Monitoring method and related equipment
By monitoring the exception prompt information of the dialog box on the front end of the application, counting the number of exception types and issuing alarms, the problem of low efficiency in application abnormal monitoring in the existing technology is solved, and fast and efficient abnormal detection is achieved.
Patent Information
- Application Number
- CN202510088101.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-20
- Publication Date
- 2025-06-06
AI Technical Summary
The prior art is inefficient in monitoring application exceptions, requiring filtering and analyzing metric data from a large number of application logs to determine the exception type.
By monitoring whether a dialog box containing exception prompt information pops up on the front end of the application, obtain these prompt information, count the number of different exception types, and issue corresponding alarm information when the preset threshold is reached.
The ability to quickly determine application exceptions is realized, and the efficiency is significantly improved compared to the way of collecting data from the application backend and then analyzing it.
Smart Images
Figure CN120104415A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of operation and maintenance, and in particular to a monitoring method, apparatus, equipment, storage medium and computer program product. Background Art
[0002] An application exception is an error or problem that occurs when running an application.
[0003] At present, in order to deal with large-scale anomalies in applications and ensure the stable operation of applications, it is often necessary to monitor multiple running applications. The monitoring method for application anomalies usually includes: collecting indicator data that can reflect application anomalies from aspects such as interface response and network delay from the application logs of the same application backend of multiple terminals; judging whether there are anomalies in the collected indicator data based on the set alarm threshold, so as to obtain monitoring results on whether large-scale anomalies occur in the application. Generally, the application log contains a large amount of indicator data generated by the operation of the application. It takes a certain amount of time to filter out indicator data that can reflect application anomalies from a large amount of indicator data, and to comprehensively analyze the indicator data based on multiple indicators to determine the type of anomaly, which is inefficient. Summary of the invention
[0004] Embodiments of the present application provide a monitoring method, apparatus, device, storage medium, and computer program product to solve the problem of how to efficiently determine whether an application is abnormal.
[0005] The present application embodiment adopts the following technical solutions: A monitoring method, comprising: Monitoring whether a dialog box pops up at the front end of a target application on at least one terminal; the dialog box is a dialog box containing abnormal prompt information; When a dialog box pops up, obtain the abnormal prompt information displayed in the dialog box; Count the number of abnormal prompt information used to characterize different abnormal types; When the number of abnormal prompt information of the target abnormal type reaches a preset threshold, an alarm message corresponding to the target abnormal type is issued.
[0006] A monitoring device, comprising: A real-time monitoring unit, used to monitor whether a dialog box pops up at the front end of a target application on at least one terminal; the dialog box is a dialog box containing abnormal prompt information; A data acquisition unit, used to obtain abnormal prompt information displayed in a dialog box when a dialog box pops up; A data analysis unit, used to count the number of abnormal prompt information used to characterize different abnormal types; The alarm unit is used to issue an alarm message corresponding to the target abnormal type when the number of abnormal prompt messages of the target abnormal type reaches a preset threshold.
[0007] A computing device, comprising: a memory and a processor, wherein: The memory is used to store computer programs; The processor is coupled to the memory and is used to execute the computer program stored in the memory to perform the above method.
[0008] A computer-readable storage medium storing a computer program, wherein the computer program can implement the above method when executed by a computer.
[0009] A computer program product comprises a computer program, and when the computer program is executed by a processor, the above method can be implemented.
[0010] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: The embodiment of the present application obtains the abnormal prompt information displayed in the dialog box that directly interacts with the user, directly analyzes the number of different abnormal types appearing on at least one terminal through the abnormal prompt information, and then sends an alarm information for the target abnormal type to the operation and maintenance team based on the number of occurrences of the target abnormal type. Since the number is directly obtained based on the abnormal prompt information displayed on the dialog box, compared with the method of collecting data from the application backend and then analyzing to determine the abnormality, the present application can quickly determine the application abnormality. BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A specific implementation flow chart of a monitoring method provided in an embodiment of the present application; Figure 2 A schematic diagram of a specific implementation architecture for processing abnormal prompt information in a monitoring method provided in an embodiment of the present application; Figure 3 A schematic diagram of a specific data storage structure for storing abnormal prompt information in a monitoring method provided in an embodiment of the present application; Figure 4 A schematic diagram of a rule configuration page for a dialog box returned for an application exception in a monitoring method provided in an embodiment of the present application; Figure 5 A schematic diagram of a specific structure of a monitoring device provided in an embodiment of the present application; Figure 6 A schematic diagram of the specific structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0012] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.
[0013] Those skilled in the art may appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.
[0014] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, which is only to describe the distinction mode adopted by the objects of the same attributes when describing in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.
[0015] In order to solve the problem of how to efficiently determine whether an application is abnormal, Embodiment 1 of the present application provides a method for monitoring application abnormalities.
[0016] The executor of the method may be any computing device that can implement the method, such as a server, a mobile phone, a personal computer, a smart wearable device, a smart robot, and the like.
[0017] In addition, the execution order of different steps is not limited in the embodiment of the present application. When using the method provided in the embodiment of the present application, the execution order of different steps can be adjusted according to actual needs.
[0018] For ease of description, the following takes an application abnormality monitoring device as an example of the execution subject of the method to introduce the method provided by the embodiment of the present application in detail. The monitoring device may specifically be an application monitoring platform.
[0019] like Figure 1 As shown, it is a specific implementation flow chart of a monitoring method provided in an embodiment of the present application, including the following steps 11 to 14: Step 11, monitoring whether a dialog box pops up at the front end of the target application on at least one terminal; the dialog box is a dialog box containing abnormal prompt information.
[0020] The front end of an application refers to the interface part with which users can directly interact, and is mainly responsible for displaying information and receiving user input.
[0021] A dialog box is a user interface element of an application, usually used to interact with the user. A dialog box usually pops up above the main interface of the application to display information, request user input, or confirm user operations, etc.
[0022] When an application is abnormal, a dialog box is usually popped up on the front end of the application to display the abnormal prompt information (error code and / or a brief description of the problem), and the application's various indicator data are recorded in the application log of the back end of the application. The application back end refers to the part that processes the application business logic and stores application-related data.
[0023] Monitoring whether a dialog box pops up at the front end of a target application on at least one terminal specifically includes: embedding SDK (Software Development Kit) code in the application, the embedded SDK code is used to create an HTTP (Hypertext Transfer Protocol) request to obtain a dialog box that pops up at the front end of the application when the user behavior meets the set conditions; wherein the set conditions can be the user starting the application, clicking a page button, and other behaviors; monitoring at least one terminal with the target application installed to determine whether a dialog box pops up at the front end of the target application.
[0024] The purpose of monitoring at least one terminal is to make it clear that the abnormal type determined later is not caused by a single target application but by a large-scale application, so as to send the alarm information of the abnormal type to the operation and maintenance personnel to prompt them to respond to the abnormality quickly and ensure the stable operation of the application.
[0025] The dialog box of interest in this embodiment is a dialog box containing abnormal prompt information.
[0026] Step 12: When a dialog box pops up, obtain the abnormal prompt information displayed in the dialog box.
[0027] The exception prompt information usually includes an error code and / or a description of the problem. Based on the exception prompt information, the type of exception that occurs in the application program that pops up the current dialog box can be determined.
[0028] If the exception prompt message is "Failure: the user login account does not exist or has expired", it can be determined that the exception type is a user account exception; if the exception prompt message is "Function abnormality, please try again later", it can be determined that the exception type is an application system function abnormality; if the exception prompt message is "Network unresponsive, please try again later", it can be determined that the exception type is a network abnormality; and so on.
[0029] For the abnormal prompt information obtained, you can use the log file type (such as the suffix .log) to record it, and record it on the cluster node that uses Nginx (a high-performance server) as the web server (web server, used to provide online information browsing services). Compared with the traditional background service architecture, Nginx has higher performance and reliability, and can effectively reduce data latency and system load. Some simple processing logic (such as supplementing user IP addresses, server identifiers, and setting response parameters) can also be easily and efficiently completed through Nginx's lua module, reducing the overhead of data serialization to achieve efficient execution speed, and at the same time, there is no need to care about the host or operating system operating environment to achieve stronger scalability.
[0030] Log files are used to record abnormal prompt information and other data because log files can be generated asynchronously, effectively reducing system performance risks and coupling with other components.
[0031] In order to facilitate the subsequent analysis and processing of the acquired abnormal prompt information, this embodiment further collects the logs on the Nginx cluster nodes through Flume, pushes the collected logs to the message queue (kafka / MQ, both of which are important message transmission components in distributed systems); further develops application services to read the log information in the message queue, and writes the read log information into Elasticsearch (a distributed, open source, highly scalable, and highly real-time search and data analysis engine) for data storage. In addition to being used for efficient storage, Elasticsearch also supports data retrieval, complex queries, and aggregate analysis, providing support for visual reports.
[0032] like Figure 2 The figure below is a schematic diagram of the specific implementation architecture for processing exception prompt information, which includes an application embedded with SDK code, an Nginx cluster for recording exception prompt information, a Flume+ message queue for collecting and transferring exception prompt information, and Elasticsearch for reading and storing exception prompt information.
[0033] It should be noted that, in addition to processing exception prompt information, this architecture can also be used to process the associated data mentioned below.
[0034] In order to fully understand the detailed information of the application when an exception occurs, this embodiment also collects the associated data of the pop-up dialog box after the dialog box pops up. The associated data at least includes the pop-up time of the dialog box, the user information corresponding to the pop-up dialog box, and the geographical location information of the terminal where the dialog box pops up.
[0035] The associated data is used to assist in analyzing the abnormal prompt information to form an alarm message containing a qualifier; the qualifier is determined based on a statistical analysis of the associated data.
[0036] For example, statistical analysis shows that a large number of dialog boxes pop up in response to network anomalies in a certain area of city A. The alarm information may include information with location coordinates of area A, so that operation and maintenance personnel can accurately view the network connection status of the area and quickly complete fault repair.
[0037] like Figure 3 As shown, in order to facilitate the storage of abnormal prompt information and related data, a schematic diagram of the data storage structure used in this embodiment is shown. Among them, UUID (Universally Unique Identifier) is a software construction standard, which is used to identify target applications installed on different terminals in this embodiment; pop-up time is the pop-up time of the dialog box in the application; push time is the time when the dialog box is pushed to the user and received by the user. Under normal circumstances, the pop-up time is consistent with the push time; pop-up name, usually related to the abnormal type, or used to summarize the abnormal prompt information in the pop-up dialog box; user code is a user code related to a user account and used to distinguish different application users; the pop-up content is the abnormal prompt information or part of the abnormal prompt information; the city code is the city code to which the specific geographical location of the terminal where the application is logged in belongs; pop_window_type is the type of pop-up window, that is, the type of dialog box that pops up, including at least a warning box (used to display important information or warnings), a confirmation dialog box (used to confirm the user's operation), a login box (used to require the user to enter login information to access protected content or functions), and an information prompt box (used to display general information or prompts to the user, usually without the user having to perform complex operations); this embodiment mainly focuses on warning boxes and information prompt boxes; rsid is the type of operating system installed on the terminal where the target application is located, such as the ios operating system and the Android operating system; region is the provincial geographical location information of the terminal where the target application is logged in; team_type is the number or code of the operation and maintenance team responsible for the corresponding pop-up dialog box.
[0038] In a feasible implementation, in order to more clearly display the abnormal prompt information and related data, this embodiment also develops a visual report for displaying the acquired abnormal prompt information and related data, such as the abnormal prompt information displayed in the dialog box, the number of dialog boxes, the time distribution of the dialog box, etc., for abnormal monitoring and abnormal trend analysis. The visual report can be displayed on the monitoring device. The front-end page for displaying the visual report uses Vue.js (a lightweight JavaScript framework for building user interfaces) + ElementUI (a set of desktop UI component libraries based on Vue.js) technology to build a user interaction interface and page layout. Through the ECharts (a JavaScript-based data visualization chart library) framework, it is integrated into the front end for drawing bar charts, line charts, pie charts and other visual reports to intuitively display data. The back end is based on the Flask (a lightweight Web application framework written in Python) framework, providing RESTfulAPI (an application interface design style based on the HTTP protocol, which is widely used to build network services and applications) service, processing front-end requests, and calling the back-end data processing logic. Based on Elasticsearch, it supports the data range and conditions selected by users in real time, and dynamically generates and updates visual reports to improve user experience.
[0039] Step 13: Count the number of abnormal prompt information used to characterize different abnormal types.
[0040] Since the exception prompt information contains an error code or a description of the problem, the exception type can be determined based on the specific content of the exception prompt information; and the number of exception prompt information corresponding to each exception type can be statistically characterized to determine the number of occurrences of each exception type.
[0041] Step 14: When the number of abnormal prompt information of the target abnormal type reaches a preset threshold, an alarm message corresponding to the target abnormal type is issued.
[0042] In this step, the front-end Vue+Element UI and the back-end Flask framework are closely integrated to achieve the visual configuration and management of dialog-based alarms and message sending. Operation and maintenance personnel can easily complete rule configuration, screening, viewing and other operations through a friendly operation interface. At the same time, the system can automatically process data, generate alarms and push them in real time, providing strong support for business operations.
[0043] The front-end is based on Vue.js with the Element UI framework to build a responsive and user-friendly operation interface. The ECharts library is integrated to intuitively display the various indicators of the dialog configuration, including matching rules, types, order creation thresholds, alarm thresholds, channels, and sending content. The back-end uses the Flask framework to build a RESTful API to process the alarm center front-end requests and execute business logic. All configured alarm rules are retrieved from the PostgreSQL database at regular intervals. Alarm information is generated based on data such as the alarm threshold in the rule and the number of dialog boxes in the current time interval. If the alarm condition is triggered, the configured notification channel sending interface is called to push the alarm information in real time. The alarm rule may be that the number of abnormal prompt messages corresponding to different abnormal types that appear within a set time period (such as 1 minute) exceeds the number (preset threshold), and an alarm message is issued.
[0044] The alarm judgment rule can be to query the abnormal prompt information of the application in the current time period from Elasticsearch once every 1 minute through a scheduled task, aggregate it according to the "abnormal type (or error classification)" configured by the alarm center, and judge whether the abnormality of this type reaches the threshold. If it reaches the threshold, according to the configuration, the key information such as the "abnormal type", number of errors, and impact scope within 1 minute will be sent to the corresponding notification channel (such as SMS, email, enterprise WeChat, etc.) for early warning; if the abnormality type does not appear for 30 minutes, an alarm recovery notification will be sent.
[0045] In order to promptly resolve the exceptions that occur in the application, this embodiment also provides an alarm solution for application users, which is specifically divided into the case of non-first alarm and the case of first alarm for the target exception type.
[0046] Case 1: if the alarm information is not the first alarm of the corresponding target exception type, an exception handling suggestion corresponding to the target exception type is selected from a preset dialog configuration library; Returning the exception handling suggestion to the user in the form of a dialog box; The dialog configuration library stores the correspondence between exception types and exception handling suggestions.
[0047] The display content in the dialog box returned to the user is determined based on the exception prompt information and exception handling suggestions.
[0048] The displayed content in the dialog box is based on the Spring Cloud microservice architecture, and the return prompt matching rules are replaced and optimized at the gateway layer.
[0049] like Figure 4The following is a schematic diagram of the rule configuration page for displaying content in the return dialog box. The rules are configured for a single API interface and are stored in the redis cache after configuration. The prompt replacement types in the return dialog box are divided into: fuzzy, precise, and merged, with the priority order being: precise → fuzzy → merged. Precise: The configured return dialog box rules are consistent with the original return prompt of the interface (abnormal prompt information), and the configured solution is encapsulated in a message and returned to the front end; Fuzzy: The interface return prompt contains the configured return dialog box rules, and the configured solution is encapsulated in a message and returned to the front end; Merge: The configured return dialog box rules are separated by %, and the actual processing is cut according to %. The interface return prompt starts with the content before % and ends with the content after %, and the configured solution is spliced on the basis of the original prompt returned by the interface.
[0050] The prompt replacement type is determined based on the abnormal prompt information in the dialog box detected in step 11; replacement refers to replacing the content in the dialog box.
[0051] Figure 4 The rule configuration list shown includes pop-up rules, interface codes, replacement types, validity, solutions and operations, etc. Among them, pop-up rules refer to the exception prompt information displayed in the dialog box; interface codes refer to the implementation code of the interface targeted by the rule; replacement types include merge, fuzzy and precise; validity indicates the validity of the current rule; solutions refer to the solutions for the exceptions corresponding to the current rule; operations include delete and modify, which are used to operate the current configuration rules.
[0052] When the gateway layer obtains the return information from the microservice, it parses the return message and uses the requestURI to read the redis cache. If the result is "empty", it is assumed to be unconfigured and no processing is required; otherwise, the data obtained from redis is processed and the prompts in the return dialog box are displayed in real time according to the replacement type.
[0053] Case 2: If the alarm information is the first alarm of the corresponding target abnormality type, then: Calculate the similarity between the abnormal prompt information of the target abnormal type and the business problems in the business problem knowledge base to determine at least one business problem similar to the abnormal prompt information; Through the at least one business problem, a business problem knowledge base is searched to determine a solution corresponding to the abnormal prompt information; the business problem knowledge base stores the corresponding relationship between abnormal problems and solutions.
[0054] The similarity may be calculated using a similarity matching algorithm, such as the BM25 algorithm. When the similarity value between the calculated abnormal prompt information and the business question is greater than 40, the two are considered similar.
[0055] In order to enrich the dialog configuration library, for the solutions determined by the business problem knowledge base, when it is verified that they can indeed solve the exception problems corresponding to the exception prompt information, the exception prompt information and the solution will be written into the dialog configuration library, so that when the alarm corresponding to the exception prompt information is encountered later, the solution can be directly found from the dialog configuration library.
[0056] The above embodiment of the present application obtains the abnormal prompt information displayed in the dialog box that directly interacts with the user, directly analyzes the number of different abnormal types appearing on at least one terminal through the abnormal prompt information, and then sends an alarm information for the target abnormal type to the operation and maintenance team based on the number of occurrences of the target abnormal type. Since the number is directly obtained based on the abnormal prompt information displayed on the dialog box, compared with the method of collecting data from the application backend and then analyzing to determine the abnormality, the present application can quickly determine the application abnormality.
[0057] In order to solve the problem of how to efficiently determine whether an application is abnormal, based on the same inventive concept as the above-mentioned embodiments of the present application, Embodiment 2 of the present application provides a monitoring device for application abnormalities.
[0058] The specific structural diagram of the device is shown in Figure 5 As shown, it includes the following functional units 51-54: A real-time monitoring unit 51 is used to monitor whether a dialog box pops up at the front end of a target application on at least one terminal; the dialog box is a dialog box containing abnormal prompt information; The data acquisition unit 52 is used to obtain the abnormal prompt information displayed in the dialog box when the dialog box pops up; A data analysis unit 53, used for counting the number of abnormal prompt information used to characterize different abnormal types; The alarm unit 54 is used to issue an alarm message corresponding to the target abnormality type when the number of abnormality prompt messages of the target abnormality type reaches a preset threshold.
[0059] Among them, the data collection unit 52 is further used to collect related data of the pop-up dialog box, and the related data is used to assist in analyzing the abnormal prompt information to form an alarm information containing qualifiers; the qualifiers are determined based on statistical analysis of the related data; the related data at least includes the pop-up time of the dialog box, the user information corresponding to the pop-up dialog box, and the geographical location information of the terminal where the dialog box pops up.
[0060] The data collection unit 52 is further used to save the acquired abnormal prompt information and the associated data in a log file type.
[0061] In order to promptly respond to the alarm information that appears, the monitoring device of this embodiment also includes a feedback unit, which is used to feedback exception handling suggestions to the application user in the form of a return dialog box based on the abnormal prompt information that triggers the alarm.
[0062] Feedback unit, specifically for: If the alarm information is not the first alarm of the corresponding target exception type, an exception handling suggestion corresponding to the target exception type is selected from the preset dialog configuration library; the exception handling suggestion is returned to the user in the form of a dialog box; the dialog configuration library stores the correspondence between the exception type and the exception handling suggestion. The display content in the dialog box returned to the user is determined based on the exception prompt information and the exception handling suggestion.
[0063] If the alarm information is the first alarm of the corresponding target exception type, then: calculate the similarity between the exception prompt information of the target exception type and the business problems in the business problem knowledge base to determine at least one business problem similar to the exception prompt information; through the at least one business problem, search the business problem knowledge base to determine the solution corresponding to the exception prompt information; the business problem knowledge base stores the correspondence between exception problems and solutions.
[0064] The above embodiment of the present application obtains the abnormal prompt information displayed in the dialog box that directly interacts with the user, directly analyzes the number of different abnormal types appearing on at least one terminal through the abnormal prompt information, and then sends an alarm information for the target abnormal type to the operation and maintenance team based on the number of occurrences of the target abnormal type. Since the number is directly obtained based on the abnormal prompt information displayed on the dialog box, compared with the method of collecting data from the application backend and then analyzing to determine the abnormality, the present application can quickly determine the application abnormality.
[0065] Based on the same inventive concept as the aforementioned embodiments of the present application, the embodiments of the present application also provide a computing device.
[0066] like Figure 6 As shown, the computing device includes: a memory 61 and a processor 62. The memory 61 can be configured to store various other data to support operations on the electronic device. Examples of these data include instructions for any application or method for operating on the electronic device. The memory 61 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.
[0067] The processor 62 is coupled to the memory 61 and is used to execute the computer program stored in the memory 61 to execute the method for monitoring application anomalies described in the above embodiment.
[0068] When the processor 62 executes the computer program to perform the monitoring method for application exceptions, the exception prompt information displayed in the dialog box that directly interacts with the user is obtained, and the number of different exception types appearing on at least one terminal is directly analyzed through the exception prompt information, so as to send an alarm information for the target exception type to the operation and maintenance team based on the number of occurrences of the target exception type. Since the number is directly obtained based on the exception prompt information displayed on the dialog box, compared with the method of collecting data from the application backend and then analyzing to determine the exception, the present application can quickly determine the application exception.
[0069] When the processor 62 executes the computer program in the memory 61, in addition to the above functions, it can also realize other functions, and the details can be found in the description of the previous embodiments.
[0070] Further, such as Figure 6 As shown, the computing device also includes: a display 64, a communication component 63, a power component 65, an audio component 66 and other components. Figure 6 Only some components are shown schematically, and it does not mean that the computing device only includes Figure 6 Components shown.
[0071] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a computer, the methods provided in the above embodiments can be implemented.
[0072] An embodiment of the present application also provides a computer program product, including a computer program, which implements the methods provided in the above embodiments when executed by a processor.
[0073] The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a processor, the methods provided in the above embodiments are implemented.
[0074] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.
[0075] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.
[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims
1. A monitoring method, characterized in that: include: Monitoring whether a dialog box pops up at the front end of a target application on at least one terminal; the dialog box is a dialog box containing abnormal prompt information; When a dialog box pops up, obtain the abnormal prompt information displayed in the dialog box; Count the number of abnormal prompt information used to characterize different abnormal types; When the number of abnormal prompt information of the target abnormal type reaches a preset threshold, an alarm message corresponding to the target abnormal type is issued.
2. The method according to claim 1, characterized in that The method further comprises: If the alarm information is a non-first alarm of the corresponding target exception type, selecting an exception handling suggestion corresponding to the target exception type from a preset dialog configuration library; Returning the exception handling suggestion to the user in the form of a dialog box; The dialog configuration library stores the correspondence between exception types and exception handling suggestions.
3. The method according to claim 2, characterized in that The display content in the dialog box returned to the user is determined based on the exception prompt information and exception handling suggestions.
4. The method according to claim 1, characterized in that The method further comprises: If the alarm information is the first alarm of the corresponding target abnormality type, then: Calculate the similarity between the abnormal prompt information of the target abnormal type and the business problems in the business problem knowledge base to determine at least one business problem similar to the abnormal prompt information; Through the at least one business problem, a business problem knowledge base is searched to determine a solution corresponding to the abnormal prompt information; the business problem knowledge base stores the corresponding relationship between abnormal problems and solutions.
5. The method according to claim 1, characterized in that The method further comprises: Collecting the associated data of the pop-up dialog box, wherein the associated data is used to assist in analyzing the abnormal prompt information to form an alarm message containing a qualifier; the qualifier is determined based on a statistical analysis of the associated data; The associated data at least includes the pop-up time of the dialog box, the user information corresponding to the pop-up of the dialog box, and the geographical location information of the terminal where the dialog box is popped up.
6. The method according to claim 5, characterized in that The method further comprises: The acquired abnormal prompt information and the associated data are saved in a log file type.
7. A monitoring device, characterized in that: include: A real-time monitoring unit, used to monitor whether a dialog box pops up at the front end of a target application on at least one terminal; the dialog box is a dialog box containing abnormal prompt information; A data acquisition unit, used to obtain abnormal prompt information displayed in a dialog box when a dialog box pops up; A data analysis unit, used to count the number of abnormal prompt information used to characterize different abnormal types; The alarm unit is used to issue an alarm message corresponding to the target abnormal type when the number of abnormal prompt messages of the target abnormal type reaches a preset threshold.
8. A computing device, characterized in that include: Memory and processor, wherein: The memory is used to store computer programs; The processor is coupled to the memory and is used to execute the computer program stored in the memory to execute the method described in any one of claims 1 to 6.
9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a computer, it can implement the method described in any one of claims 1 to 6.
10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.