Method for determining influence range of abnormal page and related device

Abstract

The invention provides a method for determining the influence range of an abnormal page and a related device, and relates to the field of data processing of the abnormal page. Responding to the abnormal data processing request, acquiring abnormal operation data of the target page, acquiring a statistical mode of the abnormal operation data, performing statistical analysis operation on the corresponding abnormal operation data by utilizing the statistical mode, determining an abnormal influence range of the target page, and determining the abnormal influence range of the target page by utilizing the abnormal page view in the abnormal operation data. And determining an exception level of the target page, and obtaining an exception range determination result according to the exception influence range and the exception level. According to the method and the device, when the H5 page is abnormal, the abnormal influence of the H5 page can be analyzed, so that the influence range of the abnormal H5 page can be determined, and the influence of the abnormal H5 page on a user is determined. In addition, the abnormal range determination result in the application comprises the abnormal level, the corresponding abnormal processing operation can be executed according to the abnormal level, and the timeliness and reliability of abnormal processing are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing of abnormal pages, and more specifically, to a method for determining the impact range of abnormal pages and related devices. Background Art

[0002] With the development of internet technology, HTML5 pages are widely used in various business scenarios, such as financial services, online e-commerce, and social entertainment. HTML5 pages often reference server-side interfaces and basic SDKs (Software Development Kits) to implement complex business logic. Any exceptions in the interface or SDK code can directly affect HTML5 page performance, thereby impacting the normal access and use of a large number of users.

[0003] Currently, when an H5 page anomaly occurs, it is urgent to determine the impact scope of the H5 page anomaly to determine the impact of the anomaly on users. Therefore, how to determine the impact scope of the H5 page anomaly is a technical problem that those skilled in the art urgently need to solve. Summary of the Invention

[0004] In view of this, the present application provides a method and related device for determining the impact range of abnormal pages to solve the problem of urgently needing to determine the impact range of H5 page abnormalities.

[0005] To solve the above technical problems, this application adopts the following technical solutions:

[0006] In a first aspect, the present application discloses a method for determining the impact range of an abnormal page, comprising:

[0007] Respond to abnormal data processing requests and obtain abnormal operation data of the target page;

[0008] Obtaining a pre-configured statistical method for the abnormal operation data, performing a statistical analysis operation on the corresponding abnormal operation data using the statistical method, and determining the abnormal impact range of the target page; the abnormal impact range is the range of preset objects affected by the abnormality when the target page has an abnormality;

[0009] Determine the abnormality level of the target page using the abnormal page access volume in the abnormal operation data; the abnormality level represents the severity of the abnormality of the target page;

[0010] An abnormal range determination result is obtained according to the abnormal impact range and the abnormal level.

[0011] Optionally, performing a statistical analysis operation on the corresponding abnormal operation data using the statistical method to determine the abnormal impact range of the target page includes:

[0012] In a case where the abnormal operation data includes abnormal interface call data and the statistical method includes an interface statistical method, obtaining an interface address in the abnormal interface call data;

[0013] Based on the data of each interface address in the interface abnormal call data, the abnormal impact range of each abnormal interface is counted.

[0014] Optionally, performing a statistical analysis operation on the corresponding abnormal operation data using the statistical method to determine the abnormal impact range of the target page includes:

[0015] In a case where the abnormal operation data includes code abnormality data and the statistical method includes a hash value dimension, using a specified abnormality analysis algorithm to analyze the code abnormality data to obtain a hash value;

[0016] The Hash values are used to perform clustering operations to obtain the abnormal impact range of each abnormal code.

[0017] Optionally, determining the abnormality level of the target page by using the abnormal page access volume in the abnormal operation data includes:

[0018] Obtaining the total number of visits to the target page;

[0019] The abnormal level corresponding to the target page is determined by using the abnormal page access volume in the abnormal operation data and the total access volume of the target page.

[0020] Optionally, determining the abnormality level corresponding to the target page by using the abnormal page access volume in the abnormal operation data and the total access volume of the target page includes:

[0021] Calculating a ratio of the abnormal page visits in the abnormal operation data to the total number of visits to the target page;

[0022] The abnormality level of the target page is determined by using the pre-configured correspondence between the ratio and the abnormality level.

[0023] Optionally, after obtaining the abnormal range determination result according to the abnormal impact range and the abnormal level, the method further includes:

[0024] Inputting multiple abnormal range determination results obtained from historical statistics into a first model, so as to use the first model to perform an interface abnormality prediction operation on the target page and determine interfaces with abnormality risks;

[0025] And / or, multiple abnormal range determination results obtained through historical statistics are input into a second model, so as to use the second model to perform a code abnormality prediction operation on the target page and determine the code with abnormality risk.

[0026] A second aspect of the present application discloses a device for determining the impact range of an abnormal page, comprising:

[0027] The data acquisition module is used to respond to the abnormal data processing request and obtain the abnormal operation data of the target page;

[0028] a range determination module, configured to obtain a pre-configured statistical method for the abnormal operation data, perform statistical analysis on the corresponding abnormal operation data using the statistical method, and determine the abnormal impact range of the target page; the abnormal impact range is the range of preset objects affected by the abnormality when the target page has an abnormality;

[0029] A level determination module, configured to determine an abnormality level of the target page using the abnormal page access volume in the abnormal operation data; the abnormality level represents the severity of the abnormality of the target page;

[0030] The result determination module is used to obtain an abnormal range determination result according to the abnormal impact range and the abnormal level.

[0031] Optionally, the range determination module includes:

[0032] An address acquisition submodule, configured to, when the abnormal operation data includes abnormal interface call data and the statistical method includes an interface statistical method, acquire an interface address in the abnormal interface call data;

[0033] The statistics submodule is used to count the abnormal impact range of each abnormal interface based on the data of each interface address in the interface abnormal call data.

[0034] A third aspect of the present application discloses an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0035] The memory is used to store computer programs;

[0036] The processor is configured to execute the computer program so that the electronic device can implement the above-mentioned method for determining the impact range of the abnormal page.

[0037] In a fourth aspect, the present application discloses a computer storage medium carrying one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement the above-mentioned method for determining the impact range of an abnormal page.

[0038] The present application provides a method for determining the impact range of an abnormal page and a related device. In the present application, in response to an abnormal data processing request, the abnormal operation data of the target page is obtained, a pre-configured statistical method of the abnormal operation data is obtained, and the corresponding abnormal operation data is statistically analyzed using the statistical method to determine the abnormal impact range of the target page. The abnormal page visits in the abnormal operation data are used to determine the abnormal level of the target page, and the abnormal range determination result is obtained based on the abnormal impact range and the abnormal level. In the present application, when an abnormality occurs on the H5 page, the abnormal impact of the H5 page can be analyzed, so that the impact range of the H5 page abnormality can be determined, and then the impact of the abnormality on the user can be determined. In addition, the abnormal range determination result in the present application includes the abnormal level, and the corresponding abnormal processing operation can be performed according to the abnormal level, thereby improving the timeliness and reliability of the abnormal processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0040] Figure 1 A flowchart of a method for determining the impact range of an abnormal page provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of the abnormal information reporting process provided in an embodiment of the present application;

[0042] Figure 3 A flow chart of a method for determining the impact range of an abnormality provided in an embodiment of the present application;

[0043] Figure 4 A flow chart of another method for determining the impact range of an abnormality provided in an embodiment of the present application;

[0044] Figure 5 A schematic diagram of a fault analysis process provided in an embodiment of the present application;

[0045] Figure 6 A schematic diagram of the structure of a device for determining the impact range of an abnormal page provided in an embodiment of the present application;

[0046] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0048] With the development of internet technology, web applications are becoming increasingly widespread. For example, H5 pages (webpages or web applications based on HTML5) are widely used in various business scenarios, such as financial services, online e-commerce, and social entertainment. H5 pages often reference server-side interfaces and basic SDKs to implement complex business logic. Any exception in the interface or SDK code can directly affect H5 page errors, thereby affecting the normal access and use of a large number of users.

[0049] Currently, taking an H5 page as an example, when an H5 page anomaly occurs, it is urgent to determine the impact scope of the H5 page anomaly to determine the impact of the anomaly on users. Therefore, how to determine the impact scope of the H5 page anomaly is a technical problem that technicians in this field urgently need to solve.

[0050] In related technologies, the impact scope analysis of H5 page anomalies usually relies on manual investigation, which is not only time-consuming and labor-intensive, but also difficult to quickly and accurately assess the actual impact scope of the problem.

[0051] To this end, an embodiment of the present application provides a method and related device for determining the impact range of an abnormal page. In the present application, in response to an abnormal data processing request, the abnormal operation data of the target page is obtained, a statistical method for obtaining pre-configured abnormal operation data is obtained, and a statistical analysis operation is performed on the corresponding abnormal operation data using the statistical method to determine the abnormal impact range of the target page. The abnormal page visits in the abnormal operation data are used to determine the abnormal level of the target page, and the abnormal range determination result is obtained based on the abnormal impact range and the abnormal level. In the present application, when an abnormality occurs on the H5 page, the abnormal impact of the H5 page can be analyzed, so that the impact range of the H5 page abnormality can be determined, and then the impact of the abnormality on the user can be determined. In addition, the abnormal range determination result in the present application includes the abnormal level, and the corresponding abnormal processing operation can be performed according to the abnormal level, thereby improving the timeliness and reliability of the abnormal processing.

[0052] In addition, in the embodiment of the present application, the abnormal operation data includes interface abnormal call data and / or code abnormal data, which can perform abnormal analysis from two levels: interface call and code, thereby improving the comprehensiveness of the abnormal analysis.

[0053] Based on the above, an embodiment of the present application provides a method for determining the impact range of an abnormal page. The execution subject can be a device with specific computing capabilities such as a server. Among them, the abnormal page in this embodiment refers to a page that has an abnormality (such as being unable to access normally, etc.), which can be a web page, etc.

[0054] Reference Figure 1 A method for determining the impact range of an abnormal page may include:

[0055] S11. Respond to the abnormal data processing request and obtain abnormal operation data of the target page.

[0056] In one implementation, the target page refers to a page that may have anomalies. It can be any type of page or a page in various scenarios. In this embodiment, the content of the target page is not limited. The subsequent embodiments will illustrate the target page as an H5 page.

[0057] In one implementation, the abnormal operation data includes interface abnormal call data and / or code abnormality data.

[0058] In real-world scenarios, HTML5 pages often reference server-side interfaces and underlying SDKs. Any exceptions in the interface or SDK code can directly impact HTML5 page performance, disrupting access and usage for a large number of users. HTML5 page anomalies can be caused by interface anomalies, underlying SDK anomalies, or both. Therefore, when analyzing HTML5 page anomalies, it's important to analyze the impact of the anomaly from the perspective of the interface and / or underlying SDK code. The underlying SDK code generally refers to the underlying library code.

[0059] If a user needs to analyze abnormal operation, they can submit an exception data processing request. For example, a backend maintenance staff member who wants to analyze the impact of a current interface abnormality can submit an exception data processing request.

[0060] When a user submits a request for abnormal data processing, they can also specify the scope of analysis to be performed. In this embodiment, there are two ways to implement the scope of analysis: one is the interface, and the other is the code. When actually specifying the scope, there are three implementations: one is to analyze the impact scope of only the interface, one is to analyze the impact scope of only the code, and one is to analyze the impact scope of both the interface and the code.

[0061] If only the impact of interfaces is analyzed, the H5 page abnormal operation data obtained at this time includes interface abnormal call data. If only the impact of code is analyzed, the H5 page abnormal operation data obtained at this time includes code abnormal data. If the impact of both interfaces and code is analyzed at this time, the H5 page abnormal operation data obtained at this time includes both interface abnormal call data and code abnormal data.

[0062] In one implementation of the present application, the interface abnormal call data refers to some interface call data when the interface call is abnormal. In one implementation, the interface abnormal call data is obtained by the client by intercepting the preset interface of the H5 page.

[0063] Specifically, on the client side, AOP (Aspect Oriented Programming) technology can be used to intercept the sending and response of the XMLHttpRequest interface in the H5 page. The intercepted information includes the complete H5 request information and the server response information. Among them, the H5 request information refers to the information requested by the client to the server, and the server response information refers to the information fed back to the client by the server in response to the client's H5 request information. The intercepted information can be used to obtain interface exception call data. In one implementation method, the interface exception call data may include:

[0064] Interface address, interface path, request method (GET / POST, etc.), request parameters, response status code, application name, calling source page URL, timestamp, etc.

[0065] After the client completes the interface interception operation and obtains the above interface abnormal call data, the interface abnormal call data can be sent to the server. In one embodiment, in order to avoid the problem of heavy client workload caused by data uploading, the interface abnormal call data can be uploaded to the server through asynchronous operation.

[0066] In another implementation, other methods may be used to obtain interface exception call data, such as through log analysis, etc. The specific implementation process of obtaining interface exception call data is not limited.

[0067] In actual scenarios, if an H5 page exception occurs on the client, the H5 page's interface call exception data will be uploaded to the server using the above method. After receiving the interface call exception data, the server can store it in a database, such as ClickHouse, so that when the impact of the interface exception needs to be analyzed later, the data in the database can be called to perform analysis operations on the impact of the interface exception and count the number of affected pages and users.

[0068] In one implementation of the present application, the code exception data is obtained by the client by registering for global events.

[0069] Registering a global event can be done through window.addEventListener. Specifically, the browser event listening mechanism is used to register a global event through window.addEventListener to implement exception capture. When an HTML5 page exception is caused by a code exception, the HTML5 page exception may be caused only by a synchronous error event, only by an asynchronous error event, or both. Therefore, it is necessary to capture both synchronous and asynchronous error events. The captured exception events include the following:

[0070] 1. Synchronous error events, such as the error event. When capturing an error event, you can capture JavaScript syntax errors or runtime errors and extract exception information from the captured error information. This includes the exception type (syntax error, runtime error, asynchronous Promise exception), exception subtype (such as network exception, resource loading error), file path, line number, column number, error stack trace, application name, and more. This extracted information is the code exception data.

[0071] 2. Asynchronous error events, such as the unhandledrejection event. When capturing the unhandledrejection event, you can capture the unhandled Promise exception and extract the required information from the Promise exception, such as the exception type (syntax error, runtime error, Promise exception), exception subtype (such as network exception, resource loading error), file path, line number, column number, error stacktrace, application name, etc. This extracted information is the code exception data.

[0072] After obtaining the code exception data, the code exception data can be reported to the server in JSON (JavaScript Object Notation) format, and the server stores the code exception data in the ClickHouse database.

[0073] In one implementation, the abnormal operation data acquisition and reporting can be achieved by referring to Figure 2 As shown, Figure 2 In the above description, capturing the base library code exception refers to capturing the above code exception data, specifically capturing the base library code exception data. Capturing interface information refers to capturing the above interface exception call data.

[0074] After storing the interface exception call data and the code exception data in the database, the interface exception call data stored in the database can be directly obtained later, and / or the code exception data stored in the database can be obtained. The specific data obtained is related to the content of the user's exception data processing request.

[0075] S12: Obtain a pre-configured statistical method for abnormal operation data, perform statistical analysis on the corresponding abnormal operation data using the statistical method, and determine the abnormal impact range of the target page.

[0076] The abnormality impact range is the range of preset objects affected by the abnormality when the target page has an abnormality.

[0077] In one embodiment, the preset object refers to an object that will be affected when an abnormality occurs in the target page. In one implementation, the preset object may include at least a page and a user.

[0078] For abnormal H5 pages, if the page is inaccessible, some users will not be able to access the H5 page normally. Therefore, if an H5 page has an abnormality, users will be affected. The degree of user impact can be reflected by the number of users. Similarly, for abnormal H5 pages, if the page is inaccessible, some pages will not be displayed normally. Therefore, if an H5 page has an abnormality, other pages will be affected. The degree of impact can be reflected by the number of pages.

[0079] The number of pages affected by the anomaly refers to the number of pages that cannot be displayed normally due to the H5 page anomaly. The number of users affected by the anomaly refers to the number of users who cannot use the H5 page normally due to the anomaly. In actual scenarios, for a user, if the corresponding display interface displays an error when the user operates a terminal such as a mobile phone, the user may initiate a request again. In this case, the user requests the same H5 page twice. In this case, the user is one, but the number of users is two. That is, the number of users is considered to increase by one with each request.

[0080] In actual scenarios, different abnormal operation data corresponds to different statistical methods. Statistical methods refer to the specific methods and tools used in data collection, organization, analysis, and interpretation.

[0081] In one implementation, when the abnormal operation data includes interface abnormality call data, the H5 page abnormality is caused by the interface abnormality, so the abnormal impact analysis can be performed using the interface statistical method, in which case the statistical method includes the interface statistical method. When the abnormal operation data includes code abnormality data, due to the high complexity of directly analyzing the code, the hash value of the code can be analyzed, and the statistical method can include the hash value dimension. In one embodiment, the hash value can be calculated using the MD5 (Message Digest Algorithm 5) algorithm.

[0082] After determining the statistical method corresponding to the abnormal operation data, the statistical method is used to perform statistical analysis on the corresponding abnormal operation data to determine the abnormal impact range of the target page. The abnormal impact range includes the number of pages and users affected by the abnormality.

[0083] S13. Determine the abnormality level of the target page using the abnormal page access volume in the abnormal operation data.

[0084] The abnormality level represents the severity of the abnormality of the target page. In one implementation, the abnormality level can be configured as multiple levels such as high, medium, and low, or multiple levels such as 1, 2, 3, etc. according to needs.

[0085] In one implementation, the exception level is primarily determined based on the exception ratio. For example, for interface calls, the exception ratio refers to the ratio of the number of exception calls to the total number of interface calls. For example, if interface A is called 10,000 times and there are 5 exceptions, the exception ratio is 5 / 10,000, and the exception level is determined based on this exception ratio. The same process is applied to code.

[0086] S14. Obtain an abnormal range determination result based on the abnormal impact range and the abnormal level. In this embodiment, the abnormal impact range and the abnormal level can be directly summarized to obtain the abnormal range determination result.

[0087] In actual scenarios, the abnormal impact range and abnormal level can be standardized, such as converted into standardized data, and then the abnormal range determination result can be obtained. Subsequently, an evaluation report can be generated based on the abnormal range determination result to intuitively display the abnormal impact range.

[0088] In this embodiment, in response to an abnormal data processing request, the abnormal operation data of the target page is obtained, a statistical method for obtaining pre-configured abnormal operation data is obtained, and a statistical analysis operation is performed on the corresponding abnormal operation data using the statistical method to determine the abnormal impact range of the target page. The abnormal page visits in the abnormal operation data are used to determine the abnormal level of the target page, and the abnormal range determination result is obtained based on the abnormal impact range and the abnormal level. In this application, when an abnormality occurs on the H5 page, the abnormal impact of the H5 page can be analyzed, so that the impact range of the H5 page abnormality can be determined, and then the impact of the abnormality on the user can be determined. In addition, the abnormal range determination result in this application includes the abnormal level, and the corresponding abnormal processing operation can be performed according to the abnormal level, thereby improving the timeliness and reliability of the abnormal processing.

[0089] In addition, in the present application, abnormal operation data includes interface abnormal call data and / or code abnormal data, which can perform abnormal analysis from two levels: interface call and code, thereby improving the comprehensiveness of abnormal analysis.

[0090] Taking the target page as an H5 page as an example, when the impact range determination method of the abnormal page in the embodiment of this application is applied to the company's production environment, the impact range of the H5 page interface abnormality or code failure can be efficiently evaluated. Compared with the traditional troubleshooting method, the impact range analysis of a single page failure can save an average of about 3 hours of positioning time, greatly improving the availability of the business system. At the same time, this application provides comprehensive data support for the technical team, helping to optimize system design and continuously improve exception handling strategies.

[0091] In another implementation of the present application, when the abnormal operation data is different, the statistical method corresponding to the abnormal operation data is different, and therefore, the process of determining the abnormal impact range is also different. Therefore, in the embodiment of the present application, different methods are used to determine the abnormal impact range for interface abnormal call data and code abnormal data, respectively.

[0092] Specifically, when the abnormal operation data includes abnormal interface call data and the statistical method includes interface statistical method, refer to Figure 3 , using statistical methods to perform statistical analysis on the corresponding abnormal operation data to determine the abnormal impact range of the target page, which may include:

[0093] S21. Obtain the interface address in the interface exception call data.

[0094] Specifically, for different interfaces, the corresponding interface addresses are different, that is, an interface can be uniquely identified by the interface address. Therefore, in this embodiment, different interfaces can be distinguished by the interface address. At this time, the interface address in the interface exception call data can be obtained, and subsequent processing can be performed based on the interface address.

[0095] S22. Based on the data of each interface address in the interface abnormal call data, count the abnormal impact range of each abnormal interface.

[0096] The impact range of the exception includes the number of pages and users affected by the exception. For the specific implementation process, please refer to the corresponding instructions above.

[0097] In real-world scenarios, a single H5 page may need to call multiple interfaces, and multiple interfaces may experience exceptions simultaneously. Therefore, in this embodiment, we need to group them by interface and count the number of pages and users affected by the exception for each interface. Since one page corresponds to one application, counting the number of pages also counts the number of applications, meaning we can perform statistics from both the application and user dimensions.

[0098] Specifically, we use Node.js to interact with the ClickHouse database and query the interface abnormal call data in the ClickHouse database through SQL (Structured Query Language). The obtained interface abnormal call data can be grouped according to the interface address, and the data belonging to the same interface address can be grouped together to analyze the group data and count the number of pages and users that reference the interface.

[0099] For example, consider a user information interface. This interface is referenced by the homepage, finance page, and shopping page to obtain user information. Therefore, if the user information interface fails, the homepage, finance page, shopping page, and other pages will fail. Therefore, the homepage, finance page, shopping page, and other pages are affected. Counting the number of pages affected by the failure is the number of pages affected by the failure.

[0100] Since one page corresponds to a unique interface address, the interface exception call data can be grouped according to the interface address to obtain grouping results of different interface addresses. For each grouping result, all affected interface addresses can be obtained through statistics of the interface addresses, and then the affected pages can be obtained based on the correspondence between the interface addresses and the pages, and then the number of affected pages can be obtained.

[0101] Regarding the number of users, one visit is considered to be the existence of one user, and the number of users can be obtained by counting the number of visits.

[0102] In this embodiment, the impact range of the interface that causes the H5 page to be abnormal is analyzed to obtain the impact range of the interface. In order to prevent the impact range from continuing to expand, the interface problem needs to be repaired as soon as possible to ensure normal access to the H5 page.

[0103] In the case where the abnormal operation data includes code abnormality data and the statistical method includes the hash value dimension, refer to Figure 4 , using statistical methods to perform statistical analysis on the corresponding abnormal operation data to determine the abnormal impact range of the target page, which may include:

[0104] S31. Analyze the code exception data using a specified exception analysis algorithm to obtain a hash value.

[0105] Specifically, the specified exception analysis algorithm may be any algorithm used for exception analysis, such as an exception summary analysis method. The exception summary analysis method will be taken as an example for explanation.

[0106] The exception summary analysis method is the MD5 algorithm. Using the MD5 algorithm to analyze code exception data can be:

[0107] Use the MD5 algorithm to generate an exception summary. Specifically, the exception type (syntax error, runtime error, asynchronous Promise exception), exception subtype, file path, line number, column number, error stack, application name and other fields are digested to generate an exception hash value, which is a unique identifier.

[0108] Therefore, for each code anomaly data accessed by a user, an anomaly hash value of the code anomaly data can be calculated, and the anomaly hash value is the hash value in the embodiment of the present application.

[0109] S32. Perform clustering operations using hash values to obtain the abnormal impact range of each abnormal code.

[0110] The impact range of the anomaly includes the number of pages and users affected by the anomaly. For specific explanations, please refer to the above corresponding instructions.

[0111] Specifically, for basic library codes, different basic library codes correspond to different abnormal hash values. Therefore, we can group them by abnormal hash values and count the number of pages and users that reference abnormal basic library codes.

[0112] During specific grouping, the code anomaly data with the same anomaly hash value are clustered together, and then the number of pages and users corresponding to the interface address are determined through the interface address in the code anomaly data. The specific implementation process refers to the corresponding instructions above.

[0113] In this embodiment, different statistical methods are used to count the abnormal impact of code exception data and interface exception call data, so as to obtain the corresponding abnormal impact range. The whole process is automatically implemented, which can avoid the problems of low accuracy and low efficiency caused by manual statistics.

[0114] In addition, in this application, the abnormal impact analysis is performed from both the code and interface perspectives, which can avoid the situation where some monitoring systems can only perform data analysis from the interface or code perspective due to different developers, and are unable to comprehensively evaluate the impact scope of code and interface when code and interface abnormalities exist simultaneously in complex systems.

[0115] After obtaining the abnormal impact range through the above steps, the abnormal level can be determined. The implementation process of determining the abnormal impact range and the abnormal level can be performed simultaneously or successively. The embodiment of the present application does not limit the execution order of the steps.

[0116] Specifically, the steps include:

[0117] 1) Get the total number of visits to the target page.

[0118] The total number of visits to the target page can be pre-counted in the form of data access key information and stored in a database.

[0119] In one implementation, the key data access information may include the total number of visits to the target page. The total number of visits to the target page specifically includes:

[0120] The total number of interface calls and user accesses.

[0121] The total number of interface calls refers to the total number of times the interface is called. The server may add one to the number of interface calls each time the interface is called, thereby obtaining the total number of interface calls.

[0122] The total number of user visits refers to the total number of times the basic library code is accessed. The server can add one to the number of code calls each time the code is called to obtain the total number of user visits.

[0123] 2) Using the abnormal page visits in the abnormal operation data and the total number of visits to the target page, determine the abnormal level corresponding to the target page.

[0124] In one implementation, the ratio of abnormal page visits in the abnormal operation data to the total number of visits to the target page can be calculated, and then the abnormality level of the target page can be determined using a pre-configured correspondence between the ratio and the abnormality level.

[0125] In this embodiment, different methods are used to obtain the exception level for the interface and the code, which are now introduced respectively.

[0126] When the abnormal operation data is interface abnormal call data, the interface abnormal call data is statistically analyzed to obtain the page abnormal visits. When the interface abnormality causes the H5 page abnormality, the page abnormal visits specifically refer to the interface abnormal call volume. Then, the ratio of the interface abnormal call volume to the total interface call volume is calculated.

[0127] Among them, the interface abnormal call volume is determined based on the interface abnormal call data. In actual scenarios, since the client uploads the interface abnormal call data once every time an interface abnormal call is made, the total amount of interface abnormal call data can be counted, and this total amount is the interface abnormal call volume.

[0128] In one implementation, different ratios can be preconfigured to correspond to anomaly levels. For example, a ratio of 0.1-0.3 indicates a low anomaly level, 0.3-0.8 indicates a medium anomaly level, and 0.8-1 indicates a high anomaly level. A higher anomaly level indicates a more severe impact. After obtaining the ratio of the number of abnormal interface calls to the total number of interface calls, the anomaly level can be obtained by querying the correspondence between the ratio and the anomaly level.

[0129] When the abnormal operation data is code abnormal data, the ratio of the abnormal page visits to the total number of user visits is calculated to obtain the abnormal level.

[0130] Among them, the page abnormal visits are determined based on the code abnormality data. In actual scenarios, since the client uploads the code abnormality data once every time a code abnormality is called, the total amount of code abnormality data can be counted, and this total amount is the page abnormal visits.

[0131] After determining the number of abnormal page visits and the total number of user visits, calculate the ratio of the number of abnormal page visits to the total number of user visits.

[0132] In one implementation, different ratios and anomaly levels can be preconfigured, such as a ratio of 0.1-0.3 for a low anomaly level, 0.3-0.8 for a medium anomaly level, and 0.8-1 for a high anomaly level. After obtaining the ratio of abnormal page views to the total number of user visits, the anomaly level can be obtained by querying the corresponding relationship between the ratio and the anomaly level.

[0133] After obtaining the abnormality level, the abnormality impact range and abnormality level can be standardized to obtain standardized data, integrate the results of the impact range of interface failures and code failures, and generate a detailed fault impact analysis report, including: a list of affected pages (sorted by importance), the number and proportion of affected users (divided by business impact), fault type and detailed information (interface abnormality, code abnormality). For specific implementation, refer to Figure 5 shown.

[0134] In this embodiment, by determining the abnormality level based on the abnormality ratio, the severity of the abnormality can be obtained, and maintenance operations of corresponding levels are performed according to different severities.

[0135] In summary, the present embodiment intercepts XMLHttpRequest to collect interface exception call data in real time, obtaining interface call relationships, thereby quickly locating the pages and user ranges affected by interface failures, improving problem location efficiency. Furthermore, by uniformly identifying and aggregating code exception data using exception digests (MD5 algorithm), code failure impact assessment can be implemented across application scenarios, making it particularly suitable for infrastructure exception scenarios.

[0136] Due to the automated and refined fault impact assessment mechanism in this application, anomalies are analyzed from both the interface and code perspectives. The analysis is comprehensive, providing a global perspective of fault impact analysis and achieving decision optimization.

[0137] In another implementation, multiple abnormal range determination results obtained through historical statistics may be used to perform abnormality prediction operations to determine interfaces and / or codes with abnormality risks.

[0138] In one implementation, multiple abnormal range determination results obtained through historical statistics are input into a first model, so as to use the first model to perform an interface abnormality prediction operation on a target page and determine interfaces with abnormality risks.

[0139] Specifically, for different interfaces, the model training can be performed using the results of multiple abnormal range determinations of the interface. The model in this embodiment can be called a first model. The first model can be a neural network model or a machine learning model, or it can be another type of model. The first model can be used to predict abnormalities later. In specific implementation, the abnormal range determination results of an interface are input into the first model, and the first model is used to analyze the abnormal risk of the interface and the potential fault impact range in the future. When the abnormal risk and potential fault impact range are high, an abnormal prompt message is output to perform interface maintenance in a timely manner to avoid future abnormal risks.

[0140] In one implementation, multiple abnormal range determination results obtained through historical statistics are input into a second model, so as to use the second model to perform a code abnormality prediction operation on a target page and determine the code with abnormality risks.

[0141] Similarly, for different codes, models can also be constructed and corresponding prediction operations can be performed. In specific implementation, for different codes, multiple abnormal range determination results of the code can be used to perform model training. The model in this embodiment can be called a second model. The second model can be a neural network model or a machine learning model, or it can be other types of models. The second model can be used to perform abnormal predictions later. In specific implementation, the abnormal range determination result of a code is input into the second model, and the second model is used to analyze the abnormal risk of the code in the future and the potential fault impact range. When the abnormal risk and potential fault impact range are high, abnormal prompt information is output, and code maintenance is performed in time to avoid future abnormal risks. For specific implementation, refer to the corresponding instructions above.

[0142] Based on the embodiment of the method for determining the influence range of abnormal pages, another embodiment of the present application provides a device for determining the influence range of normal pages. Figure 6 , which may include:

[0143] The data acquisition module 11 is used to respond to the abnormal data processing request and obtain the abnormal operation data of the target page;

[0144] The scope determination module 12 is configured to obtain a pre-configured statistical method for abnormal operation data, perform statistical analysis on the corresponding abnormal operation data using the statistical method, and determine the abnormal impact range of the target page; the abnormal impact range is the range of preset objects affected by the abnormality when the target page has an abnormality;

[0145] The level determination module 13 is used to determine the abnormal level of the target page using the abnormal page access volume in the abnormal operation data; the abnormal level represents the severity of the abnormality of the target page;

[0146] The result determination module 14 is configured to obtain an abnormal range determination result based on the abnormal impact range and the abnormal level.

[0147] In one implementation, the range determination module 12 includes:

[0148] An address acquisition submodule, configured to acquire an interface address from the interface abnormal call data when the abnormal operation data includes interface abnormal call data and the statistical method includes an interface statistical method;

[0149] The statistics submodule is used to count the abnormal impact range of each abnormal interface based on the data of each interface address in the interface abnormal call data.

[0150] In one implementation, the range determination module 12 includes:

[0151] A hash calculation submodule is used to analyze the code abnormality data using a specified abnormality analysis algorithm to obtain a hash value when the abnormal operation data includes code abnormality data and the statistical method includes a hash value dimension;

[0152] The clustering submodule is used to perform clustering operations using hash values to obtain the abnormal impact range of each abnormal code.

[0153] In one implementation, the level determination module 13 includes:

[0154] The total amount acquisition submodule is used to obtain the total number of visits to the target page;

[0155] The level determination submodule is used to determine the abnormal level corresponding to the target page by using the abnormal page access volume in the abnormal operation data and the total number of accesses to the target page.

[0156] In one implementation, the level determination submodule is specifically configured to:

[0157] The ratio of the abnormal page visits in the abnormal operation data to the total visits of the target page is calculated, and the abnormal level of the target page is determined using the pre-configured correspondence between the ratio and the abnormal level.

[0158] In one implementation, the apparatus for determining the impact range of an abnormal page further includes:

[0159] A first prediction module is configured to input a plurality of abnormal range determination results obtained through historical statistics into a first model, so as to use the first model to perform an interface abnormality prediction operation on a target page and determine interfaces with abnormality risks;

[0160] The second prediction module is used to input multiple abnormal range determination results obtained from historical statistics into the second model, so as to use the second model to perform a code abnormality prediction operation on the target page and determine the code with abnormality risks.

[0161] In this embodiment, in response to an abnormal data processing request, the abnormal operation data of the target page is obtained, a statistical method for obtaining pre-configured abnormal operation data is obtained, and a statistical analysis operation is performed on the corresponding abnormal operation data using the statistical method to determine the abnormal impact range of the target page. The abnormal page visits in the abnormal operation data are used to determine the abnormal level of the target page, and the abnormal range determination result is obtained based on the abnormal impact range and the abnormal level. In this application, when an abnormality occurs on the H5 page, the abnormal impact of the H5 page can be analyzed, so that the impact range of the H5 page abnormality can be determined, and then the impact of the abnormality on the user can be determined. In addition, the abnormal range determination result in this application includes the abnormal level, and the corresponding abnormal processing operation can be performed according to the abnormal level, thereby improving the timeliness and reliability of the abnormal processing.

[0162] It should be noted that, for the working process of each module, sub-module and unit in this embodiment, please refer to the corresponding description in the above embodiment, which will not be repeated here.

[0163] An embodiment of the present application further provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0164] Memory is used to store computer programs;

[0165] The processor is used to execute the computer program so that the electronic device can implement the above-mentioned method for determining the impact range of the abnormal page.

[0166] refer to Figure 7 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic device in the embodiments of the present application may include, but is not limited to, fixed terminals such as a server, a mobile phone, a notebook computer, a PDA (personal digital assistant), a PAD (tablet computer), a desktop computer, and the like. Figure 7 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0167] like Figure 7 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. When the electronic device is powered on, the RAM 603 also stores various programs and data required for the operation of the electronic device. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0168] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a memory card, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0169] An embodiment of the present application also provides a computer program product including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements any of the methods for determining the impact range of abnormal pages provided in the embodiments of the present application.

[0170] A computer-readable storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any of the methods for determining the impact range of abnormal pages provided in the embodiments of the present application.

[0171] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining the impact range of an abnormal page, characterized in that: include: Respond to abnormal data processing requests and obtain abnormal operation data of the target page; Obtaining a pre-configured statistical method for the abnormal operation data, performing a statistical analysis operation on the corresponding abnormal operation data using the statistical method, and determining the abnormal impact range of the target page; the abnormal impact range is the range of preset objects affected by the abnormality when the target page has an abnormality; Determine the abnormality level of the target page using the abnormal page access volume in the abnormal operation data; the abnormality level represents the severity of the abnormality of the target page; An abnormal range determination result is obtained according to the abnormal impact range and the abnormal level.

2. The method for determining the impact range of abnormal pages according to claim 1, characterized in that: Performing statistical analysis on the corresponding abnormal operation data using the statistical method to determine the abnormal impact range of the target page includes: In a case where the abnormal operation data includes abnormal interface call data and the statistical method includes an interface statistical method, obtaining an interface address in the abnormal interface call data; Based on the data of each interface address in the interface abnormal call data, the abnormal impact range of each abnormal interface is counted.

3. The method for determining the impact range of abnormal pages according to claim 1, characterized in that: Performing statistical analysis on the corresponding abnormal operation data using the statistical method to determine the abnormal impact range of the target page includes: In a case where the abnormal operation data includes code abnormality data and the statistical method includes a hash value dimension, using a specified abnormality analysis algorithm to analyze the code abnormality data to obtain a hash value; The Hash values are used to perform clustering operations to obtain the abnormal impact range of each abnormal code.

4. The method for determining the impact range of an abnormal page according to claim 1, characterized in that: Determining the abnormality level of the target page by using the abnormal page access volume in the abnormal operation data includes: Obtaining the total number of visits to the target page; The abnormal level corresponding to the target page is determined by using the abnormal page access volume in the abnormal operation data and the total access volume of the target page.

5. The method for determining the impact range of an abnormal page according to claim 4, characterized in that: Determining the abnormality level corresponding to the target page by using the abnormal page access volume in the abnormal operation data and the total access volume of the target page includes: Calculating a ratio of the abnormal page visits in the abnormal operation data to the total number of visits to the target page; The abnormality level of the target page is determined by using the pre-configured correspondence between the ratio and the abnormality level.

6. The method for determining the impact range of an abnormal page according to claim 1, characterized in that: After obtaining the abnormal range determination result according to the abnormal impact range and the abnormal level, the method further includes: Inputting multiple abnormal range determination results obtained from historical statistics into a first model, so as to use the first model to perform an interface abnormality prediction operation on the target page and determine interfaces with abnormality risks; And / or, multiple abnormal range determination results obtained through historical statistics are input into a second model, so as to use the second model to perform a code abnormality prediction operation on the target page and determine the code with abnormality risk.

7. A device for determining the impact range of an abnormal page, characterized in that: include: The data acquisition module is used to respond to the abnormal data processing request and obtain the abnormal operation data of the target page; a range determination module, configured to obtain a pre-configured statistical method for the abnormal operation data, perform statistical analysis on the corresponding abnormal operation data using the statistical method, and determine the abnormal impact range of the target page; the abnormal impact range is the range of preset objects affected by the abnormality when the target page has an abnormality; A level determination module, configured to determine an abnormality level of the target page using the abnormal page access volume in the abnormal operation data; the abnormality level represents the severity of the abnormality of the target page; The result determination module is used to obtain an abnormal range determination result according to the abnormal impact range and the abnormal level.

8. The device for determining the impact range of an abnormal page according to claim 7, characterized in that: The range determination module includes: An address acquisition submodule, configured to, when the abnormal operation data includes abnormal interface call data and the statistical method includes an interface statistical method, acquire an interface address in the abnormal interface call data; The statistics submodule is used to count the abnormal impact range of each abnormal interface based on the data of each interface address in the interface abnormal call data.

9. An electronic device, characterized in that: comprising at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is configured to execute the computer program so that the electronic device can implement the method for determining the impact range of an abnormal page according to any one of claims 1 to 6.

10. A computer storage medium, characterized in that The storage medium carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, the electronic device can implement the method for determining the impact range of an abnormal page as described in any one of claims 1 to 6.

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