorithm, an abnormality warning notification is triggered, and an abnormality report is generated according to the abnormal log.

2. The front-end abnormality monitoring method according to claim 1 is characterized in that: When an abnormality is detected in the front end, a global capture and a single-point capture method are adopted to collect specific abnormal information, and abnormal recording is performed to obtain abnormal recording information, which specifically includes: When an abnormality is detected in the front end, specific abnormal information is collected. When capturing the front end abnormality, global capture and single-point capture are used. The global capture is used to capture common error problems, and the single-point capture is used to capture special situations in a targeted manner. The user operations in the time period before and after the abnormality occurs are recorded to obtain abnormality recording information, and the abnormality recording information is used to restore the abnormality scene.

3. The front-end abnormality monitoring method according to claim 2 is characterized in that: The abnormal specific information includes: user information, behavior information, abnormal information and environmental information.

4. The front-end abnormality monitoring method according to claim 1, characterized in that: The abnormal cause classification and abnormal level definition are performed according to the abnormal specific information to obtain the abnormal cause and abnormal priority ranking, specifically including: Obtain an exception code, and classify the front-end exception according to the severity of the exception code to obtain an exception type; Classify the causes of the abnormality according to the abnormality type to obtain the causes of the abnormality; According to the specific information of the exception, the exceptions are prioritized according to the exception levels to obtain an exception priority ranking table.

5. The front-end abnormality monitoring method according to claim 4 is characterized in that: The abnormal types include: error, sluggishness, damage, pseudo-death and crash; The causes of the exception include: logical errors, data type errors, network errors and system errors.

6. The front-end abnormality monitoring method according to claim 4 is characterized in that: The processing is performed from high to low according to the abnormal level based on the abnormal recording information and the abnormal cause, obtaining the abnormal log, and reporting the abnormal log according to the reporting frequency, specifically including: According to the abnormal recording information and the abnormal cause, the front-end abnormalities in the abnormal priority sorting table are processed according to the abnormality level from high to low; Obtain an exception log and store the exception log persistently; Report the abnormal log to the log server according to the reporting frequency, and the log server is used to review the legality and security of the content of the abnormal log; The log reporting includes: immediate reporting, batch reporting and user-initiated submission.

7. The front-end abnormality monitoring method according to claim 1, characterized in that: The detecting the abnormal log based on the abnormality detection algorithm, triggering the abnormality warning notification, and generating an abnormality report according to the abnormal log specifically includes: Based on the anomaly detection algorithm, log statistics and analysis are performed on the log anomalies according to different dimensions, including: user dimension, time dimension, operating environment dimension, fine-grained code tracing and scenario backtracking; Set trigger message push rules. Once an abnormality is detected, an abnormal warning notification will be automatically triggered. The abnormal warning notification includes the default configuration warning message and the customized warning message; Generate an exception report based on multiple exception logs within a preset period.

8. A front-end abnormality monitoring system, characterized in that: The front-end abnormal monitoring system includes: The exception capture module is used to collect specific information of the exception by global capture and single-point capture when an exception is detected in the front end, and to record the exception to obtain the exception recording information; An exception analysis module is used to classify the exception causes and define the exception levels according to the exception specific information, and obtain the exception causes and exception priority rankings; An exception handling module is used to process the exception from high to low according to the exception level based on the exception recording information and the exception cause, obtain the exception log, and report the exception log according to the reporting frequency; The abnormality warning module is used to detect the abnormality log based on the abnormality detection algorithm, trigger the abnormality warning notification, and generate an abnormality report according to the abnormality log.

9. A terminal, characterized in that: The terminal includes: a memory, a processor, and a front-end abnormality monitoring program stored in the memory and executable on the processor. When the front-end abnormality monitoring program is executed by the processor, the steps of the front-end abnormality monitoring method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a front-end abnormality monitoring program, and when the front-end abnormality monitoring program is executed by a processor, the steps of the front-end abnormality monitoring method according to any one of claims 1 to 7 are implemented.

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