Method and system for automatic operation of operation and maintenance task

Abstract

The invention discloses a method and a system for automatically running an operation and maintenance task. In order to solve the problems that a script operation and maintenance environment is high in dependence risk and complex task scheduling is low in efficiency, an automatic operation and maintenance feature is obtained by obtaining a low-code arrangement feature, a Playbook structure feature and host load parameters and combining and fusing the features, and a server certificate execution method is obtained; and constructing a certificate execution operation and maintenance automation model by utilizing the automatic operation and maintenance characteristics and a server certificate execution method. According to the method, a multi-parameter correction neural network model is constructed by considering a low code arrangement feature, a Playbook structure feature and a host load parameter, and more Playbook structure feature data is generated through a reinforcement learning method, so that a certificate execution operation and maintenance automation model obtains a server certificate intelligent execution method more accurately and intelligently; and meanwhile, tasks run in a container sandbox to isolate risks, a self-healing mechanism of an executable script is generated in combination with drag-and-drop flow chart parameters, and the method is applied to intelligent batch execution and fault node avoidance during multi-server certificate updating.

Description

TECHNICAL FIELD

[0001] The application relates to a method for automatically running an operation and maintenance task, and in particular relates to a method and system for automatically running an operation and maintenance task. BACKGROUND

[0002] In the early stage of rapid development of information technology, traditional operation and maintenance highly depends on manual operation, and is faced with problems such as low efficiency, high error rate and slow response. With the increasing complexity of enterprise IT architecture and the popularization of technologies such as cloud computing and big data, manual operation and maintenance cannot meet the needs of large-scale management, and automatic operation and maintenance has gradually become the core solution in the industry. The core is to realize the automatic execution of system monitoring, configuration management, task scheduling and other operations through scripting or using automation tools. Scripting language has become the cornerstone of automation due to its flexibility, easy integration and cross-platform capabilities, and the automation operation and maintenance platform further improves the operation and maintenance efficiency and system reliability through modular design and workflow engine. However, early script operation and maintenance still has problems such as low intelligence level, dependence on manual intervention and cross-platform compatibility challenges, and needs to continue to evolve towards intelligence, integration and security compliance.

[0003] Therefore, how to face the problems of high dependence risk and low efficiency of complex task scheduling in the script operation and maintenance environment, how to obtain the operation and maintenance task related features by merging and fusing the script operation and maintenance related parameters, how to build a certificate execution operation and maintenance automation model, how to use a neural network model for normal regulation, and how to expand the most difficult to obtain Playbook structure feature data, so that the certificate execution operation and maintenance automation model is more accurate and intelligent in deriving the server certificate intelligent execution method, and the task runs in a container sandbox to isolate risks, and a self-healing mechanism for generating executable scripts is combined with a drag-and-drop flowchart parameter to apply to intelligent batch execution and avoid fault nodes when updating multiple server certificates. In the prior art, the above problems still exist. SUMMARY

[0004] To solve the above technical problems, the application provides a method and system for automatically running an operation and maintenance task.

[0005] In a first aspect of the application, a method for automatically running an operation and maintenance task is provided, the method comprising: collecting and obtaining low-code orchestration features, and parsing natural language work orders into Playbook structure features according to natural language processing technology, synchronously obtaining host load parameters, and obtaining a server certificate execution method; merging and fusing the low-code orchestration features, the Playbook structure features and the host load parameters to form original automatic operation and maintenance features; According to the reinforcement learning method, the Playbook structure features are obtained, and the corresponding low-code orchestration generation features and host load generation parameters are set by technicians, the low-code orchestration generation features, the generated Playbook structure features, and the host load generation parameters are merged to form automatic operation and maintenance generation features, and a server certificate generation execution method is obtained; According to the automatic operation and maintenance features, the server certificate execution method, the automatic operation and maintenance generation features, and the server certificate generation execution method, a certificate execution operation automation model is constructed; The certificate execution operation automation model is run in a container sandbox, a server certificate intelligent execution method corresponding to a new environment of script operation is obtained, risks are isolated according to the server certificate intelligent execution method, and the method is applied to intelligent batch execution and fault node avoidance during multi-server certificate update.

[0006] Further, the low-code orchestration features include drag-and-drop flowchart parameters, flow orchestration parameters, and data structure parameters, the drag-and-drop flowchart parameters include flowchart text feature values, colors, and data source calculations.

[0007] Further, the drag-and-drop flowchart parameters are obtained by flowchart text feature values, colors, and data source calculations.

[0008] Further, the natural language processing technology is used to analyze natural language work orders into Playbook structure features, which is to analyze the text in the natural language work order into an explanation and set the explanation feature value.

[0009] Further, the host load parameters include memory usage, read-write delay time, and CPU usage.

[0010] Further, the reinforcement learning method uses a generative adversarial network model, adjusts according to the drag-and-drop flowchart parameters, specifically uses the drag-and-drop flowchart parameters to correct the generator of the generative adversarial network model, the generator uses a deep neural network, and the drag-and-drop flowchart parameters are used to correct the activation function thereof.

[0011] Further, the low-code orchestration features, the Playbook structure features, and the host load parameters are merged to form original automatic operation and maintenance features, and the low-code orchestration generation features, the generated Playbook structure features, and the host load generation parameters are merged to form automatic operation and maintenance generation features, which are obtained through data dimension linkage.

[0012] Further, the automatic operation features and the server certificate execution method and the automatic operation generation features and the server certificate generation execution method are constructed into a unified training data set, the certificate execution operation automation model is constructed using the unified training data set, and the certificate execution operation automation model is a neural network model improved by an activation function.

[0013] In the activation function, the change of the activation function can well show the gradient change of the model under the scene application, thereby affecting the accuracy of the neural network model classification. By using the host load parameter coefficient specific to the scene to correct the model, a more accurate server certificate intelligent execution method can be obtained.

[0014] A system for automatically running operation and maintenance tasks is also provided, which implements a method for automatically running operation and maintenance tasks, including a low-code orchestration feature acquisition module, a natural language work order analysis module, a host load parameter acquisition module, a certificate execution operation automation model construction module, and a server certificate intelligent execution module: The low-code orchestration feature acquisition module is used to collect and acquire low-code orchestration features. The natural language work order analysis module is built-in natural language processing model, which is used to analyze natural language work orders into Playbook structure features according to natural language processing technology. The host load parameter acquisition module is used to acquire host load parameters and server certificate execution methods. The certificate execution operation automation model construction module is used to obtain generated Playbook structure features according to reinforcement learning methods, and to generate corresponding low-code orchestration features and corresponding host load generation parameters according to technical personnel settings. The low-code orchestration generation features, the generated Playbook structure features, and the host load generation parameters are merged and fused to form automatic operation generation features, and the server certificate generation execution method is acquired synchronously. The certificate execution operation automation model is constructed according to the automatic operation features and the server certificate execution method and the automatic operation generation features and the server certificate generation execution method. The server certificate intelligent execution module is used to run the certificate execution operation automation model in a container sandbox, acquire corresponding server certificate intelligent execution methods in a new environment of script operation and maintenance, isolate risks according to the server certificate intelligent execution method, and apply to intelligent batch execution and fault node avoidance when multiple server certificates are updated.

[0015] Further, the reinforcement learning method utilizes a generative adversarial network model to adjust according to the drag-and-drop flowchart parameters, specifically, the drag-and-drop flowchart parameters are used to correct the generator of the generative adversarial network model, and the generator adopts a deep neural network, and the drag-and-drop flowchart parameters are used to correct the activation function thereof.

[0016] Therefore, the beneficial effects of the present application are that the present application obtains the automatic operation and maintenance feature by merging and fusing the low-code arrangement feature, the playbook structure feature and the host load parameter, and obtains the server certificate execution method, and constructs the certificate execution operation and maintenance automation model by using the automatic operation and maintenance feature and the server certificate execution method. The present application considers the low-code arrangement feature, the playbook structure feature and the host load parameter to construct the multi-parameter correction neural network model, generates more playbook structure feature data through the reinforcement learning method, so that the certificate execution operation and maintenance automation model is more accurate and intelligent to obtain the server certificate intelligent execution method, and the task runs in the container sandbox to isolate the risk, and the self-recovery mechanism of the executable script is generated by combining the drag-and-drop flowchart parameters, and is applied to the intelligent batch execution and fault node avoidance of the multi-server certificate update.

[0017] More embodiments and improvement effects of the present application will be further introduced in combination with the drawings and specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is a method flowchart of an operation and maintenance task automatic running of the present application; Figure 2 is a method system schematic diagram of an operation and maintenance task automatic running of the present application; Figure 3 is a low-code application example diagram adopted in an embodiment of the present application; Figure 4 is an activation function principle diagram of a neural network model in an embodiment of the present application; Figure 5 is an electronic device structure schematic diagram for realizing the method of the present application. DETAILED DESCRIPTION

[0019] Next, the application will be further described in combination with the drawings and specific embodiments, and the reinforcement learning model adopted in the present application is an improved generative adversarial network model, and the neural network model finally used for classification is also an improved model for scene application.

[0020] As Figure 2As shown, the system of this invention belongs to the field of script operation and maintenance engineering technology research and development, and belongs to the research and development of script operation and maintenance engineering technology other than marine engineering equipment, new materials, biotechnology, new energy, new energy vehicles, energy conservation, environmental protection, etc. Therefore, it belongs to new technology and innovation and entrepreneurship services.

[0021] The core task of generative artificial intelligence is to generate new data that conforms to certain rules or patterns. Neural networks, with their powerful learning and expressive capabilities, play a crucial role in GAI.

[0022] Generative Adversarial Networks (GANs) are a typical example of GAN-based neural network models. A GAN consists of two networks: a generator and a discriminator, which compete against and learn from each other. The generator is responsible for generating fake data, while the discriminator is responsible for judging the authenticity of the data. During training, the generator continuously attempts to generate more realistic images to deceive the discriminator, while the discriminator continuously improves its judgment to identify the generator's deceptions. This adversarial learning process allows the generator to gradually learn to generate high-quality data. The generator itself is constructed from a neural network, which requires the use of activation functions. The specific steps involved are readily available to those skilled in the art and will not be elaborated upon here.

[0023] In a first aspect of the present invention, a method for automatically running maintenance tasks is provided, the method comprising: Collect and acquire low-code orchestration features, and parse natural language work orders into Playbook structure features based on natural language processing technology, synchronously acquire host load parameters, and acquire server certificate execution methods; The original automated operation and maintenance features are formed by merging and integrating the low-code orchestration features, the Playbook structure features, and the host load parameters. The Playbook structure features are obtained based on reinforcement learning methods. The corresponding low-code orchestration generation features and host load generation parameters are set by the technicians. The low-code orchestration generation features, the Playbook structure features, and the host load generation parameters are merged and fused to form automatic operation and maintenance generation features. The server certificate generation execution method is obtained simultaneously. Based on the automated operation and maintenance features, the server certificate execution method, the automated operation and maintenance generation features, and the server certificate generation and execution method, an automated certificate execution operation and maintenance model is constructed. The certificate execution and maintenance automation model is run in a container sandbox to obtain the corresponding intelligent execution method for server certificates in the new script maintenance environment. Risks are isolated based on the intelligent execution method for server certificates, and the model is applied to intelligently batch execution and avoid faulty nodes when updating multiple server certificates.

[0024] In this embodiment, a method for automatically running operation and maintenance tasks is as follows: Figure 1 As shown.

[0025] Furthermore, the low-code orchestration features include drag-and-drop flowchart parameters, flowchart orchestration parameters, and data structure parameters. The drag-and-drop flowchart parameters include flowchart text feature values, colors, and data source calculations.

[0026] In this embodiment, low-code orchestration features include core architecture parameters, UI interface orchestration parameters, and integrated extension parameters. However, during model data processing, only three types of parameters that can be represented in data form—drag-and-drop flowchart parameters, process orchestration parameters, and data structure parameters—are selected as low-code orchestration features to adapt to subsequent reinforcement learning and neural network model training and use.

[0027] like Figure 3 The diagram shown is an application diagram related to the low-code orchestration feature in this embodiment.

[0028] This embodiment therefore only provides a detailed description of drag-and-drop flowchart parameters, flowchart orchestration parameters, and data structure parameters. Drag-and-drop flowchart parameters are a type of UI interface orchestration parameter, possessing variable attributes, including text, color, and data source. Flowchart orchestration parameters include the supported node types, connection conditions, and the product of variable parameters for the entire script. Data structure parameters primarily consist of data table structures, which are divided into field names, types, and indexes, obtained by multiplying the set values ​​corresponding to the field names, types, and indexes. Therefore, in this embodiment, the low-code orchestration features are represented by three numerical values ​​characterizing the generation of the server certificate execution method.

[0029] Furthermore, the parameters of the drag-and-drop flowchart are calculated using flowchart text feature values, colors, and data sources:

[0030] In the formula, This represents the total number of drag-and-drop flowchart parameters during low-code orchestration, where i represents the i-th drag-and-drop flowchart during low-code orchestration, and n represents the total number of drag-and-drop flowcharts. This represents the text feature value of the i-th drag-and-drop flowchart. Indicates the color complexity of the i-th drag-and-drop flowchart. The complexity of the data source for the i-th drag-and-drop flowchart is represented by the complexity of the color, which is defined by the amount of memory occupied by the color in the computer, and the complexity of the data source is represented by the reciprocal of the time taken for data transfer during script execution.

[0031] The more complex the parameters of the drag-and-drop flowchart, the more complex the operation and maintenance method. Therefore, in this embodiment, the drag-and-drop flowchart parameters are specifically calculated based on the flowchart text feature value, color, and data source complexity to characterize the automatic operation and maintenance task.

[0032] Furthermore, the step of parsing the natural language work order into Playbook structural features based on natural language processing technology involves parsing the text in the natural language work order into descriptions and processing them accordingly to set description feature values.

[0033] In this embodiment, the text in the natural language work order is parsed into descriptions and processed accordingly, and set as description feature values, as shown in the table below: ; The feature values ​​are set by those skilled in the art according to the needs of subsequent model training and processing. Therefore, a Playbook structural feature in this embodiment can be (234, 133, 165).

[0034] Furthermore, the host load parameters include memory utilization, read / write latency, and CPU utilization; Due to the correlation between operation and maintenance tasks and host load, this application fully considers the most important host load parameters, such as memory usage, read / write latency, and CPU usage, to form the relevant parameters for building the model, so that the intelligent execution method of server certificates obtained by the model is more objective and realistic.

[0035] Furthermore, the reinforcement learning method utilizes a generative adversarial network (GAN) model, adjusting it based on the drag-and-drop flowchart parameters. Specifically, it uses the drag-and-drop flowchart parameters to modify the generator of the GAN model. The generator employs a deep neural network, and its activation function is modified using the drag-and-drop flowchart parameters. The modified activation function is as follows:

[0036] In the formula, For the activation function value, Let m be the total drag-and-drop flowchart parameter for low-code orchestration of the j-th data in the training set, and m be the total amount of data in the training set. The weighted sum of the input Playbook structural features is used to modify the activation function by dragging and dropping flowchart parameters. This makes the Playbook structural features generated by the generator more suitable for generating automatic operation and maintenance features in this scenario. At the same time, since the automatic operation and maintenance features generated by the generator have a small difference from the actual data, the discriminator is selected in the conventional way in this embodiment. By optimizing the execution parameters through generated data, the original time consumption of script operation and maintenance is reduced, and the efficiency of building the subsequent multi-parameter correction neural network model is improved.

[0037] like Figure 4 The diagram shown is a basic schematic of the activation function in this embodiment.

[0038] Furthermore, the original automated operation and maintenance features are formed by merging and fusing the low-code orchestration features, the Playbook structure features, and the host load parameters, and the automated operation and maintenance generation features are formed by merging and fusing the low-code orchestration generation features, the generated Playbook structure features, and the host load generation parameters, both of which are obtained through data dimension linking.

[0039] In this embodiment, an original automated operation and maintenance feature is (low-code orchestration feature, Playbook structure feature, host load parameters), and an automated operation and maintenance generation feature is (low-code orchestration generation feature, generated Playbook structure feature, host load generation parameters).

[0040] Furthermore, a unified training dataset is constructed based on the automated operation and maintenance features, the server certificate execution method, the automated operation and maintenance generation features, and the server certificate generation and execution method. The certificate execution operation and maintenance automation model is constructed using this unified training dataset. The certificate execution operation and maintenance automation model is a neural network model with an improved activation function, and the activation function it adopts is:

[0041] In the formula, X represents the activation function value, where X is the feature value of the original automated operation and maintenance features or the features generated by automated operation and maintenance through a linear transformation using weights and biases. The total number of features generated by automated operations and maintenance is added to the original automated operations and maintenance features used to train the model. The host load parameter coefficient for the kth training set data is calculated by taking the reciprocal of the product of memory utilization, read / write latency, and CPU utilization.

[0042] In the activation function, changes in the activation function can effectively show the gradient changes of the model under the application scenario, thus affecting the classification accuracy of the neural network model. By using the host load parameter coefficients unique to this scenario to correct the model, a more accurate intelligent execution method for server certificates can be obtained.

[0043] It also provides a system for automatically running operation and maintenance tasks. This system implements a method for automatically running operation and maintenance tasks, including a low-code orchestration feature acquisition module, a natural language work order parsing module, a host load parameter acquisition module, a certificate execution operation and maintenance automation model construction module, and a server certificate intelligent execution module. The low-code orchestration feature acquisition module is used to collect and acquire low-code orchestration features. The natural language work order parsing module has a built-in natural language processing model, which is used to parse natural language work orders into Playbook structural features based on natural language processing technology; The host load parameter acquisition module is used to acquire host load parameters and acquire server certificate execution methods. The certificate execution and maintenance automation model construction module is used to obtain the Playbook structure features based on reinforcement learning methods, and according to the low-code orchestration generation features and corresponding host load generation parameters set by technical personnel, the low-code orchestration generation features, the Playbook structure features, and the host load generation parameters are merged and fused to form automatic operation and maintenance generation features, and the server certificate generation execution method is obtained simultaneously. Based on the automated operation and maintenance features, the server certificate execution method, the automated operation and maintenance generation features, and the server certificate generation and execution method, an automated certificate execution operation and maintenance model is constructed. The server certificate intelligent execution module is used to run the certificate execution operation and maintenance automation model in a container sandbox, obtain the server certificate intelligent execution method corresponding to the new script operation and maintenance environment, isolate risks according to the server certificate intelligent execution method, and apply it to intelligent batch execution and avoid fault nodes when updating multiple server certificates.

[0044] Furthermore, the reinforcement learning method utilizes a generative adversarial network (GAN) model, adjusting it based on the drag-and-drop flowchart parameters. Specifically, it uses the drag-and-drop flowchart parameters to modify the generator of the GAN model. The generator employs a deep neural network, and its activation function is modified using the drag-and-drop flowchart parameters. The modified activation function is as follows:

[0045] In the formula, For the activation function value, Let m be the total drag-and-drop flowchart parameter for low-code orchestration of the j-th data in the training set, and m be the total amount of data in the training set. The weighted sum of the input Playbook structural features is used to modify the activation function by dragging and dropping flowchart parameters. This makes the Playbook structural features generated by the generator more suitable for generating automatic operation and maintenance features in this scenario. At the same time, since the automatic operation and maintenance features generated by the generator have a small difference from the actual data, the discriminator is selected in the conventional way in this embodiment. By optimizing the execution parameters through generated data, the original time consumption of script operation and maintenance is reduced, and the efficiency of building the subsequent multi-parameter correction neural network model is improved.

[0046] Therefore, the beneficial effects of the present invention are as follows: This invention obtains automated operation and maintenance (O&M) features by merging low-code orchestration features, Playbook structure features, and host load parameters, and acquires server certificate execution methods. It then constructs an automated certificate execution O&M model using these features and methods. This invention considers low-code orchestration features, Playbook structure features, and host load parameters to construct a multi-parameter corrective neural network model. Reinforcement learning is used to generate more Playbook structure feature data, enabling the automated certificate execution O&M model to more accurately and intelligently derive intelligent server certificate execution methods. Simultaneously, the task runs in a container sandbox to isolate risks. Combined with a drag-and-drop flowchart parameter-based self-healing mechanism to generate executable scripts, this is applied to intelligently batch execution and avoid faulty nodes during multi-server certificate updates.

[0047] Of course, it is understood that each embodiment of the present invention can achieve one of the effects individually, and the combination of multiple embodiments of the present invention can achieve all the above effects. However, it is not required that each embodiment of the present invention achieve all the above advantages and effects, because each embodiment of the present invention can constitute a separate technical solution and make one or more contributions to the prior art. Each embodiment of the present invention is not affected, and the solution can still be implemented even if one embodiment is deleted.

[0048] For any module structures not specifically defined in this invention, the existing technical descriptions shall prevail. The prior art mentioned in the foregoing background and specific embodiments sections can be considered part of this invention and used to understand the meaning of certain technical features or parameters.

Claims

1. A method for automatic operation of operation and maintenance tasks, characterized in that, The method comprises: Collect and obtain low-code orchestration features, and parse natural language work orders into Playbook structure features according to natural language processing technology, synchronously obtain host load parameters, and obtain server certificate execution methods; The low-code orchestration features, the Playbook structure features, and the host load parameters are merged and fused to form original automatic operation and maintenance features; According to the reinforcement learning method, the generated Playbook structure features are obtained, and the corresponding low-code orchestration generation features and the corresponding host load generation parameters are set according to the technicians, the low-code orchestration generation features, the generated Playbook structure features, and the host load generation parameters are merged and fused to form automatic operation and maintenance generation features, and the server certificate generation execution method is synchronously obtained; According to the automatic operation and maintenance features and the server certificate execution method, and the automatic operation and maintenance generation features and the server certificate generation execution method, a certificate execution operation and maintenance automation model is constructed; The certificate execution operation and maintenance automation model is run in a container sandbox, the corresponding server certificate intelligent execution method in a new environment of script operation and maintenance is obtained, the server certificate intelligent execution method is used to isolate risks, and is applied to intelligent batch execution and fault node avoidance during multi-server certificate update.

2. The method of claim 1, wherein the low-code orchestration features include drag-and-drop flowchart parameters, flow orchestration parameters, and data structure parameters, and the drag-and-drop flowchart parameters include flowchart text feature values, colors, and data source calculations.

3. The method of claim 2, wherein the drag-and-drop flowchart parameters are obtained through flowchart text feature values, colors, and data source calculations.

4. The method of claim 3, wherein the natural language work orders are parsed into Playbook structure features according to natural language processing technology by parsing the text in the natural language work orders into instructions and setting corresponding processing instructions as instruction feature values.

5. The method of claim 4, wherein the host load parameters include memory usage, read-write delay time, and CPU usage.

6. The method of claim 5, wherein the reinforcement learning method uses a generative adversarial network model to adjust according to the drag-and-drop flowchart parameters, which specifically uses the drag-and-drop flowchart parameters to correct the generator of the generative adversarial network model, and the generator uses a deep neural network to correct its activation function using the drag-and-drop flowchart parameters.

7. The method of claim 6, wherein ​ ​ ​ ​ ​ The low-code orchestration feature, the playbook structure feature, and the host load parameter are combined to form an original automatic operation feature, and the low-code orchestration generation feature, the generated playbook structure feature, and the host load generation parameter are combined to form an automatic operation generation feature, which are obtained through data dimension linking.

8. The method of claim 7, wherein: According to the automatic operation feature and the server certificate execution method, and the automatic operation generation feature and the server certificate generation execution method, a unified training data set is constructed, the certificate execution operation automation model is constructed using the unified training data set, and the certificate execution operation automation model is a neural network model with an improved activation function. In the activation function, the change of the activation function can well display the gradient change of the model under the scene application, thereby affecting the accuracy of the neural network model classification. By using the host load parameter coefficient specific to the scene to modify the model, a more accurate server certificate intelligent execution method is obtained.

9. A system for automatically running operation and maintenance tasks, which implements the method of claim 8, and comprises a low-code orchestration feature acquisition module, a natural language work order analysis module, a host load parameter acquisition module, a certificate execution operation automation model construction module, and a server certificate intelligent execution module, wherein: The low-code orchestration feature acquisition module is configured to collect and acquire low-code orchestration features. The natural language work order analysis module is configured to analyze natural language work orders into playbook structure features based on natural language processing technology. The host load parameter acquisition module is configured to acquire host load parameters and server certificate execution methods. The certificate execution operation automation model construction module is configured to obtain generated playbook structure features based on reinforcement learning methods, and to set corresponding low-code orchestration generation features and corresponding host load generation parameters according to technical personnel, to form automatic operation generation features by combining the low-code orchestration generation features, the generated playbook structure features, and the host load generation parameters, and to synchronously acquire server certificate generation execution methods. The certificate execution operation automation model is constructed based on the automatic operation feature and the server certificate execution method, and the automatic operation generation feature and the server certificate generation execution method. The server certificate intelligent execution module is configured to run the certificate execution operation automation model in a container sandbox, to acquire corresponding server certificate intelligent execution methods in a new script operation environment, to isolate risks according to the server certificate intelligent execution methods, and to be applied to intelligent batch execution and fault node avoidance during multi-server certificate update.

10. The system of claim 9, wherein: The reinforcement learning method utilizes a generative adversarial network model, and is adjusted according to the drag-and-drop flowchart parameters, specifically, the drag-and-drop flowchart parameters are used to correct a generator of the generative adversarial network model, the generator adopts a deep neural network, and the drag-and-drop flowchart parameters are used to correct an activation function of the generator.

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