Operation and maintenance task processing method, storage medium, and electronic device

Abstract

The present application discloses an operation and maintenance task processing method, a storage medium, and an electronic device. The method comprises: acquiring text information, wherein the text information is used for using a natural language to describe an operation and maintenance task to be executed; performing semantic analysis on the text information to determine an operation and maintenance operation instruction corresponding to the operation and maintenance task; and calling a target interface to send the operation and maintenance operation instruction to a target cloud service, and acquiring a target operation and maintenance result, wherein the target interface is a cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is an operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction. The present application solves the technical problems of high learning costs, high operation difficulty and low efficiency in a method for using an operation and maintenance template to define operation and maintenance operation steps.

Description

[0001]Technical Field: This application relates to the fields of computer technology and artificial intelligence technology, and more specifically, to an operation and maintenance task processing method, storage medium, and electronic device. Background: With the development of cloud computing, automated orchestration is increasingly being used in application scenarios to improve the efficiency of cloud-based operations and maintenance. A common method for implementing automated orchestration of cloud-based operations and maintenance in related technologies is to first create a cloud-based operations and maintenance template, define the operation steps in the template, and then execute automated operations and maintenance tasks using the template to complete the automated operations and maintenance. However, template writing requires familiarity with template syntax and the open programming interface (API) of cloud services, resulting in a high learning cost and high operational difficulty. Currently, no effective solution has been proposed to address the aforementioned issues. Summary of the Invention: Embodiments of the present application provide an operation and maintenance task processing method, storage medium, and electronic device to at least address the technical issues of using operation and maintenance templates to define operation and maintenance operation steps, which have high learning costs, high operational difficulty, and low efficiency. According to one aspect of an embodiment of the present application, a method for processing an operation and maintenance task is provided, comprising: obtaining text information, wherein the text information is used to describe the operation and maintenance task to be performed in natural language; performing semantic analysis on the text information to determine the operation and maintenance operation instruction corresponding to the operation and maintenance task; calling a target interface to issue the operation and maintenance operation instruction to a target cloud service, and obtaining a target operation and maintenance result, wherein the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction. According to another aspect of an embodiment of the present application, a method for processing an operation and maintenance task is provided. A graphical user interface is provided through a terminal device, and content displayed by the graphical user interface at least partially includes an operation and maintenance task processing scenario. The method for processing the operation and maintenance task includes: in response to a first touch operation on the graphical user interface, obtaining text information, wherein the text information is used to describe the operation and maintenance task to be performed in natural language; in response to a second touch operation on the graphical user interface, performing semantic analysis on the text information to determine an operation and maintenance operation instruction corresponding to the operation and maintenance task, calling a target interface to issue the operation and maintenance operation instruction to a target cloud service, and obtaining a target operation and maintenance result, wherein the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction.According to another aspect of an embodiment of the present application, a method for processing an operation and maintenance task is further provided, comprising: obtaining text information, wherein the text information is used to describe an e-commerce operation and maintenance task to be executed in natural language; performing semantic analysis on the text information to determine an e-commerce operation and maintenance operation instruction corresponding to the e-commerce operation and maintenance task; calling a target interface to issue the e-commerce operation and maintenance operation instruction to a target e-commerce cloud service, and obtaining a target e-commerce operation and maintenance result, wherein the target interface is the e-commerce cloud service interface corresponding to the e-commerce operation and maintenance task, and the target e-commerce operation and maintenance result is the e-commerce operation and maintenance result obtained after the target e-commerce cloud service executes the e-commerce operation and maintenance operation instruction. According to another aspect of an embodiment of the present application, a method for processing an operation and maintenance task is provided, comprising: obtaining text information, wherein the text information uses natural language to describe a logistics operation and maintenance task to be executed; performing semantic analysis on the text information to determine a logistics operation and maintenance operation instruction corresponding to the logistics operation and maintenance task; invoking a target interface to issue the logistics operation and maintenance operation instruction to a target logistics cloud service, and obtaining a target logistics operation and maintenance result, wherein the target interface is the logistics cloud service interface corresponding to the logistics operation and maintenance task, and the target logistics operation and maintenance result is the logistics operation and maintenance result obtained after the target logistics cloud service executes the logistics operation and maintenance operation instruction. According to another aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein, when the program is executed, the device containing the computer-readable storage medium is controlled to execute any one of the aforementioned operation and maintenance task processing methods. According to another aspect of an embodiment of the present application, an operation and maintenance task processing system is provided, comprising: a processor; and a memory, connected to the processor, configured to provide the processor with instructions for performing the following processing steps: obtaining text information, wherein the text information uses a natural language description of an operation and maintenance task to be performed; performing semantic analysis on the text information to determine an operation and maintenance operation instruction corresponding to the operation and maintenance task; and invoking a target interface to issue the operation and maintenance operation instruction to a target cloud service and obtain a target operation and maintenance result, wherein the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction. According to another aspect of an embodiment of the present application, a computer program product is provided, comprising a computer program, which, when executed by the processor, implements any of the above-described operation and maintenance task processing methods.In an embodiment of the present application, text information is obtained. This text information is used to describe an operation and maintenance task to be executed in natural language. Semantic analysis is then performed on the text information to determine the operation and maintenance instructions corresponding to the operation and maintenance task. A target interface is then called to issue the operation and maintenance instructions to a target cloud service, and a target operation and maintenance result is obtained. The target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance instruction. In this application scenario, a large language model can be used to analyze text information expressed in natural language to determine the user's operation and maintenance intent. This achieves the goal of automatically determining the operation and maintenance instructions in the operation and maintenance task based on natural language input, thereby automatically completing the operation and maintenance task. This reduces the user's operational difficulty in processing operation and maintenance tasks and improves operational efficiency. This addresses the technical issues of high learning costs, operational difficulty, and low efficiency associated with defining operation and maintenance steps using operation and maintenance templates. It should be noted that the general description above and the detailed description that follow are intended only to illustrate and explain the present application and do not constitute limitations on the present application. BRIEF DESCRIPTION OF THE DRAWINGS The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the accompanying drawings: Figure 1 is a schematic diagram of an application scenario of an operation and maintenance task processing method according to an embodiment of the present application; Figure 2 is a flow chart of an operation and maintenance task processing method according to embodiment 1 of the present application; Figure 3 is a schematic diagram of an optional operation and maintenance task processing process according to embodiment 1 of the present application; Figure 4 is a schematic diagram of a graphical user interface of an optional cloud-based operation and maintenance intelligent assistant according to embodiment 1 of the present application; Figure 5 is a schematic diagram of a secondary confirmation view of an optional cloud-based operation and maintenance intelligent assistant according to embodiment 1 of the present application; Figure 6 is a flow chart of an operation and maintenance task processing method according to embodiment 2 of the present application; Figure 7 is a flow chart of an operation and maintenance task processing method according to embodiment 3 of the present application; Figure 8 is a structural schematic diagram of an operation and maintenance task processing device according to embodiment 4 of the present application; Figure 9 is a structural schematic diagram of another operation and maintenance task processing device according to embodiment 4 of the present application; Figure 10 is a structural schematic diagram of another operation and maintenance task processing device according to embodiment 4 of the present application; Figure 11 is a structural block diagram of a computer terminal according to embodiment 5 of the present application. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS To enable persons skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments.All other embodiments derived by persons of ordinary skill in the art based on the embodiments described herein without inventive effort shall fall within the scope of protection of this application. It should be noted that the terms "first," "second," and so on, in the specification and claims of this application, and in the accompanying drawings, are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that such terms are interchangeable where appropriate, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to the steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus. The technical solutions provided in this application are primarily implemented using large-scale model technology. Large-scale models herein refer to deep learning models with large-scale model parameters, typically including hundreds of millions, tens of billions, hundreds of billions, trillions, or even more than ten trillion model parameters. Large models, also known as cornerstone models or foundation models, are pre-trained on large amounts of unlabeled corpora, producing pre-trained models with over 100 million parameters. These models are adaptable to a wide range of downstream tasks and exhibit good generalization capabilities. Examples include large language models (LLMs) and multi-modal pre-training models. It should be noted that in practical applications, large models can be fine-tuned using a small number of samples, allowing them to be applied to different tasks. For example, large models are widely used in fields such as natural language processing (NLP) and computer vision. Specifically, they can be applied to computer vision tasks such as visual question answering (VQA), image captioning (IC), and image generation. They can also be widely used in natural language processing tasks such as text-based sentiment classification, text summarization, and machine translation. Therefore, the main application scenarios of the large model include but are not limited to digital assistants, intelligent robots, search, online education, office software, e-commerce, intelligent design, etc. First, some nouns or terms that appear in the description of the embodiments of this application are subject to the following explanations.Operation Orchestration Service (OOS): This is an automated cloud-based O&M service that automates task management and execution. Users can define tasks, execution order, inputs, and outputs using templates, and then execute the templates to automate O&M tasks. AI-Generated Content (AIGC): This utilizes AI technology to automatically generate and manage content. Generative Artificial Intelligence Model (ChatGPT): refers to a generative model that combines comprehension, creativity, and memory, with its primary function being to interact with users in natural language. Example 1: According to an embodiment of the present application, a method for processing operations and maintenance tasks is provided. It should be noted that the steps shown in the flowcharts of the accompanying figures can be executed in a computer system, such as a set of computer-executable instructions. Furthermore, although the flowcharts illustrate a logical order, in some cases, the steps shown or described can be executed in a different order. Considering the large number of model parameters in large models and the limited computing resources of mobile terminals, the operation and maintenance task processing method provided in the embodiments of the present application can be applied to the application scenario shown in Figure 1, but is not limited thereto. In the application scenario shown in Figure 1, the large model is deployed on a server 10. The server 10 can be connected to one or more client devices 20 via a local area network, a wide area network, the Internet, or other types of data networks. Client devices 20 herein may include, but are not limited to, smartphones, tablet computers, laptops, PDAs, personal computers, smart home devices, and in-vehicle devices. The client device 20 can interact with the user through a graphical user interface to call the large model, thereby implementing the method provided in the embodiments of the present application. In the aforementioned operating environment, the present application provides an operation and maintenance task processing method as shown in Figure 2. Figure 2 is a flow chart of an operation and maintenance task processing method according to Example 1 of the present application. As shown in Figure 2, the operation and maintenance task processing method includes: Step S21, obtaining text information, wherein the text information is used to describe the operation and maintenance task to be executed in natural language; Step S22, performing semantic analysis on the text information to determine the operation and maintenance operation instructions corresponding to the operation and maintenance task; Step S23, calling a target interface to issue the operation and maintenance operation instructions to the target cloud service and obtaining a target operation and maintenance result, wherein the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction. The above-mentioned text information can be obtained by directly obtaining text input by the user, or by obtaining text converted from images, audio, or video input by the user. That is, in an application scenario, when a user wishes to perform an operation and maintenance task, they describe the operation and maintenance task in natural language on a user terminal through text input, voice input, or other methods. The user terminal then executes the operation and maintenance task processing method provided in the embodiments of the present application to obtain the text information. In one exemplary application scenario, an artificial intelligence model with natural language processing capabilities is used to perform semantic analysis on the text information to determine the operation and maintenance operation instructions corresponding to the operation and maintenance task.The artificial intelligence model can be a pre-trained large language model. This large language model can also determine the target interface corresponding to the described operation and maintenance task based on the text information and trigger the execution of operation and maintenance instructions to obtain the target operation and maintenance results. In an exemplary application scenario, a semantic analysis algorithm is used to perform semantic analysis on the text information, and an instruction generation algorithm is used to generate corresponding operation and maintenance instructions based on the semantic analysis results. The semantic analysis algorithm and instruction generation algorithm can be customized for the specific application scenario of the operation and maintenance task. For example, the semantic analysis algorithm can be a semantic analysis algorithm based on rules (such as logical reasoning rules, grammatical analysis rules, etc.), a semantic analysis algorithm based on corpus statistics, a semantic analysis algorithm based on a graphical model, or a semantic analysis algorithm based on a knowledge graph. The instruction generation algorithm can be an algorithm that generates operation and maintenance instructions based on an instruction template library and semantic analysis results for the operation and maintenance task application scenario. The target cloud service is a cloud service from multiple candidate cloud services that matches the operation and maintenance task. The multiple candidate cloud services are server-side products that expose APIs for the operation and maintenance scenario, with each candidate cloud service exposing at least one API (i.e., exposing multiple candidate interfaces to the operation and maintenance scenario). When it is necessary to control the target cloud service to execute the aforementioned operation and maintenance instructions, the target cloud service's exposed target interface is invoked, and the operation and maintenance instructions are issued to the target cloud service, enabling the target cloud service to execute the operation and maintenance instructions and obtain the target operation and maintenance results. It is easy to understand that after the user enters the description text of the operation and maintenance task, the operation and maintenance task processing process does not require human intervention, making the operation easy, user-friendly, highly automated, and efficient. It should be noted that the operation and maintenance tasks described above can be deployed on the cloud. The operation and maintenance task processing method provided in the embodiments of the present application can be executed in a system consisting of a client device and a server. The server can be a standalone server or a distributed server cluster, and the server can be located in the cloud. The client device executes the steps of the operation and maintenance task processing method, performs semantic analysis on the text information, determines the operation and maintenance instructions corresponding to the operation and maintenance task, invokes the target interface to issue the operation and maintenance instructions to the target cloud service, obtains the target operation and maintenance results, and returns the target operation and maintenance results to the client. It should be noted that, if the operating resources of the client device can meet the deployment and operation requirements of the large model, the embodiments of the present application can be performed on the client device. It is easy to understand that the above-mentioned operation and maintenance task processing method provided in the embodiments of the present application applies artificial intelligence natural language processing technology to automated operation and maintenance scenarios. Automatic cloud operation and maintenance is performed through natural language conversational interaction, lowering the threshold for cloud operation and maintenance, improving the efficiency of cloud automated operation and maintenance, and improving the operation and maintenance experience of operation and maintenance personnel.The above-described operation and maintenance task processing method can also be used to provide specialized automated operation and maintenance services for specific application scenarios. Such application scenarios include, but are not limited to, scenarios involving automated cloud-based operation and maintenance in fields such as e-commerce, education, healthcare, conferencing, social networking, financial products, logistics, and navigation. Accordingly, a specialized large language model pre-trained for these application scenarios can be used to perform semantic analysis on text information to implement operation and maintenance task processing. This specialized large language model can demonstrate high performance for specific tasks within the corresponding application scenario. In an embodiment of the present application, text information is obtained, which uses natural language to describe the operation and maintenance task to be executed. Semantic analysis is further performed on the text information to determine the operation and maintenance operation instructions corresponding to the operation and maintenance task. A target interface is then invoked to issue the operation and maintenance operation instructions to a target cloud service, and a target operation and maintenance result is obtained. The target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction. In application scenarios, a large language model can be used to analyze text information expressed in natural language to determine the user's operation and maintenance intent. This achieves the goal of automatically determining the operation and maintenance instructions in the operation and maintenance task based on natural language input, thereby automatically completing the operation and maintenance task. This reduces the difficulty of user operation in the operation and maintenance task processing and improves operation and maintenance efficiency. This further addresses the technical issues of high learning cost, high operational difficulty, and low efficiency associated with defining operation and maintenance steps using operation and maintenance templates. In an optional embodiment, in step S22, semantic analysis is performed on the text information to determine the operation and maintenance instructions corresponding to the operation and maintenance task. The method includes the following steps: Step S221: Using a semantic analysis model, semantic analysis is performed on the text information to generate operation and maintenance instructions, where the operation and maintenance instructions are used to trigger the execution of the operation and maintenance operation corresponding to the operation and maintenance task. The execution of the operation and maintenance task may include one or more operation and maintenance operations. In this optional embodiment, the semantic analysis model may be a large language model (e.g., Chat GPT) pre-trained using a training dataset for natural language semantic analysis. The training data set includes multiple sets of training data, each set of training data includes training text and corresponding text semantics. In the above optional embodiment, the semantic analysis model may also be a large language model pre-trained using the training data set for identifying operation and maintenance operations. The training data set includes multiple sets of training data, each set of training data includes training text and corresponding operation instructions.The process of semantic analysis of text information using a semantic analysis model includes: performing preprocessing operations on the text information to convert it into a data format that can be analyzed by the model. Among them, the preprocessing operations include text cleaning operations, word segmentation operations, stop word removal operations, word form reduction operations, etc.; extracting features from the text information. Specifically, methods such as the bag-of-words model, the term frequency-inverse document frequency (TF-IDF) model, and the word embedding model can be used to convert the text information into feature vectors; further, a suitable network model (such as a recurrent neural network, a long short-term memory network, a convolutional neural network, etc.) is selected to train the feature vectors. During the training process, evaluation metrics (such as accuracy, recall, etc.) are used to evaluate the performance and generalization ability of the network model to determine that the network model training is completed; then the output results of the network model are interpreted and analyzed to obtain the semantic analysis results. On this basis, operation and maintenance operation instructions are generated based on the instruction template library and the semantic analysis results of the operation and maintenance task application scenario. Thus, using a pre-trained model with good performance in semantic analysis and operation and maintenance operation instruction generation to process the text information input by the user can accurately identify the user's intention and generate operation and maintenance operation instructions. That is, it realizes the conversion of natural language (i.e., text information) into machine language (i.e., operation and maintenance operation instructions). In an optional embodiment, in step S221, semantic analysis is performed on the text information to generate operation and maintenance operation instructions, including the following method steps: step S2211, performing semantic analysis on the text information to obtain semantic information; step S2212, extracting key information from the semantic information to obtain operation descriptions of multiple operation and maintenance operations; step S2213, generating operation and maintenance operation instructions based on the operation descriptions. As an exemplary implementation manner, the specific implementation process of semantic analysis of text information may include: identifying the text intention to obtain the text intention; based on the text intention, performing word segmentation on the text information, and screening the word segmentation results to retain content words (for example, removing meaningless words such as "please", "bar", "um", "uh", etc.) to obtain semantic information. For example, in the process of implementing the operation and maintenance orchestration service using the semantic analysis model, the user inputs the text "Help me monitor the network security status within the next two hours, and send a monitoring report half an hour after the monitoring is completed, and then shut down" through the operation interface. For this input text, the semantic analysis model identifies and understands the text intention, and the text intention includes: intention one, monitoring the network security status within the next two hours; intention two: sending a monitoring report half an hour after the monitoring is completed; intention three: shutting down.Based on the text intent, the input text is segmented. The segmentation results contain multiple tokens arranged sequentially. For example, the segmentation results for the input text include: "help me," "monitor," "within the next two hours," "network security status," "and," "half an hour after monitoring is completed," "issue," "test report," and "then," "shut down." After filtering the segmentation results, the resulting semantic information includes: "monitor," "within the next two hours," "network security status," "half an hour after monitoring is completed," "issue," "test report," and "shut down." Furthermore, the semantic analysis model can extract key information based on this semantic information. Key information includes: monitoring time, monitoring content, monitoring report issuance time, and shutdown time. Thus, descriptive information for multiple operation and maintenance operations is determined, including: "Operation 1: Monitor the network security status within the next two hours"; "Operation 2: Generate a monitoring report and issue it half an hour after monitoring is completed"; and "Operation 3: Shut down after issuing the monitoring report." Based on this descriptive information, operation and maintenance instructions are generated. Furthermore, after determining the descriptive information for multiple operation and maintenance operations, the semantic analysis model can organize, reinterpret, and confirm this information to ensure the model accurately understands the user request. Thus, utilizing a pre-trained semantic analysis model, the natural language processing capabilities of artificial intelligence can be applied to automated operation and maintenance scenarios. This allows the system in these scenarios to directly process operation and maintenance tasks based on natural language, eliminating the need for technicians to manually define the operational steps in the operation and maintenance template, thus achieving de-templating. In an optional embodiment, the operation and maintenance task processing method further includes the following method steps: Step S24: Using a risk verification model, perform a risk verification on the operation and maintenance operation instructions to obtain a verification result, where the verification result is used to determine whether the operation and maintenance operation instructions contain risky instructions. In this application scenario, the operation and maintenance tasks that a user wishes to perform may include some high-risk operations. In this case, it is necessary to reconfirm with the user whether these high-risk operations should still be performed to avoid operation and maintenance risks caused by user input errors and protect system security. The risk verification model can be a pre-trained artificial intelligence model for risk verification of operation and maintenance operation instructions. The target risk verification model is trained using multiple sets of training data. Each set of training data includes operation and maintenance instruction samples and the risk instructions contained therein. Furthermore, the risk verification function can be integrated with the aforementioned semantic analysis function within the operation and maintenance task processing model to train the operation and maintenance task processing model for different operation and maintenance scenarios.In an optional embodiment, in step S24, risk verification is performed on the operation and maintenance instructions to obtain a verification result, which includes at least one of the following method steps: Step S241: Based on preset risk keywords, keyword matching is performed on the operation and maintenance instructions to determine whether the operation and maintenance instructions contain risk instructions corresponding to the risk keywords, thereby obtaining a verification result; Step S242: Based on preset risk operation rules, operation comparison is performed on the operation and maintenance instructions to determine whether the operation and maintenance instructions contain risk instructions corresponding to the risk operation rules, thereby obtaining a verification result. In this optional embodiment, the risk keywords can be predetermined by a technician based on prior data. In application scenarios, operation and maintenance instructions containing risk keywords can be considered risk instructions. The risk operation rules can be determined by a technician based on prior data or obtained from a system database. In application scenarios, operation and maintenance instructions that meet the risk operation rules can be considered risk instructions. As an exemplary embodiment, based on the aforementioned risk keywords and / or risk operation rules, operation and maintenance instructions are traversed and verified. If the operation and maintenance instruction does not contain a risk instruction, the verification result is "no risk or low risk." If the operation and maintenance instruction contains a risk instruction, the verification result is "high risk." Furthermore, when the operation and maintenance instruction contains a risk instruction, the instruction identifier of the identified risk instruction can be provided to the user, and the risk instruction can also be highlighted on the user interface. In application scenarios, the aforementioned risk keywords may include: change password, restart, delete, shutdown, etc. The aforementioned risk operation rules can be a library of known high-risk operations, such as write operations that are modified through the open application programming interface (OpenAPI) of the cloud service. In application scenarios, the risk verification model can also make a comprehensive determination of whether the operation and maintenance instruction contains a risk instruction based on the contextual data and contextual analysis data of the descriptive information corresponding to the operation and maintenance instruction during the risk verification process, thereby improving the accuracy of risk verification. In an optional embodiment, the operation and maintenance task processing method further includes the following method steps: Step S25: Selecting a target cloud service from multiple candidate cloud services based on the operation and maintenance task, and determining an application programming interface (API) exposed by the target cloud service as the target interface. The server executing the operation and maintenance task processing method provided in this embodiment of the application is associated with multiple candidate cloud services, each of which exposes multiple application programming interfaces (i.e., multiple candidate interfaces) for the operation and maintenance scenario.A pre-trained large language model for implementing the operation and maintenance task processing method can select a target cloud service for processing the operation and maintenance task from multiple candidate cloud services based on the operation and maintenance task. Accordingly, the application program interface (API) exposed by the target cloud service is determined as the target interface for invocation. The pre-trained large language model for implementing the operation and maintenance task processing method can also select a target interface. Specifically, after generating an operation and maintenance operation instruction based on text information, the model then matches a suitable interface from multiple candidate interfaces as the target interface based on the execution of the operation and maintenance operation. Thus, the operation and maintenance task processing method provided in the embodiments of the present application also automatically selects a target cloud service or target interface. In an optional embodiment, in step S23, invoking the target interface to issue the operation and maintenance operation instruction to the target cloud service includes the following method steps: Step S231: In response to the operation and maintenance operation instruction containing a risk instruction, generating a secondary confirmation request based on the risk instruction; Step S232: In response to the secondary confirmation result corresponding to the secondary confirmation request, invoking the target interface to issue the operation and maintenance operation instruction to the target cloud service. In this optional embodiment, the secondary confirmation request is used to confirm with the user whether to execute the risk instruction. If the user confirms that they still want to execute the risky instruction, a secondary confirmation result will be issued in response to the secondary confirmation request. Upon detecting the secondary confirmation result, the target interface is automatically invoked to execute the aforementioned operation and maintenance instruction. As an exemplary embodiment, in this application scenario, the automated operation and maintenance task processing process is shown in Figure 3. A pre-trained large language model (such as chatGPT) is used to assist in completing the operation and maintenance task. Specifically, the user enters a text description into the large language model and then responds to a one-click run operation trigger. The large language model performs semantic analysis on the text description to determine the multiple operation steps of the operation and maintenance task the user wishes to execute (steps 1, 2, and 3 in this example). Furthermore, the large language model determines whether any of the multiple operation steps contain high-risk operations. If a high-risk operation is found, the user is asked for secondary confirmation to confirm whether the high-risk operation should still be executed. If the user confirms execution, the target interface exposed by the target cloud service is invoked to execute the operation and maintenance task. If the user does not confirm the high-risk operation (for example, if the user cancels the operation and maintenance task or the waiting time expires), the operation and maintenance task processing process ends. If no high-risk operations are found in the multiple operation steps, the target interface exposed by the target cloud service is directly called to perform the operation and maintenance task. It should be noted that the risk assessment for high-risk operations can be performed directly on the description information of the multiple operation steps, or on the corresponding operation instructions of the multiple operation steps.In other words, because the large language model possesses natural language processing capabilities, it can process the operation and maintenance task information expressed in natural language before calling the target cloud service's exposed target interface. After converting the multiple operation and maintenance operations in the operation and maintenance task into machine-readable operation and maintenance instructions, the target cloud service's exposed target interface is called and delivered to the target cloud service. For example, if a user enters the text description "Help me create a Linux-based ECS instance, then install GIT on this instance, and reboot after installation" and clicks the "Run" button, the large language model will automatically perform operations such as semantic understanding of the text description, risk verification, interface selection, and operation and maintenance task execution. This will cause the system to automatically create a Linux-based Elastic Compute Service (ECS) instance, install the distributed version control system (Global Information Tracker, GIT) on this instance, and reboot after installation, thus completing the operation and maintenance task described by the user in natural language. In an embodiment of the present application, a method for processing an operation and maintenance task is further provided, wherein a graphical user interface is provided through a terminal device, and content displayed by the graphical user interface at least partially includes an operation and maintenance task processing scenario. The operation and maintenance task processing method further includes the following method steps: Step S261, in response to a first touch operation applied to the graphical user interface, obtaining text information, wherein the text information is used to describe the operation and maintenance task to be executed in a natural language; Step S262, in response to a second touch operation applied to the graphical user interface, performing semantic analysis on the text information, determining an operation and maintenance operation instruction corresponding to the operation and maintenance task, calling a target interface to issue the operation and maintenance operation instruction to a target cloud service, and obtaining a target operation and maintenance result, wherein the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction; Step S263, displaying the target operation and maintenance result in the graphical user interface. According to the operation and maintenance task processing method provided in an embodiment of the present application, a cloud-based intelligent operation and maintenance assistant with operation and maintenance task processing capabilities is implemented. The graphical user interface of this cloud-based intelligent operation and maintenance assistant is shown in FIG4 . The graphical user interface displays a user interaction interface corresponding to the operation and maintenance task processing scenario. The text input box within the operation interface supports text typing and voice input. As shown in FIG4 , the first touch operation can be an operation in which a user enters text (including text typing and / or voice data) through the text input box. When the first touch operation is detected, the text information entered in the input box is obtained, and this text information is used to describe the operation and maintenance task to be executed.As shown in Figure 4, the second touch operation can be a user clicking an "Execute" button. This second touch operation triggers the cloud-based intelligent operation and maintenance assistant to automatically execute the operation and maintenance task processing method to obtain the target operation and maintenance result. Furthermore, after obtaining the target operation and maintenance result, the target operation and maintenance result is displayed on a graphical user interface. Alternatively, log data or an operation and maintenance report is automatically generated based on the target operation and maintenance result, and the log data or operation and maintenance report of the target operation and maintenance result is displayed on a graphical user interface. In an optional embodiment, the operation and maintenance task processing method further includes the following method steps: Step S264: In response to the operation and maintenance operation instruction including a risk instruction, displaying a secondary confirmation indicator corresponding to the risk instruction in the graphical user interface; Step S265: In response to a third touch operation acting on the secondary confirmation indicator, obtaining the target operation and maintenance result. As an exemplary embodiment, when the cloud-based operation and maintenance intelligent assistant detects that the operation and maintenance operation instructions corresponding to the user input text contain risk instructions, a secondary confirmation view is displayed within the graphical user interface, as shown in FIG5 . Based on the risk instruction "Change Password" and a reminder template, a reminder message is generated. The reminder template reads, "Attention! The operation and maintenance task you entered contains the following risky operation: [xxxx] Please confirm whether you still want to execute it?" The reminder message is generated by replacing the placeholder "xxxx" in the reminder template with the operation and maintenance operation corresponding to the risky instruction. The cloud-based operation and maintenance intelligent assistant displays the secondary confirmation window (also known as the secondary confirmation indicator) shown in FIG5 . The third touch operation is the user clicking the "Confirm" button. When the user clicks the "Confirm" button, the user confirms that the risky instruction will continue to be executed and the target operation and maintenance result will be obtained. If the user clicks the "I'll Think About It" button, the operation and maintenance task processing process is terminated. It should be noted that the first, second, and third touch operations can all be operations in which the user touches the display screen of the terminal device with a finger and then touches the terminal device. The touch operation may include single-point touch or multi-point touch, wherein the touch operation at each touch point may include click, long press, hard press, swipe, etc. The first touch operation, the second touch operation, and the third touch operation may also be touch operations performed via input devices such as a mouse and keyboard. Through the above embodiments, the present application provides a visual implementation of an operation and maintenance task processing method. This visual implementation can be implemented on a client to enable users to conveniently input description text for an operation and maintenance task and control the start of the operation and maintenance task processing process. This method is user-friendly, convenient, and provides a good user experience.It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, storage, and display, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. The collection, use, and processing of the relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation portals are provided for the user to choose to authorize or reject. It should be noted that for the sake of simplicity, the aforementioned method embodiments are described as a series of actions. However, those skilled in the art should be aware that this application is not limited by the order of the actions described, as certain steps can be performed in a different order or simultaneously according to this application. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application. Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented using software and a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, can essentially be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk) and includes instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of this application. Example 2: In the operating environment described in Example 1, this application provides another operation and maintenance task processing method, as shown in Figure 6. Figure 6 is a flowchart of a method for processing an operation and maintenance task according to Example 2 of the present application. As shown in Figure 6, the operation and maintenance task processing method includes: Step S61, obtaining text information, wherein the text information is used to describe the e-commerce operation and maintenance task to be executed in natural language; Step S62, performing semantic analysis on the text information to determine the e-commerce operation and maintenance operation instruction corresponding to the e-commerce operation and maintenance task; Step S63, invoking a target interface to issue the e-commerce operation and maintenance operation instruction to a target e-commerce cloud service, and obtaining a target e-commerce operation and maintenance result, wherein the target interface is the e-commerce cloud service interface corresponding to the e-commerce operation and maintenance task, and the target e-commerce operation and maintenance result is the e-commerce operation and maintenance result obtained after the target e-commerce cloud service executes the e-commerce operation and maintenance operation instruction. The above-described e-commerce operation and maintenance task processing method can also be used to provide automated operation and maintenance services for specific e-commerce operation and maintenance scenarios.Correspondingly, a large language model specifically designed for e-commerce operations and maintenance scenarios can be trained using machine learning methods using training data collected in e-commerce operations and maintenance scenarios to perform semantic analysis on text information to implement e-commerce operations and maintenance task processing. This large language model can demonstrate high performance in processing specific tasks in e-commerce operations and maintenance scenarios. The text information can be obtained directly from user input, or by converting images, audio, or video input into text. Specifically, in an application scenario, when a user wishes to perform an e-commerce operations and maintenance task, they describe the task in natural language on a user terminal through text typing, voice input, or other methods. The user terminal then executes the e-commerce operations and maintenance task processing method provided in the embodiments of this application to obtain the text information. In one exemplary application scenario, an artificial intelligence model with natural language processing capabilities is used to perform semantic analysis on the text information to determine the e-commerce operations and maintenance operation instructions corresponding to the e-commerce operations and maintenance task. The artificial intelligence model can be a pre-trained large language model. The large language model can also determine the target interface corresponding to the described e-commerce operations and maintenance task based on the text information, trigger the execution of the e-commerce operations and maintenance operation instructions, and obtain the target e-commerce operations and maintenance results. In an exemplary application scenario, a semantic analysis algorithm is used to perform semantic analysis on text information, and an instruction generation algorithm is used to generate corresponding e-commerce operations and maintenance instructions based on the semantic analysis results. The semantic analysis algorithm and instruction generation algorithm can be tailored to the specific application scenario of the e-commerce operations and maintenance task. For example, the semantic analysis algorithm can be a rule-based semantic analysis algorithm (such as logical reasoning rules or grammatical analysis rules), a corpus statistics-based semantic analysis algorithm, a graph-based semantic analysis algorithm, or a knowledge graph-based semantic analysis algorithm. The instruction generation algorithm can be an algorithm that generates e-commerce operations and maintenance instructions based on an instruction template library and semantic analysis results specific to the e-commerce operations and maintenance task application scenario. The target e-commerce cloud service is a cloud service from multiple candidate cloud services that matches the e-commerce operations and maintenance task. The multiple candidate cloud services are service-side products that expose APIs for the e-commerce operations and maintenance scenario, with each candidate cloud service exposing at least one API (i.e., multiple candidate interfaces are exposed to the e-commerce operations and maintenance product). When it is necessary to control the target e-commerce cloud service to execute the e-commerce operation and maintenance instructions, the target interface exposed by the target e-commerce cloud service is called, and the e-commerce operation and maintenance instructions are issued to the target e-commerce cloud service to control the target e-commerce cloud service to execute the e-commerce operation and maintenance instructions and obtain the target e-commerce operation and maintenance results. It is easy to understand that after the user enters the description text of the e-commerce operation and maintenance task, the e-commerce operation and maintenance task processing process does not require human intervention, making the operation easy, user-friendly, highly automated, and highly efficient.It should be noted that the aforementioned e-commerce operation and maintenance tasks can be deployed on the cloud. The e-commerce operation and maintenance task processing method provided in the embodiments of this application can be run on a system consisting of a client device and a server. The server can be a standalone server or a distributed server cluster, and the server can be located in the cloud. The client device sends a text message to the server. The server executes the steps corresponding to the e-commerce operation and maintenance task processing method, performs semantic analysis on the text message, determines the e-commerce operation and maintenance operation instructions corresponding to the e-commerce operation and maintenance task, invokes a target interface to issue the e-commerce operation and maintenance operation instructions to the target e-commerce cloud service, obtains the target e-commerce operation and maintenance results, and returns the target e-commerce operation and maintenance results to the client. It should be noted that if the operating resources of the client device can meet the deployment and operation requirements of the large model, the embodiments of this application can be performed on the client device. It is easy to understand that the e-commerce operation and maintenance task processing method provided in the embodiments of this application applies artificial intelligence natural language processing technology to automated operation and maintenance scenarios. Automated cloud operation and maintenance is performed through natural language conversational interaction, lowering the threshold for cloud operation and maintenance, improving the efficiency of automated cloud operation and maintenance, and improving the operation and maintenance experience of operation and maintenance personnel. In an embodiment of the present application, text information is obtained. This text information is used to describe an e-commerce operation and maintenance task to be executed in natural language. Semantic analysis is then performed on the text information to determine the e-commerce operation and maintenance instructions corresponding to the e-commerce operation and maintenance task. A target interface is then called to issue the e-commerce operation and maintenance instructions to a target e-commerce cloud service, and a target e-commerce operation and maintenance result is obtained. The target interface is the e-commerce cloud service interface corresponding to the e-commerce operation and maintenance task, and the target e-commerce operation and maintenance result is the e-commerce operation and maintenance result obtained after the target e-commerce cloud service executes the e-commerce operation and maintenance instruction. In this application scenario, a large language model can be used to analyze text information expressed in natural language to determine the user's operation and maintenance intent. This achieves the goal of automatically determining the e-commerce operation and maintenance instructions in the e-commerce operation and maintenance task based on natural language input, thereby automatically completing the e-commerce operation and maintenance task. This reduces user operational difficulty and improves operational efficiency in processing e-commerce operation and maintenance tasks, thereby addressing the high learning cost, operational difficulty, and low efficiency of defining operation and maintenance steps using operation and maintenance templates.In an optional embodiment, a graphical user interface is provided by a terminal device, and the content displayed by the graphical user interface at least partially includes an e-commerce operation and maintenance task processing scenario. The operation and maintenance task processing method further includes the following method steps: Step S641: In response to a first touch operation on the graphical user interface, obtaining text information entered in an input box; Step S642: In response to a second touch operation on the graphical user interface, performing semantic analysis on the text information, determining an e-commerce operation and maintenance operation instruction corresponding to the e-commerce operation and maintenance task, invoking a target interface to issue the e-commerce operation and maintenance operation instruction to a target e-commerce cloud service, and obtaining a target e-commerce operation and maintenance result; Step S643: Displaying the target e-commerce operation and maintenance result in the graphical user interface. The e-commerce operation and maintenance task processing method provided in this embodiment of the application implements an e-commerce operation and maintenance intelligent assistant with e-commerce operation and maintenance task processing capabilities. The e-commerce operation and maintenance intelligent assistant provides a graphical user interface similar to those shown in Figures 4 and 5. The graphical user interface displays a user interaction interface corresponding to the e-commerce operation and maintenance task processing scenario. The text input box in the operation interface supports text typing and voice input. The first touch operation can be an operation in which a user enters text (including text typing and / or voice data) through a text input box in an e-commerce operation and maintenance task processing scenario. When the first touch operation is detected, the text information entered in the input box is retrieved, and this text information is used to describe the e-commerce operation and maintenance task to be executed. The second touch operation can be an operation in which a user clicks an "Execute" button in an e-commerce operation and maintenance task processing scenario. This second touch operation triggers the e-commerce operation and maintenance intelligent assistant to automatically execute the e-commerce operation and maintenance task processing method to obtain a target e-commerce operation and maintenance result. Furthermore, after obtaining the target e-commerce operation and maintenance result, the target e-commerce operation and maintenance result is displayed on a graphical user interface. Alternatively, e-commerce operation and maintenance log data or an e-commerce operation and maintenance report is automatically generated based on the target e-commerce operation and maintenance result, and the e-commerce operation and maintenance log data or e-commerce operation and maintenance report of the target e-commerce operation and maintenance result is displayed on a graphical user interface. It should be noted that both the first touch operation and the second touch operation can be operations in which a user touches the display screen of the terminal device with a finger, thereby touching the terminal device. The touch operation may include single-point touch or multi-point touch, wherein the touch operation of each touch point may include click, long press, hard press, swipe, etc. The first touch operation and the second touch operation may also be touch operations implemented through input devices such as a mouse and a keyboard.Through the above-described embodiments, this application provides a visual implementation of an e-commerce operation and maintenance task processing method. This visual implementation can be implemented on a client to allow users to conveniently input e-commerce operation and maintenance task descriptions and control the start of the e-commerce operation and maintenance task processing process. This method is user-friendly and convenient, providing a good user experience. It should be noted that the preferred implementation of this embodiment can be found in the relevant description of Example 1 and will not be repeated here. Example 3: In the operating environment of Example 1, this application provides another operation and maintenance task processing method as shown in Figure 7. Figure 7 is a flowchart of a method for processing an operation and maintenance task according to Example 3 of the present application. As shown in Figure 7, the operation and maintenance task processing method includes: Step S71: obtaining text information, wherein the text information is used to describe the logistics operation and maintenance task to be executed in natural language; Step S72: performing semantic analysis on the text information to determine the logistics operation and maintenance operation instructions corresponding to the logistics operation and maintenance task; Step S73: invoking a target interface to issue the logistics operation and maintenance operation instructions to a target logistics cloud service and obtain a target logistics operation and maintenance result, wherein the target interface is the logistics cloud service interface corresponding to the logistics operation and maintenance task, and the target logistics operation and maintenance result is the logistics operation and maintenance result obtained after the target logistics cloud service executes the logistics operation and maintenance operation instruction. The above-described logistics operation and maintenance task processing method can also be used to provide automated operation and maintenance services for specific logistics operation and maintenance scenarios. Correspondingly, a large language model dedicated to logistics operation and maintenance scenarios can be trained using machine learning methods using training data collected from logistics operation and maintenance scenarios to perform semantic analysis on the text information to implement logistics operation and maintenance task processing. This large language model can demonstrate high performance in processing specific tasks in logistics operation and maintenance scenarios. The above-mentioned text information can be obtained by directly acquiring user-entered text or by converting images, audio, or video input into text. Specifically, in an application scenario, when a user wishes to perform a logistics operation and maintenance task, they describe the task in natural language on a user terminal through text typing, voice input, or other methods. The user terminal then executes the logistics operation and maintenance task processing method described in the embodiments of this application to obtain the above-mentioned text information. In one exemplary application scenario, an artificial intelligence model with natural language processing capabilities is used to perform semantic analysis on the text information to determine the logistics operation and maintenance operation instructions corresponding to the logistics operation and maintenance task. The above-mentioned artificial intelligence model can be a pre-trained large language model. The large language model can also determine the target interface corresponding to the described logistics operation and maintenance task based on the text information, trigger the execution of the logistics operation and maintenance operation instructions, and obtain the target logistics operation and maintenance result.In an exemplary application scenario, a semantic analysis algorithm is used to perform semantic analysis on text information, and an instruction generation algorithm is used to generate corresponding logistics operations and maintenance instructions based on the semantic analysis results. These semantic analysis and instruction generation algorithms can be tailored to the specific application scenario of the logistics operations and maintenance task. For example, the semantic analysis algorithm can be a rule-based semantic analysis algorithm (such as logical reasoning rules or grammatical analysis rules), a corpus statistics-based semantic analysis algorithm, a graph-based semantic analysis algorithm, or a knowledge graph-based semantic analysis algorithm. The instruction generation algorithm can be an algorithm that generates logistics operations and maintenance instructions based on an instruction template library and semantic analysis results specific to the logistics operations and maintenance task application scenario. The target logistics cloud service is a cloud service from multiple candidate cloud services that matches the logistics operations and maintenance task. The multiple candidate cloud services are service-side products that expose APIs for the logistics operations and maintenance scenario, with each candidate cloud service exposing at least one API (i.e., multiple candidate interfaces are exposed to the logistics operations and maintenance product). When it is necessary to control the target logistics cloud service to execute the aforementioned logistics operations and maintenance instructions, the target interface exposed by the target logistics cloud service is called, and the logistics operations and maintenance instructions are issued to the target logistics cloud service, thereby controlling the target logistics cloud service to execute the logistics operations and maintenance instructions and obtain the target logistics operations and maintenance results. It is easy to understand that after the user enters the description text of the logistics operations and maintenance task, the logistics operations and maintenance task processing process does not require human intervention, making the operation simple, user-friendly, highly automated, and highly efficient. It should be noted that the aforementioned logistics operations and maintenance tasks can be deployed in the cloud. The logistics operations and maintenance task processing method provided in the embodiments of the present application can be executed in a system consisting of a client device and a server. The server can be a standalone server or a distributed server cluster, and the server can be located in the cloud. The client device sends a text message to the server. The server executes the steps corresponding to the logistics operation and maintenance task processing method, performs semantic analysis on the text message, determines the logistics operation and maintenance operation instructions corresponding to the logistics operation and maintenance task, calls the target interface to issue the logistics operation and maintenance operation instructions to the target logistics cloud service, obtains the target logistics operation and maintenance results, and returns the target logistics operation and maintenance results to the client. It should be noted that, if the operating resources of the client device meet the deployment and operation requirements of the large model, the embodiments of the present application can be implemented on the client device. It is easy to understand that the logistics operation and maintenance task processing method provided in the embodiments of the present application applies artificial intelligence natural language processing technology to automated operation and maintenance scenarios. This allows for automated cloud operation and maintenance through conversational natural language interaction, lowering the barrier to entry for cloud operation and maintenance, improving the efficiency of automated cloud operation and maintenance, and improving the operational experience for operators.In this embodiment of the present application, text information is obtained. This text information is used to describe a logistics operation and maintenance task to be executed in natural language. Semantic analysis is then performed on the text information to determine the logistics operation and maintenance instructions corresponding to the logistics operation and maintenance task. A target interface is then called to issue the logistics operation and maintenance instructions to a target logistics cloud service, and a target logistics operation and maintenance result is obtained. The target interface is the logistics cloud service interface corresponding to the logistics operation and maintenance task, and the target logistics operation and maintenance result is the logistics operation and maintenance result obtained after the target logistics cloud service executes the logistics operation and maintenance instruction. In this application scenario, a large language model can be used to analyze text information expressed in natural language to understand the user's operation and maintenance intent. This achieves the goal of automatically determining the logistics operation and maintenance instructions in the logistics operation and maintenance task based on natural language input, thereby automatically completing the logistics operation and maintenance task. This reduces user operational difficulty and improves operational efficiency in logistics operation and maintenance task processing, thereby addressing the high learning cost, operational difficulty, and low efficiency of methods that define operation and maintenance steps using operation and maintenance templates. It should be noted that the preferred implementation of this embodiment can be found in the relevant descriptions of Example 1 or Example 2 and will not be repeated here. Example 4 According to an embodiment of the present application, an embodiment of an apparatus for implementing the above-mentioned operation and maintenance task processing method is also provided. Figure 8 is a schematic structural diagram of an operation and maintenance task processing apparatus according to Example 4 of the present application. As shown in Figure 8, the apparatus includes: an acquisition module 801 configured to acquire text information, wherein the text information is used to describe the operation and maintenance task to be executed in natural language; a determination module 802 configured to perform semantic analysis on the text information and determine the operation and maintenance operation instructions corresponding to the operation and maintenance task; and an execution module 803 configured to call a target interface to issue the operation and maintenance operation instructions to a target cloud service and obtain a target operation and maintenance result, wherein the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction. Optionally, the determination module 802 is further configured to perform semantic analysis on the text information using a semantic analysis model to generate an operation and maintenance operation instruction, wherein the operation and maintenance operation instruction is used to trigger the execution of the operation and maintenance operation corresponding to the operation and maintenance task. Optionally, the determination module 802 is further configured to: perform semantic analysis on the text information to obtain semantic information; extract key information from the semantic information to obtain operation descriptions of multiple operation and maintenance operations; and generate operation and maintenance operation instructions based on the operation descriptions.Optionally, in addition to all of the aforementioned modules, the operation and maintenance task processing apparatus further includes: a verification module (not shown in the figure), configured to use a risk verification model to perform risk verification on the operation and maintenance operation instructions, obtaining a verification result, wherein the verification result is used to determine whether the operation and maintenance operation instructions contain risk instructions. Optionally, the verification module is further configured to: perform keyword matching on the operation and maintenance operation instructions based on preset risk keywords to determine whether the operation and maintenance operation instructions contain risk instructions corresponding to the risk keywords, thereby obtaining a verification result; and perform operation comparison on the operation and maintenance operation instructions based on preset risk operation rules to determine whether the operation and maintenance operation instructions contain risk instructions corresponding to the risk operation rules, thereby obtaining a verification result. Optionally, in addition to all of the aforementioned modules, the operation and maintenance task processing apparatus further includes: a selection module (not shown in the figure), configured to select a target cloud service from multiple candidate cloud services based on the operation and maintenance task, and determine the application program interface (API) exposed by the target cloud service as the target interface. Optionally, the execution module 803 is further configured to: in response to the O&M instruction including a risk instruction, generate a secondary confirmation request based on the risk instruction; and in response to the secondary confirmation result corresponding to the secondary confirmation request, invoke a target interface to issue the O&M instruction to the target cloud service. In this embodiment of the present application, the acquisition module acquires text information that uses a natural language description of the O&M task to be executed. The determination module further performs semantic analysis on the text information to determine the O&M instruction corresponding to the O&M task. The execution module then invokes a target interface to issue the O&M instruction to the target cloud service and obtain a target O&M result. The target interface is the cloud service interface corresponding to the O&M task, and the target O&M result is the O&M result obtained after the target cloud service executes the O&M instruction. In application scenarios, a large language model can be used to analyze text information expressed in natural language to obtain the user's operation and maintenance intent. This achieves the goal of automatically determining the operation and maintenance instructions in the operation and maintenance task based on natural language input, thereby automatically completing the operation and maintenance task. This achieves the technical effect of reducing the user's operation difficulty in the operation and maintenance task processing and improving operation and maintenance efficiency. This further solves the technical problems of high learning cost, high operation difficulty, and low efficiency of the method of defining operation and maintenance operation steps using operation and maintenance templates. It should be noted that the acquisition module 801, determination module 802, and execution module 803 described above correspond to steps S21 to S23 in Example 1. The examples and application scenarios implemented by these three modules and the corresponding steps are the same, but are not limited to the content disclosed in Example 1.It should be noted that the above-mentioned modules or units may be hardware components or software components stored in a memory and processed by one or more processors. The above-mentioned modules may also be part of a device and run in the server 10 provided in Example 1. According to an embodiment of the present application, another operation and maintenance task processing device is also provided for implementing visualization of an operation and maintenance task processing solution. The device includes: a first response module for responding to a first touch operation on a graphical user interface and obtaining text information, wherein the text information uses a natural language description of the operation and maintenance task to be executed; a second response module for responding to a second touch operation on the graphical user interface and performing semantic analysis on the text information to determine the operation and maintenance operation instruction corresponding to the operation and maintenance task, invoking a target interface to issue the operation and maintenance operation instruction to a target cloud service, and obtaining a target operation and maintenance result, wherein the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction; and a display module for displaying the target operation and maintenance result in the graphical user interface. Optionally, the operation and maintenance task processing apparatus further includes a third response module configured to: in response to the operation and maintenance instruction including a risk instruction, display a secondary confirmation indicator corresponding to the risk instruction in the graphical user interface; and in response to a third touch operation on the secondary confirmation indicator, obtain a target operation and maintenance result. According to an embodiment of the present application, another embodiment of an apparatus for implementing the operation and maintenance task processing method of the above-mentioned embodiment 2 is also provided. Figure 9 is a structural diagram of another operation and maintenance task processing device according to Example 4 of the present application. As shown in Figure 9, the device includes: an acquisition module 901, configured to acquire text information, wherein the text information is used to describe the e-commerce operation and maintenance task to be executed in natural language; a determination module 902, configured to perform semantic analysis on the text information to determine the e-commerce operation and maintenance operation instruction corresponding to the e-commerce operation and maintenance task; an execution module 903, configured to call a target interface to issue the e-commerce operation and maintenance operation instruction to the target e-commerce cloud service, and obtain a target e-commerce operation and maintenance result, wherein the target interface is the e-commerce cloud service interface corresponding to the e-commerce operation and maintenance task, and the target e-commerce operation and maintenance result is the e-commerce operation and maintenance result obtained after the target e-commerce cloud service executes the e-commerce operation and maintenance operation instruction.Optionally, in addition to all of the aforementioned modules, the operation and maintenance task processing apparatus further includes: a visualization module (not shown), configured to, in response to a first touch operation on a graphical user interface, obtain text information entered into an input box; in response to a second touch operation on the graphical user interface, perform semantic analysis on the text information, determine an e-commerce operation and maintenance instruction corresponding to the e-commerce operation and maintenance task, invoke a target interface to issue the e-commerce operation and maintenance instruction to a target e-commerce cloud service, and obtain a target e-commerce operation and maintenance result; and display the target e-commerce operation and maintenance result within the graphical user interface. It should be noted that the acquisition module 901, determination module 902, and execution module 903 correspond to steps S61 to S63 in Example 2. The examples and application scenarios implemented by these three modules and the corresponding steps are the same, but are not limited to those disclosed in Example 2. It should be noted that the aforementioned modules or units may be hardware or software components stored in a memory and processed by one or more processors. Alternatively, the aforementioned modules may be executed as part of the apparatus on the server 10 provided in Example 1. According to an embodiment of the present application, another device embodiment for implementing the operation and maintenance task processing method of the aforementioned embodiment 3 is also provided. Figure 10 is a schematic structural diagram of another operation and maintenance task processing device according to embodiment 4 of the present application. As shown in Figure 10, the device includes: an acquisition module 1001, configured to acquire text information, wherein the text information is used to describe the logistics operation and maintenance task to be executed in natural language; a determination module 1002, configured to perform semantic analysis on the text information to determine the logistics operation and maintenance operation instructions corresponding to the logistics operation and maintenance task; and an execution module 1003, configured to call a target interface to issue the logistics operation and maintenance operation instructions to a target logistics cloud service, and obtain a target logistics operation and maintenance result, wherein the target interface is the logistics cloud service interface corresponding to the logistics operation and maintenance task, and the target logistics operation and maintenance result is the logistics operation and maintenance result obtained after the target logistics cloud service executes the logistics operation and maintenance operation instruction. It should be noted that the acquisition module 1001, determination module 1002, and execution module 1003 described above correspond to steps S71 to S73 in Example 3. The examples and application scenarios implemented by these three modules and the corresponding steps are the same, but are not limited to the contents disclosed in Example 3. It should be noted that the above modules or units may be hardware components or software components stored in a memory and processed by one or more processors. The above modules may also be part of an apparatus and run in the server 10 provided in Example 1. It should be noted that the preferred implementation of this embodiment can be found in the relevant descriptions in Example 1, Example 2, or Example 3, and will not be repeated here.Example 5 According to an embodiment of the present application, an operation and maintenance task processing system is also provided. The operation and maintenance task processing system can be a computer terminal, which can be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal can be replaced with a terminal device such as a mobile terminal. Optionally, in this embodiment, the computer terminal can be located on at least one of multiple network devices in a computer network. In this embodiment, the computer terminal can execute program code for the following steps in the operation and maintenance task processing method: obtaining text information, where the text information uses natural language to describe the operation and maintenance task to be executed; performing semantic analysis on the text information to determine the operation and maintenance operation instructions corresponding to the operation and maintenance task; invoking a target interface to issue the operation and maintenance operation instructions to a target cloud service, and obtaining a target operation and maintenance result, where the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction. Alternatively, Figure 11 is a block diagram of a computer terminal according to Embodiment 5 of the present application. As shown in Figure 11 , the computer terminal 110 may include one or more processors 1102 (only one is shown), a memory 1104, a storage controller 1106, and a peripheral interface 1108. The peripheral interface 1108 is connected to a radio frequency module, an audio module, and a display. The memory 1104 may be configured to store software programs and modules, such as program instructions / modules corresponding to the operation and maintenance task processing method and apparatus in the embodiments of the present application. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, thereby implementing the aforementioned operation and maintenance task processing method. The memory 1104 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 1104 may further include memory located remotely from the processor. Such remote memory may be connected to the computer terminal 110 via a network. Examples of the above-mentioned network include but are not limited to the Internet, corporate intranet, local area network, mobile communication network and their combinations.Processor 1102 can access information and applications stored in memory via a transmission device to perform the following steps: obtaining text information, where the text information uses natural language to describe the operation and maintenance task to be performed; performing semantic analysis on the text information to determine the operation and maintenance operation instructions corresponding to the operation and maintenance task; invoking a target interface to issue the operation and maintenance operation instructions to a target cloud service, and obtaining a target operation and maintenance result, where the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction. Optionally, processor 1102 can also execute program code for the following steps: using a semantic analysis model to perform semantic analysis on the text information and generate operation and maintenance operation instructions, where the operation and maintenance operation instructions are used to trigger the execution of the operation and maintenance operation corresponding to the operation and maintenance task. Optionally, processor 1102 can also execute program code for the following steps: performing semantic analysis on the text information to obtain semantic information; extracting key information from the semantic information to obtain operation descriptions of multiple operation and maintenance operations; and generating operation and maintenance operation instructions based on the operation descriptions. Optionally, the processor 1102 may further execute program code for the following steps: using a risk verification model to perform risk verification on the operation and maintenance instructions, obtaining verification results, wherein the verification results are used to determine whether the operation and maintenance instructions contain risk instructions. Optionally, the processor 1102 may further execute program code for the following steps: performing keyword matching on the operation and maintenance instructions based on preset risk keywords to determine whether the operation and maintenance instructions contain risk instructions corresponding to the obtained risk keywords, obtaining verification results; performing operation comparison on the operation and maintenance instructions based on preset risk operation rules to determine whether the operation and maintenance instructions contain risk instructions corresponding to the obtained risk operation rules, obtaining verification results. Optionally, the processor 1102 may further execute program code for the following steps: selecting a target cloud service from multiple candidate cloud services based on the operation and maintenance task, and determining an application program interface (API) exposed by the target cloud service as a target interface. Optionally, the processor 1102 may further execute program code of the following steps: in response to the operation and maintenance instruction including a risk instruction, generating a secondary confirmation request based on the risk instruction; in response to the secondary confirmation result corresponding to the secondary confirmation request, calling the target interface to send the operation and maintenance instruction to the target cloud service.Optionally, the processor 1102 may further execute program code for the following steps: in response to a first touch operation on the graphical user interface, obtaining text information, wherein the text information is used to describe the operation and maintenance task to be executed in natural language; in response to a second touch operation on the graphical user interface, performing semantic analysis on the text information to determine the operation and maintenance operation instruction corresponding to the operation and maintenance task, invoking a target interface to issue the operation and maintenance operation instruction to a target cloud service, and obtaining a target operation and maintenance result, wherein the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction; and displaying the target operation and maintenance result in the graphical user interface. Optionally, the processor 1102 may further execute program code for the following steps: in response to the operation and maintenance operation instruction including a risk instruction, displaying a secondary confirmation indicator corresponding to the risk instruction in the graphical user interface; and in response to a third touch operation on the secondary confirmation indicator, obtaining the target operation and maintenance result. The processor 1102 can access information and applications stored in the memory through a transmission device to perform the following steps: obtaining text information, wherein the text information is used to describe the e-commerce operation and maintenance task to be executed in natural language; performing semantic analysis on the text information to determine the e-commerce operation and maintenance operation instructions corresponding to the e-commerce operation and maintenance task; invoking a target interface to issue the e-commerce operation and maintenance operation instructions to a target e-commerce cloud service, and obtaining a target e-commerce operation and maintenance result, wherein the target interface is the e-commerce cloud service interface corresponding to the e-commerce operation and maintenance task, and the target e-commerce operation and maintenance result is the e-commerce operation and maintenance result obtained after the target e-commerce cloud service executes the e-commerce operation and maintenance operation instruction. Optionally, the processor 1102 can also execute program code for the following steps: in response to a first touch operation on a graphical user interface, obtaining text information entered in an input box; in response to a second touch operation on the graphical user interface, performing semantic analysis on the text information to determine the e-commerce operation and maintenance operation instructions corresponding to the e-commerce operation and maintenance task; invoking the target interface to issue the e-commerce operation and maintenance operation instructions to the target e-commerce cloud service, and obtaining the target e-commerce operation and maintenance result; and displaying the target e-commerce operation and maintenance result in the graphical user interface. The processor 1102 can call the information and application programs stored in the memory through the transmission device to perform the following steps: obtaining text information, wherein the text information is used to describe the logistics operation and maintenance task to be executed in natural language; performing semantic analysis on the text information to determine the logistics operation and maintenance operation instructions corresponding to the logistics operation and maintenance task; calling the target interface to issue the logistics operation and maintenance operation instructions to the target logistics cloud service, and obtaining the target logistics operation and maintenance result, wherein the target interface is the logistics cloud service interface corresponding to the logistics operation and maintenance task, and the target logistics operation and maintenance result is the logistics operation and maintenance result obtained after the target logistics cloud service executes the logistics operation and maintenance operation instructions.An embodiment of the present application provides a computer terminal solution for implementing the aforementioned operation and maintenance task processing method. The method obtains text information that uses natural language to describe the operation and maintenance task to be executed. The text information is further semantically analyzed to determine the operation and maintenance instructions corresponding to the operation and maintenance task. A target interface is then called to issue the operation and maintenance instructions to a target cloud service, and a target operation and maintenance result is obtained. The target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance instruction. In application scenarios, a large language model can be used to analyze text information expressed in natural language to determine the user's operation and maintenance intent. This achieves the goal of automatically determining the operation and maintenance instructions in the operation and maintenance task based on natural language input, thereby automatically completing the operation and maintenance task. This reduces user operational difficulty and improves operational efficiency in operation and maintenance task processing, thereby addressing the high learning cost, operational difficulty, and low efficiency of methods that define operation and maintenance steps using operation and maintenance templates. Those skilled in the art will appreciate that the structure shown in FIG11 is merely illustrative, and the computer terminal may also be a terminal device such as a smartphone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a PDA, or a mobile internet device (MID). FIG11 does not limit the structure of the computer terminal described above. For example, the computer terminal 110 may include more or fewer components (e.g., a network interface, a display device, etc.) than those shown in FIG11 , or may have a configuration different from that shown in FIG11 . Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by a program instructing the hardware associated with the terminal device. The program may be stored in a computer-readable storage medium, which may include a flash drive, ROM, RAM, a magnetic disk, or an optical disk. Example 6 According to an embodiment of the present application, a computer-readable storage medium is also provided. Optionally, in this embodiment, the storage medium may be configured to store the program code executed by the operation and maintenance task processing method provided in Example 1, Example 2, or Example 3. Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining text information, wherein the text information is used to describe an operation and maintenance task to be executed in natural language; performing semantic analysis on the text information to determine the operation and maintenance operation instructions corresponding to the operation and maintenance task; invoking a target interface to issue the operation and maintenance operation instructions to a target cloud service, and obtaining a target operation and maintenance result, wherein the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: performing semantic analysis on the text information using a semantic analysis model to generate an operation and maintenance operation instruction, wherein the operation and maintenance operation instruction is used to trigger the execution of the operation and maintenance operation corresponding to the operation and maintenance task. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: performing semantic analysis on the text information to obtain semantic information; extracting key information from the semantic information to obtain operation descriptions of multiple operation and maintenance operations; and generating an operation and maintenance operation instruction based on the operation descriptions. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: using a risk verification model to perform risk verification on the operation and maintenance instructions to obtain verification results, wherein the verification results are used to determine whether the operation and maintenance instructions contain risk instructions. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: performing keyword matching on the operation and maintenance instructions based on preset risk keywords to determine whether the operation and maintenance instructions contain risk instructions corresponding to the obtained risk keywords, thereby obtaining verification results; performing operation comparison on the operation and maintenance instructions based on preset risk operation rules to determine whether the operation and maintenance instructions contain risk instructions corresponding to the obtained risk operation rules, thereby obtaining verification results. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: selecting a target cloud service from multiple candidate cloud services based on the operation and maintenance task, and determining an application program interface (API) exposed by the target cloud service as a target interface. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: in response to the operation and maintenance operation instruction including a risk instruction, generating a secondary confirmation request based on the risk instruction; in response to the secondary confirmation result corresponding to the secondary confirmation request, calling the target interface to issue the operation and maintenance operation instruction to the target cloud service.Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: in response to a first touch operation on a graphical user interface, obtaining text information, wherein the text information uses a natural language description of an operation and maintenance task to be performed; in response to a second touch operation on the graphical user interface, performing semantic analysis on the text information to determine an operation and maintenance instruction corresponding to the operation and maintenance task, invoking a target interface to issue the operation and maintenance instruction to a target cloud service, and obtaining a target operation and maintenance result, wherein the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance instruction; and displaying the target operation and maintenance result in the graphical user interface. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: in response to the operation and maintenance instruction including a risk instruction, displaying a secondary confirmation indicator corresponding to the risk instruction in the graphical user interface; and in response to a third touch operation on the secondary confirmation indicator, obtaining the target operation and maintenance result. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining text information, wherein the text information is used to describe the e-commerce operation and maintenance task to be executed in natural language; performing semantic analysis on the text information to determine the e-commerce operation and maintenance operation instruction corresponding to the e-commerce operation and maintenance task; calling a target interface to issue the e-commerce operation and maintenance operation instruction to a target e-commerce cloud service, and obtaining a target e-commerce operation and maintenance result, wherein the target interface is the e-commerce cloud service interface corresponding to the e-commerce operation and maintenance task, and the target e-commerce operation and maintenance result is the e-commerce operation and maintenance result obtained after the target e-commerce cloud service executes the e-commerce operation and maintenance operation instruction. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: in response to a first touch operation applied to the graphical user interface, obtaining text information input into the input box; in response to a second touch operation applied to the graphical user interface, performing semantic analysis on the text information, determining an e-commerce operation and maintenance operation instruction corresponding to the e-commerce operation and maintenance task, calling a target interface to issue the e-commerce operation and maintenance operation instruction to a target e-commerce cloud service, and obtaining a target e-commerce operation and maintenance result; and displaying the target e-commerce operation and maintenance result in the graphical user interface.Optionally, in this embodiment, a computer-readable storage medium is configured to store program code for executing the following steps: obtaining text information, wherein the text information uses natural language to describe a logistics operation and maintenance task to be executed; performing semantic analysis on the text information to determine the logistics operation and maintenance operation instructions corresponding to the logistics operation and maintenance task; and invoking a target interface to issue the logistics operation and maintenance operation instructions to a target logistics cloud service and obtain a target logistics operation and maintenance result, wherein the target interface is the logistics cloud service interface corresponding to the logistics operation and maintenance task, and the target logistics operation and maintenance result is the logistics operation and maintenance result obtained after the target logistics cloud service executes the logistics operation and maintenance operation instruction. This embodiment of the present application provides a computer-readable storage medium solution for implementing the aforementioned operation and maintenance task processing method. The system obtains text information that uses natural language to describe an operation and maintenance task to be performed. It then performs semantic analysis on the text information to determine the operation and maintenance instructions corresponding to the operation and maintenance task. It then calls a target interface to issue the operation and maintenance instructions to a target cloud service, and obtains a target operation and maintenance result. The target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance instruction. In application scenarios, a large language model can be used to analyze text information expressed in natural language to determine the user's operation and maintenance intent. This achieves the goal of automatically determining the operation and maintenance instructions in the operation and maintenance task based on natural language input, thereby automatically completing the operation and maintenance task. This reduces user operational difficulty and improves operational efficiency in operation and maintenance task processing, thereby addressing the high learning cost, operational difficulty, and low efficiency of methods that define operation and maintenance steps using operation and maintenance templates. According to embodiments of the present application, a computer program product is also provided, comprising a computer program that, when executed by a processor, implements the aforementioned operation and maintenance task processing method. It should be noted that the program code used to implement the operation and maintenance task processing method of the present application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server. The serial numbers of the above-mentioned embodiments of the present application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the above-mentioned embodiments of the present application, the description of each embodiment is repeated. For portions not described in detail in one embodiment, reference can be made to the relevant descriptions of other embodiments.In the several embodiments provided herein, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, other divisions may be employed. For example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be through interfaces, or indirect couplings or communication connections between units or modules, and may be electrical or other forms. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of these units may be selected to achieve the objectives of the present embodiments based on actual needs. Furthermore, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units can be implemented in either hardware or software functional units. If implemented as software functional units and sold or used as standalone products, the integrated units can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to perform all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, ROM, RAM, mobile hard drives, magnetic disks, or optical disks. The above description is merely a preferred embodiment of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and such improvements and modifications should be considered within the scope of protection of this application.

Claims

Claims 1. An operation and maintenance task processing method, wherein, Including: Obtain text information, where the text information is used to describe the operation and maintenance task to be executed in natural language; perform semantic analysis on the text information to determine the operation and maintenance operation instruction corresponding to the operation and maintenance task; call the target interface to send the operation and maintenance operation instruction to the target cloud service, and obtain the target operation and maintenance result, where the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction.

2. The operation and maintenance task processing method according to claim 1, performing semantic analysis on the text information to determine the operation and maintenance operation instructions corresponding to the operation and maintenance task, includes: Use a semantic analysis model to perform semantic analysis on the text information to generate the operation and maintenance operation instruction, where the operation and maintenance operation instruction is used to trigger the execution of the operation and maintenance operation corresponding to the operation and maintenance task.

3. The operation and maintenance task processing method according to claim 2, performing semantic analysis on the text information to generate the operation and maintenance operation instruction includes: Perform semantic analysis on the text information to obtain semantic information; Extract key information from the semantic information to obtain the operation description of the operation and maintenance operation; Generate the operation and maintenance operation instruction based on the operation description.

4. The operation and maintenance task processing method according to claim 1, the operation and maintenance task processing method further comprising: Use a risk verification model to perform risk verification on the operation and maintenance operation instruction to obtain a verification result, where the verification result is used to determine whether the operation and maintenance operation instruction contains a risk instruction.

5. According to the operation and maintenance task processing method described in claim 4, performing risk verification on the operation and maintenance operation instruction to obtain the verification result includes at least one of the following: performing keyword matching on the operation and maintenance operation instruction based on a preset risk keyword to determine whether the operation and maintenance operation instruction contains the risk instruction corresponding to the obtained risk keyword to obtain the verification result; performing operation comparison on the operation and maintenance operation instruction based on a preset risk operation rule to determine whether the operation and maintenance operation instruction contains the risk instruction corresponding to the obtained risk operation rule to obtain the verification result.

6. The method for processing operation and maintenance tasks according to claim 5, wherein invoking the target interface to send the operation and maintenance operation instruction to the target cloud service includes: In response to the risk instruction being included in the operation and maintenance operation instruction, generate a secondary confirmation request based on the risk instruction; In response to the secondary confirmation result corresponding to the secondary confirmation request, call the target interface to send the operation and maintenance operation instruction to the target 26 target cloud service.

7. The operation and maintenance task processing method according to claim 1, wherein the operation and maintenance task processing method further includes: Select the target cloud service from multiple candidate cloud services according to the operation and maintenance task, and determine that the application program interface opened by the target cloud service is the target interface.

8. A method for processing operation and maintenance tasks, wherein, Provide a graphical user interface through a terminal device. The content displayed on the graphical user interface at least partially includes an operation and maintenance task processing scenario. The operation and maintenance task processing method includes: responding to a first touch operation on the graphical user interface to obtain text information, where the text information is used to describe the operation and maintenance task to be executed in natural language; responding to a second touch operation on the graphical user interface to perform semantic analysis on the text information, determine the operation and maintenance operation instruction corresponding to the operation and maintenance task, call a target interface to send the operation and maintenance operation instruction to a target cloud service, and obtain a target operation and maintenance result, where the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instruction; display the target operation and maintenance result within the graphical user interface.

9. The operation and maintenance task processing method according to claim 8, wherein the operation and maintenance task processing method further comprises: In response to the risk instruction being included in the operation and maintenance operation instruction, display a secondary confirmation identifier corresponding to the risk instruction within the graphical user interface; Respond to a third touch operation on the secondary confirmation identifier to obtain the target operation and maintenance result.

10. A method for processing operation and maintenance tasks, wherein, Include: Obtain text information, where the text information is used to describe the e-commerce operation and maintenance task to be executed in natural language; perform semantic analysis on the text information to determine the e-commerce operation and maintenance operation instruction corresponding to the e-commerce operation and maintenance task; call a target interface to send the e-commerce operation and maintenance operation instruction to a target e-commerce cloud service, and obtain a target e-commerce operation and maintenance result, where the target interface is the e-commerce cloud service interface corresponding to the e-commerce operation and maintenance task, and the target e-commerce operation and maintenance result is the e-commerce operation and maintenance result obtained after the target e-commerce cloud service executes the e-commerce operation and maintenance operation instruction.

11. The operation and maintenance task processing method according to claim 10, provides a graphical user interface through a terminal device, and the content displayed by the graphical user interface at least partially includes an e-commerce operation and maintenance task processing scenario. The operation and maintenance task processing method further includes: Respond to a first touch operation on the graphical user interface to obtain the Text information entered in the input box; respond to a second touch operation on the graphical user interface to perform semantic analysis on the text information, determine the e-commerce operation and maintenance operation instruction corresponding to the e-commerce operation and maintenance task, call the target interface to send the e-commerce operation and maintenance operation instruction to the target e-commerce cloud service, and obtain the target e-commerce operation and maintenance result; display the target e-commerce operation and maintenance result within the graphical user interface.

12. A method for processing operation and maintenance tasks, wherein, Include: Obtain text information, where the text information is used to describe the logistics operation and maintenance task to be executed in natural language; perform semantic analysis on the text information to determine the logistics operation and maintenance operation instruction corresponding to the logistics operation and maintenance task; call a target interface to send the logistics operation and maintenance operation instruction to a target logistics cloud service, and obtain a target logistics operation and maintenance result, where the target interface is the logistics cloud service interface corresponding to the logistics operation and maintenance task, and the target logistics operation and maintenance result is the logistics operation and maintenance result obtained after the target logistics cloud service executes the logistics operation and maintenance operation instruction.

13. A computer-readable storage medium, wherein, The computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the operation and maintenance task processing method described in any one of claims 1 to 12.

14. An operation and maintenance task processing system, wherein, Comprising: a processor; and a memory, connected to the processor and configured to provide instructions for the processor to process the following steps: obtaining text information, wherein the text information describes the operation and maintenance task to be executed in natural language; performing semantic analysis on the text information to determine the operation and maintenance operation instructions corresponding to the operation and maintenance task; calling a target interface to send the operation and maintenance operation instructions to a target cloud service, and obtaining a target operation and maintenance result, wherein the target interface is the cloud service interface corresponding to the operation and maintenance task, and the target operation and maintenance result is the operation and maintenance result obtained after the target cloud service executes the operation and maintenance operation instructions.

15. A computer program product, wherein, Comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-12.

Images