Multi-application maintenance method, device, equipment, medium and program product

Abstract

The invention provides a multi-application maintenance method which can be applied to the technical field of artificial intelligence. The multi-application maintenance method comprises the steps of obtaining an identifier and a plurality of error reporting logs of each target application in a plurality of target applications in the same use environment; analyzing each error log to obtain an error reason and a priority of each error log; determining a maintenance strategy of each error log according to the error reasons; and maintaining the target application corresponding to each error log according to the identifier, the maintenance strategy and the priority. The invention further provides a multi-application maintenance device and equipment, a storage medium and a program product.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, specifically to a multi-application maintenance method, apparatus, device, medium, and program product. Background Technology

[0002] In existing information management scenarios, multiple independent applications are often built. When it is necessary to conduct comprehensive analysis of the usage of each application or troubleshoot production environment issues, it is usually necessary to connect to the backend system of each application separately to obtain log data and conduct offline analysis. This decentralized management model forces administrators to frequently switch between different system interfaces and adapt to the unique log formats and query methods of each application. This not only significantly reduces the efficiency of problem diagnosis and the comprehensiveness of analysis results, but also makes it difficult to have an overall grasp of the operational status of all application systems and predict trends. Summary of the Invention

[0003] In view of the above problems, this application provides a multi-application maintenance method, device, equipment, medium and program product, which can realize centralized storage, unified processing and multi-dimensional analysis of multiple applications in the same usage scenario, thereby improving the efficiency of cross-application operation and maintenance management and the accuracy of problem diagnosis.

[0004] According to the first aspect of this application, a multi-application maintenance method is provided, comprising: obtaining the identifier of each target application and multiple error logs in multiple target applications under the same usage environment; analyzing each error log to obtain the error reason and priority of each error log; determining the maintenance strategy for each error log based on the error reason; and maintaining the target application corresponding to each error log based on the identifier, maintenance strategy and priority.

[0005] According to embodiments of this application, the multiple error logs include front-end error logs and back-end error logs. The front-end error logs are obtained through the Hypertext Transfer Protocol Security (HTTP) interface, and the back-end error logs are obtained through the HTTP interface.

[0006] According to an embodiment of this application, each error log is analyzed to obtain the error cause and priority of each error log, including: performing semantic analysis on each error log using a semantic analysis engine to obtain multiple key fields; evaluating the multiple key fields using a preset priority evaluation algorithm; and determining the priority of each error log based on the evaluation results.

[0007] According to an embodiment of this application, determining the priority of each error log based on the evaluation results includes: sorting multiple key fields of each error log using a natural language processing model to obtain a sorting result; and determining the priority based on the evaluation results and the sorting result.

[0008] According to an embodiment of this application, each error log is analyzed to obtain the error reason and priority of each error log, and the method further includes: inputting multiple key fields of each error log into a natural language processing model to obtain the error reason of each error log.

[0009] According to an embodiment of this application, a maintenance strategy for each error log is determined based on the error cause, including: classifying multiple error causes corresponding to multiple error logs to obtain classification results; and determining a maintenance strategy matching the classification results from a preset maintenance strategy knowledge base.

[0010] According to an embodiment of this application, the target application corresponding to each error log is maintained sequentially according to the identifier, maintenance policy, and priority, including: determining the correspondence between multiple error logs and multiple target applications according to the identifier; and maintaining the target application corresponding to each error log sequentially according to the priority, correspondence, and maintenance policy.

[0011] A second aspect of this application provides a multi-application maintenance device, comprising: an acquisition module for acquiring the identifier of each target application and multiple error logs in multiple target applications under the same usage environment; an analysis module for analyzing each error log to obtain the error cause and priority of each error log; a determination module for determining the maintenance strategy for each error log based on the error cause; and a maintenance module for maintaining the target application corresponding to each error log based on the identifier, maintenance strategy, and priority.

[0012] A third aspect of this application provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0013] A fourth aspect of this application also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0014] The fifth aspect of this application also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method. Attached Figure Description

[0015] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0016] Figure 1The illustrations depict application scenarios of multi-application maintenance methods, apparatuses, devices, media, and program products according to embodiments of this application.

[0017] Figure 2 A flowchart illustrating a multi-application maintenance method according to an embodiment of this application is shown schematically;

[0018] Figure 3 This illustration schematically shows a flowchart of obtaining front-end and back-end error logs according to an embodiment of this application;

[0019] Figure 4 A schematic diagram illustrating the structure of a multi-application maintenance device according to embodiments of this application is shown; and

[0020] Figure 5 A block diagram schematically illustrates an electronic device suitable for implementing a multi-application maintenance method according to an embodiment of this application. Detailed Implementation

[0021] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0022] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0023] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0024] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0025] The embodiments of this application provide a multi-application maintenance method that unifies and analyzes multiple error logs from multiple applications, significantly improving the efficiency of cross-application operation and maintenance management and the accuracy of problem diagnosis. It can solve the problem of low error analysis efficiency caused by the dispersion of log data from multiple applications in the prior art.

[0026] In the technical solution of this application, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse.

[0027] In scenarios involving automated decision-making using personal information, the methods, devices, and systems provided in this application all offer users corresponding entry points for choosing to agree to or reject the automated decision-making results. If the user chooses to reject, the process proceeds to the expert decision-making stage. Here, "automated decision-making" refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests, or economic, health, and credit status through computer programs, and then making a decision. Here, "expert decision-making" refers to the activity of making decisions by personnel who specialize in a particular field, possess specialized experience, knowledge, and skills, and have reached a certain level of professional expertise.

[0028] Figure 1 The illustration shows application scenarios of the multi-application maintenance method, apparatus, device, medium, and program product according to embodiments of this application.

[0029] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0030] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0031] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0032] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0033] It should be noted that the multi-application maintenance method provided in this application embodiment can generally be executed by server 105. Correspondingly, the multi-application maintenance device provided in this application embodiment can generally be located in server 105. The multi-application maintenance method provided in this application embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the multi-application maintenance device provided in this application embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0034] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0035] The following will be based on Figure 1 The described scene, through Figures 2-3 A multi-application maintenance method according to embodiments of this application will be described in detail.

[0036] Figure 2 A flowchart illustrating a multi-application maintenance method according to an embodiment of this application is shown schematically.

[0037] like Figure 2 As shown, the multi-application maintenance method of this embodiment includes operations S210 to S240, and the transaction processing method can be executed by the server.

[0038] In operation S210, the identifier of each target application and multiple error logs are obtained from multiple target applications in the same usage environment.

[0039] In some embodiments, when acquiring multiple target application identifiers and error logs from the same usage environment, data capture can be achieved through automatically deployed collection agents or standardized interfaces. For example, the legitimacy of the target application can be verified first, and a mapping relationship can be established through unique identifiers to ensure the accuracy of the attribution of error logs for each target application.

[0040] For example, multiple target applications in the same usage environment may be target applications with different technology stacks. For the unstructured raw logs generated by these applications, format normalization processing can be performed using regular expression matching, syntax tree parsing, or semantic understanding techniques based on natural language processing models to obtain error logs. Simultaneously, the real-time performance and completeness of error log collection need to be considered. This can be achieved by real-time monitoring of the collection channel's working status and setting up buffer queues to handle network fluctuations. Ultimately, error logs and identifiers can be associated and stored to provide a data source for subsequent analysis.

[0041] During operation S220, each error log is analyzed to obtain the error reason and priority of each error log.

[0042] In some embodiments, a multi-level parsing engine can be pre-built to analyze error logs. For example, the error type of each error log is first located through keyword matching and pattern recognition, and then the cause of each error log is inferred by combining contextual information (such as request parameters, thread status, and resource utilization). The priority of each error log can be analyzed using a dynamic weighted algorithm, comprehensively considering multiple dimensions such as log type, core log tag, core log content, error frequency, affected user scope, and relevance to the business critical path, to output the algorithm results and obtain the priority of each error log.

[0043] In some embodiments, the cause and priority of each error log can be visualized. For example, in some complex scenarios, the call topology can be reconstructed by calling the link tracing data, and causal relationship mining can be performed by combining the log time series, ultimately generating a standardized analysis report that includes multiple dimensions such as log type, log core tags, log core content, and error classification.

[0044] When operating S230, determine the maintenance strategy for each error log based on the error cause.

[0045] In some embodiments, for complex error reasons requiring manual intervention, the error reasons involved in this scenario can be recorded and analyzed, and administrators can be notified via email or SMS according to the priority of each error log, so that administrators can handle them in a timely manner. Through specific error logs, administrators can quickly locate the corresponding target application's interface and service, analyze the error, and provide solutions to quickly fix the target application.

[0046] In operation S240, the target application corresponding to each error log is maintained according to the identifier, maintenance policy, and priority.

[0047] In some embodiments, each error log carries a unique identifier, which establishes a correspondence between the error log and the target application. The specific steps are as follows: First, the processing order of each error log is determined based on its priority. Then, the target application corresponding to the currently pending error log is quickly located using its identifier. Finally, after locating the target application, maintenance is performed on the target application according to the maintenance policy corresponding to that error log.

[0048] According to the multi-application maintenance method provided in the embodiments of this application, by acquiring the error logs of multiple target applications in the same usage environment and performing aggregated analysis, unified maintenance for multiple target applications is achieved. Furthermore, the maintenance order can be determined based on the priority of the current error logs of each target application, thus achieving more efficient multi-application maintenance.

[0049] According to embodiments of this application, the multiple error logs include front-end error logs and back-end error logs. The front-end error logs are obtained through the Hypertext Transfer Protocol Security (HTTP) interface, and the back-end error logs are obtained through the HTTP interface.

[0050] Figure 3 The flowchart illustrating the process of obtaining front-end and back-end error logs according to an embodiment of this application is shown in the illustration.

[0051] like Figure 3 As shown, front-end logs often contain entries indicating code errors, memory overflow errors, and blank pages. These entries can be collected and recorded as front-end error logs using event tracking tools. For example, event tracking tools can be used to obtain the front-end and back-end error logs of target application A, target application B, and target application C, respectively. These front-end error logs from multiple target applications can then be uploaded to a server via a Hypertext Transfer Protocol (HTTP) interface. During the upload process, asymmetric encryption algorithms can be used to encrypt the front-end error logs to improve transmission security.

[0052] The backend error logs of the aforementioned target applications can be uploaded to the server via the Hypertext Transfer Protocol (HTTP) interface. To more accurately analyze the cause of each error log, the full content of the backend error logs can be uploaded.

[0053] According to the multi-application maintenance method provided in the embodiments of this application, based on the differentiated protocol design of the front-end Hypertext Transfer Protocol interface and the back-end Hypertext Transfer Protocol interface, front-end error logs and back-end error logs are collected respectively, taking into account both security and efficiency.

[0054] According to an embodiment of this application, each error log is analyzed to obtain the error cause and priority of each error log, including: performing semantic analysis on each error log using a semantic analysis engine to obtain multiple key fields; evaluating the multiple key fields using a preset priority evaluation algorithm; and determining the priority of each error log based on the evaluation results.

[0055] In some embodiments, a semantic analysis engine can be used to achieve accurate error attribution and prioritization. First, the semantic analysis engine, as a core component, uses a combination of natural language processing technology and a regular expression rule base to perform deep parsing of unstructured error logs. This semantic analysis engine extracts key entities (such as error codes, exception types, associated modules, and timestamps) from the error logs through word segmentation and uses parsing algorithms to construct the contextual relationships of the error logs, identifying multiple key fields contained within them. For example, for front-end error logs, the semantic analysis engine can extract multiple key fields such as user operation paths, device information, and interface response status codes. For back-end error logs, the semantic analysis engine can parse multiple key fields from the stack trace, such as method call sequences, database query statements, and third-party service dependencies.

[0056] Based on the extraction of key fields, a priority evaluation algorithm can be used to evaluate the n key fields of each error log, obtaining n priority scores corresponding to the n key fields as the evaluation result, where n is an integer greater than 1. Then, the n priority scores are summarized to obtain the priority of each error log.

[0057] For example, a priority evaluation algorithm can employ a dynamic weighted model to comprehensively consider multiple dimensions to generate priority evaluation results. This algorithm first matches a preset priority pattern using a rule engine, automatically marking key fields related to user payment failures, core function unavailability, or security vulnerabilities as high priority. Simultaneously, the aforementioned dynamic weighted model can also use analyzed error logs as training data to dynamically adjust the weights within the model. The training data for this dynamic weighted model can include key fields such as error frequency, affected user scope, relevance to critical business paths, and recent error trend changes.

[0058] The multi-application maintenance method provided in this application's embodiments uses a semantic analysis engine to accurately parse error logs and extract key fields, then combines this with a priority algorithm to comprehensively evaluate priorities. This automates the process from error log parsing to priority determination, ensuring that servers prioritize high-priority error logs and improving operational efficiency and reliability.

[0059] According to an embodiment of this application, determining the priority of each error log based on the evaluation results includes: sorting multiple key fields of each error log using a natural language processing model to obtain a sorting result; and determining the priority based on the evaluation results and the sorting result.

[0060] In some embodiments, the process of determining the priority of error logs can achieve accurate evaluation by combining the evaluation results of a natural language processing model with the evaluation results of multiple key fields.

[0061] First, a natural language processing (NLP) model is used to perform in-depth analysis of the key fields extracted by the semantic analysis engine. This NLP model converts the multiple key fields into numerical features through text vectorization and combines an attention mechanism to identify the relationships between these key fields.

[0062] Subsequently, the natural language processing model, based on preset ranking rules, prioritizes multiple key fields according to the aforementioned numerical features, generating a ranking result that represents a sequence composed of multiple key fields. This priority ranking process not only considers the numerical features of the key fields themselves but also uses an attention mechanism to identify the correlations between multiple key fields, dynamically adjusting the ranking result based on these correlations.

[0063] Finally, a pre-defined optimization algorithm is used to assign weights to the sorting and evaluation results, and the priority is determined based on the weight values ​​of both the sorting and evaluation results. Once the priority is determined, it can be associated with the error cause and stored to provide data support for the formulation of subsequent maintenance strategies.

[0064] According to the multi-application maintenance method provided in the embodiments of this application, the ranking results of multiple key fields by the natural language processing model and the evaluation results of multiple key fields are combined to achieve accurate priority evaluation.

[0065] According to an embodiment of this application, each error log is analyzed to obtain the error reason and priority of each error log, and the method further includes: inputting multiple key fields of each error log into a natural language processing model to obtain the error reason of each error log.

[0066] In some embodiments, a natural language processing model can be used to perform semantic parsing on the key fields of each error log, and the error reason for each error log can be summarized through text generation technology.

[0067] According to the multi-application maintenance method provided in the embodiments of this application, the error reason of each error log can be accurately obtained by parsing key fields through a natural language processing model.

[0068] According to an embodiment of this application, a maintenance strategy for each error log is determined based on the error cause, including: classifying multiple error causes corresponding to multiple error logs to obtain classification results; and determining a maintenance strategy matching the classification results from a preset maintenance strategy knowledge base.

[0069] In some embodiments, error reasons can be categorized based on their semantic features and business attributes to obtain classification results. For example, when a user fails to log in to the front-end page of a target application, the target application generates a front-end error log. Analyzing this log reveals the error reason. If the semantic feature of this error reason is "login timeout" and its business attribute is "login module," then the classification result is "login module - login timeout." As another example, when the back-end order processing of a target application experiences a processing interruption, the application generates a back-end error log. Analyzing this log reveals the error reason. If the semantic feature of this error reason is "processing interruption" and its business attribute is "processing module," then the classification result is "processing module - processing interruption."

[0070] The maintenance strategy knowledge base can be constructed using a knowledge graph structure. Multiple maintenance strategies within the knowledge base can be obtained by integrating historical maintenance records and expert rules. The knowledge base assigns different identifiers to multiple maintenance strategies based on their types. When matching classification results, semantic analysis is first performed on the results to obtain the semantic analysis outcome. Then, based on the semantic analysis outcome, the knowledge base is searched for the identifier that best matches the semantic analysis outcome. Finally, the maintenance strategy matching the classification outcome is determined based on this identifier.

[0071] The maintenance strategy knowledge base can include two categories of strategies: short-term emergency strategies and long-term optimization strategies. Short-term emergency strategies are solutions targeting the cause of the error (such as service degradation and resource expansion), used to quickly restore the normal functionality of the target application corresponding to the error. Long-term optimization strategies, on the other hand, are specific optimization strategies proposed based on the cause of the error, such as architectural optimization (such as introducing caching mechanisms) and dependency upgrades (such as database driver version iterations), used to optimize the target application corresponding to the error to prevent the same error from occurring in subsequent operations.

[0072] According to the multi-application maintenance method provided in the embodiments of this application, by matching the classification results with the maintenance strategy knowledge base, the automatic association from the error cause to the maintenance solution is realized, which shortens the strategy generation time and greatly improves the maintenance efficiency.

[0073] According to an embodiment of this application, the target application corresponding to each error log is maintained sequentially according to the identifier, maintenance policy, and priority, including: determining the correspondence between multiple error logs and multiple target applications according to the identifier; and maintaining the target application corresponding to each error log sequentially according to the priority, correspondence, and maintenance policy.

[0074] In some embodiments, each error log carries a unique identifier, which establishes a correspondence between the error log and the target application. The identifier then allows for quick location of the target application corresponding to the error log.

[0075] The multi-application maintenance method provided in the embodiments of this application accurately locates the target application through identifiers, shortens the fault maintenance cycle, and significantly improves maintenance efficiency.

[0076] Based on the above-described multi-application maintenance method, this application also provides a multi-application maintenance device. The following will be combined with... Figure 4 The device is described in detail.

[0077] Figure 4 A schematic block diagram of a multi-application maintenance device according to an embodiment of this application is shown.

[0078] like Figure 4 As shown, the multi-application maintenance device 400 of this embodiment includes an acquisition module 410, an analysis module 420, a determination module 430, and a maintenance module 440.

[0079] The acquisition module 410 is used to acquire the identifier of each target application and multiple error logs from multiple target applications in the same usage environment. In one embodiment, the acquisition module 410 can be used to perform the operation S210 described above, which will not be repeated here.

[0080] The analysis module 420 is used to analyze each error log to obtain the error reason and priority of each error log. In one embodiment, the analysis module 420 can be used to perform the operation S220 described above, which will not be repeated here.

[0081] The determination module 430 is used to determine the maintenance strategy for each error log based on the error cause. In one embodiment, the determination module 430 can be used to perform the operation S230 described above, which will not be repeated here.

[0082] The maintenance module 440 is used to maintain the target application corresponding to each error log according to the identifier, maintenance policy, and priority. In one embodiment, the maintenance module 440 can be used to perform the operation S240 described above, which will not be repeated here.

[0083] According to embodiments of this application, any multiple modules among the acquisition module 410, analysis module 420, determination module 430, and maintenance module 440 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this application, at least one of the acquisition module 410, analysis module 420, determination module 430, and maintenance module 440 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the acquisition module 410, analysis module 420, determination module 430, and maintenance module 440 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0084] Figure 5 A block diagram schematically illustrates an electronic device suitable for implementing a multi-application maintenance method according to an embodiment of this application.

[0085] like Figure 5As shown, an electronic device 500 according to an embodiment of this application includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage portion 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0086] RAM 503 stores various programs and data required for the operation of electronic device 500. Processor 501, ROM 502, and RAM 503 are interconnected via bus 504. Processor 501 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0087] According to embodiments of this application, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to a bus 504. The electronic device 500 may also include one or more of the following components connected to the input / output (I / O) interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0088] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0089] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include ROM 502 and / or RAM 503 and / or one or more memories other than ROM 502 and RAM 503 described above.

[0090] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the multi-application maintenance method provided in the embodiments of this application.

[0091] When the computer program is executed by the processor 501, it performs the functions defined in the system / apparatus of this application embodiment. According to the embodiments of this application, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0092] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 509, and / or installed from a removable medium 511. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0093] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by processor 501, it performs the functions defined in the system of this application embodiment. According to embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0094] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0096] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

Claims

1. A multi-application maintenance method, characterized in that, The method includes: Obtain the identifier of each target application and multiple error logs from multiple target applications in the same usage environment; Each error log is analyzed to obtain the error reason and priority of each error log; Based on the error reason, determine the maintenance strategy for each error log; The target application corresponding to each error log is maintained according to the identifier, the maintenance policy, and the priority.

2. The method according to claim 1, characterized in that, The multiple error logs include front-end error logs and back-end error logs. The front-end error logs are obtained through the Hypertext Transfer Protocol Security (HTTP) interface, and the back-end error logs are obtained through the HTTP interface.

3. The method according to claim 1, characterized in that, The step of analyzing each error log to obtain the error cause and priority of each error log includes: The semantic analysis engine was used to perform semantic analysis on each error log to obtain several key fields; The multiple key fields are evaluated using a preset priority evaluation algorithm, and the priority of each error log is determined based on the evaluation results.

4. The method according to claim 3, characterized in that, The step of determining the priority of each error log based on the evaluation results includes: The multiple key fields of each error log are sorted using a natural language processing model to obtain a sorting result; The priority is determined based on the evaluation results and the ranking results.

5. The method according to claim 3, characterized in that, The step of analyzing each error log to obtain the error cause and priority of each error log also includes: Input the multiple key fields of each error log into a natural language processing model to obtain the error reason for each error log.

6. The method according to claim 1, characterized in that, The step of determining the maintenance strategy for each error log based on the error cause includes: The error reasons corresponding to the multiple error logs are classified to obtain the classification results; The maintenance strategy that matches the classification result is determined from the preset maintenance strategy knowledge base.

7. The method according to claim 1, characterized in that, The step of maintaining the target application corresponding to each error log in sequence according to the identifier, the maintenance policy, and the priority includes: Based on the identifier, determine the correspondence between the plurality of error logs and the plurality of target applications; Based on the priority, the correspondence, and the maintenance strategy, the target application corresponding to each error log is maintained sequentially.

8. A multi-application maintenance device, characterized in that, The device includes: The acquisition module is used to acquire the identifier of each target application and multiple error logs from multiple target applications in the same usage environment; The analysis module is used to analyze each of the error logs to obtain the error cause and priority of each error log; The determination module is used to determine the maintenance strategy for each error log based on the error reason; The maintenance module is used to maintain the target application corresponding to each error log according to the identifier, the maintenance strategy, and the priority.

9. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 7.

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