A system operation and maintenance method, device, medium and program product

Abstract

This invention relates to the field of computer technology and discloses a system operation and maintenance method, device, medium, and program product. The method includes: collecting the operational information of a target object based on an analysis request for that object in the system, and extracting the contextual features corresponding to the operational information; performing a matching search in an analysis unit library based on the contextual features to obtain each target analysis unit, and generating an analysis execution flow based on each target analysis unit; and executing each target analysis unit according to the analysis execution flow to obtain the final analysis result corresponding to the target object. By breaking down object analysis capabilities into independent analysis units and realizing the search and combination of matching analysis units based on contextual features to generate the analysis execution flow corresponding to the target object, and then obtaining the final analysis result by executing the analysis execution flow, the reusability, scalability, and flexibility of observable object analysis can be improved, and the iteration cost of object analysis can be reduced.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a system operation and maintenance method, device, medium, and program product. Background Technology

[0002] With the widespread adoption of distributed systems and cloud-native architectures, various types of observable objects exist within these systems. Analyzing the state of these observable objects has gradually become a crucial task in system operations and maintenance.

[0003] Currently, existing system operation and maintenance methods typically predefine fixed analysis processes for different observable objects and implement object analysis based on these fixed processes. However, this approach of setting fixed analysis processes results in strong coupling between the analysis process and the object type, leading to poor reusability. In addition, if new analysis capabilities are needed, the entire analysis logic must be modified, resulting in high iteration costs. Finally, it is impossible to dynamically adjust the analysis process according to the actual running status of the object and data availability, resulting in poor flexibility. Summary of the Invention

[0004] This invention provides a system operation and maintenance method, equipment, medium, and program product that can improve the reusability, scalability, and flexibility of observable object analysis and reduce the iterative cost of object analysis.

[0005] According to one aspect of the present invention, a system operation and maintenance method is provided, comprising: Based on the analysis request for the target object in the system, the running information of the target object is collected, and the context features corresponding to the running information are extracted; The analysis unit library is matched and searched according to the context features to obtain each target analysis unit, and an analysis execution flow is generated according to each target analysis unit. The target analysis units are executed according to the analysis execution process to obtain the final analysis results corresponding to the target object.

[0006] According to another aspect of the present invention, a system operation and maintenance device is provided, comprising: The feature extraction module is used to collect the running information of the target object according to the analysis request of the target object in the system, and extract the context features corresponding to the running information; The process generation module is used to perform matching and searching in the analysis unit library according to the context features to obtain each target analysis unit, and generate an analysis execution process according to each target analysis unit; The result acquisition module is used to execute each target analysis unit according to the analysis execution process to obtain the final analysis result corresponding to the target object.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the system operation and maintenance method described in any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, the computer program being used to cause a processor to execute and implement the system operation and maintenance method described in any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the system operation and maintenance method described in any embodiment of the present invention.

[0010] The technical solution of this invention collects the running information of the target object according to the analysis request of the target object in the system, and extracts the context features corresponding to the running information; performs matching search in the analysis unit library according to the context features to obtain each target analysis unit, and generates an analysis execution flow according to each target analysis unit; executes each target analysis unit according to the analysis execution flow to obtain the final analysis result corresponding to the target object; by splitting the object analysis capability into independent analysis units, and realizing the search and combination of matching analysis units based on context features to generate the analysis execution flow corresponding to the target object, and then obtaining the final analysis result by executing the analysis execution flow, the reusability, scalability and flexibility of observable object analysis can be improved, and the iteration cost of object analysis can be reduced.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a system operation and maintenance method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a system operation and maintenance method provided according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of a system operation and maintenance device according to Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the system operation and maintenance method of this invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Example 1 Figure 1 This is a flowchart of a system operation and maintenance method provided in Embodiment 1 of the present invention. This embodiment is applicable to the analysis of observable objects in a system operation and maintenance scenario. This method can be executed by a system operation and maintenance device, which can be implemented in hardware and / or software. Typically, the system operation and maintenance device can be configured in electronic devices, such as computer equipment, servers, etc. Figure 1 As shown, the method includes: S110. Based on the analysis request for the target object in the system, collect the running information of the target object and extract the context features corresponding to the running information.

[0017] The system can be a distributed business system, transaction system, etc. The target object can be an observable object within the system that has monitoring value and conditions; the analysis results of the target object can reflect the system's operational status. Optionally, the target object can include application services, hosts, containers, system resources, and / or custom entities. System resources can be Kubernetes resources, etc.

[0018] In this embodiment, the analysis operation on the target object can be triggered periodically or manually. During object analysis, firstly, all relevant data of the target object can be identified from log files, link data, etc., as runtime information. Next, based on preset feature extraction rules, the context features corresponding to the target object can be extracted from the runtime information. Context features include the target object's own characteristics, as well as the characteristics of its related upstream and downstream entities. This embodiment does not specifically limit the method of collecting runtime information.

[0019] Extracting the context features corresponding to the runtime information may include: Extract the entity type features, entity attribute features, related data features, upstream entity features, and downstream entity features corresponding to the target object from the runtime information; The context features are generated based on the entity type features, the entity attribute features, the related data features, the upstream entity features, and the downstream entity features.

[0020] In this embodiment, a context builder can be used to extract context features corresponding to runtime information. The context builder is responsible for answering three questions: (1) What is the entity type, such as service, host, container, database, etc.; (2) What data is there; (3) Which analyses are "worth running". The output of the context builder can include entity type features, entity attribute features, related data features, upstream entity features, and downstream entity features. Among them, entity type features can include service, host, database, etc. Entity attribute features can include service language, host type, data type, etc. Related data features can include metric (such as latency, errors, traffic, gc_time, etc. associated metric names), log (indicating whether there are associated logs), trace (indicating whether there are associated links), alert (indicating whether there are associated alarms), change (indicating whether there are associated changes), etc. Upstream entity features and downstream entity features can include entity type and entity identifier, etc.

[0021] Specifically, the context builder can first extract the entity type features, entity attribute features, related data features, upstream entity features, and downstream entity features corresponding to the target object from the runtime information. Then, the extracted features are combined to generate the final context features.

[0022] The advantage of the above settings is that they can improve the completeness of contextual features and increase the accuracy of analysis unit matching and searching.

[0023] S120. Match and search in the analysis unit library according to the context features to obtain each target analysis unit, and generate an analysis execution flow according to each target analysis unit.

[0024] In this embodiment, the analytical capabilities of the observable object are decomposed into multiple independent analytical units, and a set of analytical units is generated as an analytical unit library. Each analytical unit is configured with corresponding information, such as identifier, application conditions, required data, analysis method, and output content. The analytical unit library supports operations such as adding, deleting, modifying, and searching analytical units.

[0025] Specifically, using contextual features as search criteria, target analysis units that successfully match the target object are selected from the analysis unit library. Then, based on the preset execution priority and order of each analysis unit, the target analysis units are combined to generate the analysis execution flow.

[0026] The matching search in the analysis unit library based on the context features to obtain each target analysis unit may include: Obtain the application conditions corresponding to each initial analysis unit in the analysis unit library; The context features are matched with the application conditions corresponding to each initial analysis unit. If the context features are successfully matched with the application conditions corresponding to the current initial analysis unit, the current initial analysis unit is determined as the target analysis unit.

[0027] In a specific example, the configuration information for each initial analysis unit may include fields such as id, applied_to, required_data, analysis_method, outputs, and confidence / explainability. The id field represents a unique identifier (ID), the applied_to field represents the application conditions, and the required_data field represents the required data. The analysis_method field represents the analysis method, typically including deterministic, hybrid, and llm_only methods. Deterministic methods involve metric calculation, threshold judgment, and causal chains; they must be deterministic, verifiable, and time-dependent, and cannot be covered by a Large Language Model (LLM). Hybrid methods use system-based fact-finding and LLM interpretation models, such as summarizing anomaly patterns and classifying error logs. llm_only methods are used for synthesizing conclusions and action recommendations, such as summarizing the outputs of multiple analysis units, subjective judgments plus execution levels, and translating technical facts into natural language. The Outputs field represents the structured output content, including conclusions and evidence. The confidence / explainability field indicates whether the analysis is reliable and can be explained.

[0028] For example, the initial analysis unit is the JVM GC analysis unit, and its configuration information is: id: jvm_gc_analysis; applies_to: [entity_type: service # application service; language: java # applied to Java service], [entity_type: process # process; Technology: java # Java process]; Required_date: metrics: [gc_pause, heap_used]; analysis_method: type: hybrid # rule + LLM; outputs: type: finding, type: recommendation.

[0029] In this embodiment, the corresponding application conditions can be extracted from the configuration information of the initial analysis unit, and the current context features can be matched with the application conditions of each initial analysis unit to filter out the initial analysis units that meet the execution conditions as target analysis units.

[0030] The advantage of the above settings is that they enable accurate selection of target analysis units and improve the accuracy of object analysis.

[0031] S130. Execute each target analysis unit according to the analysis execution process to obtain the final analysis result corresponding to the target object.

[0032] Specifically, following this analysis execution process, each target analysis unit is executed sequentially or in parallel to obtain the analysis results output by each target analysis unit, and each analysis result is directly used as the final analysis result corresponding to the target object.

[0033] The technical solution of this invention collects the running information of the target object according to the analysis request of the target object in the system, and extracts the context features corresponding to the running information; performs matching search in the analysis unit library according to the context features to obtain each target analysis unit, and generates an analysis execution flow according to each target analysis unit; executes each target analysis unit according to the analysis execution flow to obtain the final analysis result corresponding to the target object; by splitting the object analysis capability into independent analysis units, and realizing the search and combination of matching analysis units based on context features to generate the analysis execution flow corresponding to the target object, and then obtaining the final analysis result by executing the analysis execution flow, the reusability, scalability and flexibility of observable object analysis can be improved, and the iteration cost of object analysis can be reduced.

[0034] Example 2 Figure 2 This is a flowchart of a system operation and maintenance method provided in Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution, and the technical solution in this embodiment can be combined with one or more of the above implementation methods. Figure 2 As shown, the method includes: S210. Based on the analysis request for the target object in the system, collect the running information of the target object and extract the context features corresponding to the running information.

[0035] S220. Match and search in the analysis unit library according to the context features to obtain each target analysis unit, obtain the dependency relationship between each target analysis unit, and generate the analysis execution flow according to the dependency relationship.

[0036] It is understandable that some functional analyses require the results of other functional analyses as input. Based on this, this embodiment can pre-set the dependencies between initial analysis units. Therefore, after determining the target analysis units, the dependencies between each target analysis unit can be determined first based on the pre-set dependencies between the initial analysis units. Then, for target analysis units that have dependencies on each other, serial execution is selected, while for target analysis units that do not have dependencies, parallel execution can be selected. After determining the execution order of all target analysis units, the current analysis execution flow can be generated.

[0037] It should be noted that the analysis and execution process of the target object is not fixed and unchanging. When its running information changes, the corresponding context features and target analysis units will be dynamically adjusted, which will lead to dynamic changes in the analysis and execution process.

[0038] S230. Execute each target analysis unit according to the analysis execution process to obtain the initial analysis results corresponding to each target analysis unit, and obtain the final analysis results based on each initial analysis result.

[0039] In this embodiment, when obtaining the final analysis results, each target analysis unit can be executed serially or in parallel according to the analysis execution flow to obtain the initial analysis results of each target analysis unit. Then, a large language model and / or preset rules are used to integrate and further analyze the initial analysis results to generate the final analysis results of the target object. The final analysis results may include the main cause determination results, confidence level, and next step suggestions.

[0040] Obtaining the final analysis result based on each of the initial analysis results may include: Based on the preset prompt word template, as well as each target analysis unit and the corresponding initial analysis results, target prompt words are generated, and the final analysis results are obtained based on the target prompt words through a pre-trained result analysis model.

[0041] In this embodiment, a result output device can be used to fill each target analysis unit and the corresponding initial analysis result into the corresponding position of the preset prompt word template to generate target prompt words, and call the pre-trained result analysis model, taking the target prompt words as input, to obtain the final analysis result output by the result analysis model.

[0042] Before obtaining the final analysis result based on the target prompt words using the pre-trained result analysis model, the process may further include: acquiring a basic large language model and collecting correlation data between the analysis unit, the initial analysis result, and the manual analysis result as training samples; then, using the training samples to fine-tune the basic large language model to obtain the trained large language model as the result analysis model. The basic large language model can be an open-source large language model or a large language model built based on preset model parameters.

[0043] The advantage of the above settings is that they can improve the accuracy of the final analysis results and further enhance the accuracy of object analysis.

[0044] In one specific implementation of this embodiment, taking the intelligent analysis of an application service as an example, the target object is a microservice written in Java and deployed in a Kubernetes environment. After collecting runtime information and extracting context features, the obtained context features indicate that the service language is Java, there is a database dependency, and call chain data has been collected. Then, based on the context features, the target analysis units are selected as a JVM (Java Virtual Machine) analysis unit, an HTTP (Hypertext Transfer Protocol) latency analysis unit, and an SQL (Structured Query Language) latency analysis unit. Further, it is determined that the SQL latency analysis unit depends on the call chain analysis results; therefore, it is decided to execute the call chain-related analysis units first, followed by the SQL analysis unit, thereby generating an analysis execution flow. Finally, by executing each target analysis unit according to the analysis execution flow, a service performance bottleneck analysis conclusion is generated.

[0045] Secondly, when it is necessary to expand the analysis capabilities, such as adding a new JVM garbage collection analysis capability, it is only necessary to add the corresponding analysis unit and define its application conditions, without modifying the existing analysis process.

[0046] In this embodiment, observable analytics capabilities are abstracted and decomposed into multiple independent analysis units, and the analysis execution flow is dynamically orchestrated based on contextual features. Before the analysis begins, the contextual features of the analysis object are automatically constructed, and the analysis path and set of analysis units are determined based on these features. Moreover, the analysis execution flow is not a predefined sequence, but rather depends on the driving execution graph.

[0047] Modularizing analytical capabilities significantly improves their reusability. Dynamically generating analytical workflows based on context avoids ineffective analysis. Furthermore, the technical solution of this invention supports flexible expansion of analytical capabilities, reducing system evolution costs while enhancing the consistency and interpretability of intelligent analysis results.

[0048] The technical solution of this invention obtains the dependencies between various target analysis units and generates an analysis execution flow based on these dependencies. By pre-setting the dependencies between analysis units, the execution flow of the analysis units can be automatically determined, improving the efficiency and accuracy of the analysis execution flow generation. Secondly, each target analysis unit is executed according to the analysis execution flow to obtain the initial analysis results corresponding to each target analysis unit, and the final analysis results are obtained based on these initial analysis results. By integrating the initial analysis results of each target analysis unit to obtain the final analysis results, the accuracy of the final analysis results can be improved, thus enhancing the accuracy of the object analysis.

[0049] Example 3 Figure 3 This is a schematic diagram of a system operation and maintenance device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a feature extraction module 310, a process generation module 320, and a result acquisition module 330; wherein, The feature extraction module 310 is used to collect the running information of the target object according to the analysis request of the target object in the system, and extract the context features corresponding to the running information; The process generation module 320 is used to perform matching and searching in the analysis unit library according to the context features to obtain each target analysis unit, and generate an analysis execution process according to each target analysis unit; The result acquisition module 330 is used to execute each target analysis unit according to the analysis execution process to obtain the final analysis result corresponding to the target object.

[0050] The technical solution of this invention collects the running information of the target object according to the analysis request of the target object in the system, and extracts the context features corresponding to the running information; performs matching search in the analysis unit library according to the context features to obtain each target analysis unit, and generates an analysis execution flow according to each target analysis unit; executes each target analysis unit according to the analysis execution flow to obtain the final analysis result corresponding to the target object; by splitting the object analysis capability into independent analysis units, and realizing the search and combination of matching analysis units based on context features to generate the analysis execution flow corresponding to the target object, and then obtaining the final analysis result by executing the analysis execution flow, the reusability, scalability and flexibility of observable object analysis can be improved, and the iteration cost of object analysis can be reduced.

[0051] Optionally, the feature extraction module 310 is specifically used to extract entity type features, entity attribute features, related data features, upstream entity features, and downstream entity features corresponding to the target object from the running information; The context features are generated based on the entity type features, the entity attribute features, the related data features, the upstream entity features, and the downstream entity features.

[0052] Optionally, the process generation module 320 includes: The condition acquisition unit is used to acquire the application conditions corresponding to each initial analysis unit in the analysis unit library; The condition matching unit is used to match the context features with the application conditions corresponding to each initial analysis unit. If the context features are successfully matched with the application conditions corresponding to the current initial analysis unit, the current initial analysis unit is determined as the target analysis unit.

[0053] Optionally, the process generation module 320 also includes: The process generation unit is used to obtain the dependencies between the target analysis units and generate the analysis execution process based on the dependencies.

[0054] Optionally, the result acquisition module 330 is specifically used to execute each target analysis unit according to the analysis execution process to obtain the initial analysis results corresponding to each target analysis unit, and to obtain the final analysis results based on each initial analysis result.

[0055] Optionally, the result acquisition module 330 is further configured to generate target prompt words based on the preset prompt word template, each target analysis unit and the corresponding initial analysis result, and obtain the final analysis result based on the target prompt words through a pre-trained result analysis model.

[0056] Optionally, target objects may include application services, hosts, containers, system resources, and / or custom entities.

[0057] The system operation and maintenance device provided in the embodiments of the present invention can execute the system operation and maintenance method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0058] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0059] Example 4 Figure 4A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device 40 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 40 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0060] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 42 or loaded from the storage unit 48 into the random access memory 43. The RAM 43 can also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0061] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0062] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, central processing units, graphics processing units, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as system operation and maintenance methods.

[0063] In some embodiments, the system maintenance method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the system maintenance method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the system maintenance method by any other suitable means (e.g., by means of firmware).

[0064] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays, application-specific integrated circuits (ASICs), application-specific standard products (ASICs), system-on-a-chip (SoCs), complex programmable logic devices, computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0065] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0066] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0067] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 40, which includes: a display device (e.g., a cathode ray tube or liquid crystal display) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device 40. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0068] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0069] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact via a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server.

[0070] This embodiment may also include a computer program product, which includes a computer program that, when executed by a processor, implements the system operation and maintenance method provided in any embodiment of the present invention.

[0071] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0072] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A system operation and maintenance method, characterized in that, include: Based on the analysis request for the target object in the system, the running information of the target object is collected, and the context features corresponding to the running information are extracted; The analysis unit library is matched and searched according to the context features to obtain each target analysis unit, and an analysis execution flow is generated according to each target analysis unit. The target analysis units are executed according to the analysis execution process to obtain the final analysis results corresponding to the target object.

2. The method according to claim 1, characterized in that, Extracting the context features corresponding to the runtime information includes: Extract the entity type features, entity attribute features, related data features, upstream entity features, and downstream entity features corresponding to the target object from the runtime information; The context features are generated based on the entity type features, the entity attribute features, the related data features, the upstream entity features, and the downstream entity features.

3. The method according to claim 1, characterized in that, Based on the contextual features, a matching search is performed in the analysis unit library to obtain each target analysis unit, including: Obtain the application conditions corresponding to each initial analysis unit in the analysis unit library; The context features are matched with the application conditions corresponding to each initial analysis unit. If the context features are successfully matched with the application conditions corresponding to the current initial analysis unit, the current initial analysis unit is determined as the target analysis unit.

4. The method according to claim 1, characterized in that, Based on the aforementioned target analysis units, an analysis execution flow is generated, including: Obtain the dependencies between each target analysis unit, and generate the analysis execution flow based on the dependencies.

5. The method according to claim 1, characterized in that, The analysis execution process is followed to execute each target analysis unit to obtain the final analysis result corresponding to the target object, including: The target analysis unit is executed according to the analysis execution process to obtain the initial analysis result corresponding to each target analysis unit, and the final analysis result is obtained based on each initial analysis result.

6. The method according to claim 5, characterized in that, Based on the initial analysis results, the final analysis results are obtained, including: Based on the preset prompt word template, as well as each target analysis unit and the corresponding initial analysis results, target prompt words are generated, and the final analysis results are obtained based on the target prompt words through a pre-trained result analysis model.

7. The method according to any one of claims 1-6, characterized in that, The target objects include application services, hosts, containers, system resources, and / or custom entities.

8. An electronic device, characterized in that, The electronic device includes: At least one processor, and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the system operation and maintenance method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the system operation and maintenance method according to any one of claims 1-7.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the system operation and maintenance method according to any one of claims 1-7.

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