Intelligent deployment method of information system components based on generative AI and blockchain technology

Abstract

The present invention provides an intelligent deployment method for information system components based on generative AI and blockchain technology, including: building a comprehensive AI model training data set; using the AI ​​model training data set to train and optimize the generative AI model; building private blockchain, smart contract and trusted media file download and automatic verification services; opening up generative AI models, smart contracts and trusted media services; initiating deployment operations based on human-computer interaction; enabling deployment job log records; real-time monitoring of deployment job logs, intelligent identification and automatic processing of errors and abnormal situations; after the deployment operation is completed, performing adaptive optimization of the generative AI model and cleaning up the installation environment. The present invention improves the automation level and efficiency of deployment, ensures the compliance and consistency of the results, and guarantees the security and reliability of the installation media and the controllability and security of the automation process.

Description

Technical Field

[0001] The present invention relates to the field of electronic information technology, and in particular to an intelligent deployment method for information system components based on generative AI and blockchain technology. Background Art

[0002] With the rapid development of information technology, automation has become an important trend in the deployment of information system infrastructure and application service components. At present, the mainstream automated deployment methods mainly include: script automated deployment (such as Bash, PowerShell, Python, etc.), configuration management tool automated deployment (such as Ansible, Puppet, Chef, SaltStack, etc.), containerized automated deployment, continuous integration / continuous deployment (CI / CD), and infrastructure as code (IaC) automated deployment. However, in large-scale enterprise intranets and hybrid cloud environments, automated deployment still faces some challenges and difficulties.

[0003] First, many enterprises have a large number of legacy systems, which are often difficult to effectively integrate with modern automation tools. In addition, the enterprise intranet is usually isolated from the Internet, and IaC tools that rely on public cloud APIs cannot be used. In an intranet or hybrid cloud environment, container orchestration tools and CI / CD processes require special network configuration and resource sharing to achieve communication and collaboration between containers, and face compatibility issues when deploying across multiple platforms. Although script automation deployment has high flexibility and compatibility in a hybrid cloud environment across multiple platforms, scripts need to be manually written and maintained. As the complexity of the script increases, the difficulty of maintenance will also increase.

[0004] Second, the advantage of automation tools lies in standardization and consistency, but most automation tools use predefined parameters and templates and cannot meet the specific configuration requirements of all environments and businesses. For example, different operating systems, hardware configurations and other factors will have an impact on deployment. Some specific configuration requirements, such as database parameters, application JVM configuration, security policies, etc., often need to be adjusted according to the specific environment to ensure the stability and security of the system.

[0005] Third, during the system deployment process, a large number of configuration adjustments still rely on manual work, which requires experts in related fields to have deep professional knowledge and rich practical experience. However, even experienced experts are inevitably affected by subjective factors such as fatigue, stress, and emotional fluctuations, which may lead to misjudgment or operational errors. In addition, the departure of key personnel or insufficient inheritance of professional knowledge will further affect the stable operation of the system and subsequent operation and maintenance work.

[0006] In addition to the above issues, security issues in the automated deployment process cannot be ignored. Security risks may come from many aspects, such as:

[0007] Security of scripts and installation media: If the scripts and installation media used for automated deployment are from unreliable sources or have been tampered with, malicious code, Trojans, ransomware, etc. may be introduced, providing hackers with opportunities to exploit and endangering the information security of the entire enterprise.

[0008] Misconfiguration: Misconfiguration of automation tools can lead to system vulnerabilities, such as opening unnecessary ports, using weak passwords, or granting excessive permissions, which can be exploited by hackers.

[0009] Credential management: Various credentials are often required during automated deployment, such as SSH keys, user passwords, etc. Improper credential management may lead to information system security and data leakage incidents.

[0010] Therefore, in order to achieve efficient, safe and reliable automated deployment, it is necessary to improve and perfect the existing technology. Summary of the invention

[0011] In view of the shortcomings of the prior art, the present invention proposes an intelligent deployment method for information system components based on generative AI and blockchain technology.

[0012] The present invention will use the following technical solutions to improve and perfect the existing technology:

[0013] A method for intelligent deployment of information system components based on generative AI and blockchain technology, comprising the following steps:

[0014] S110. Build a comprehensive AI model training dataset;

[0015] S120, training and optimizing a generative AI model using an AI model training data set;

[0016] S130, building private blockchain, smart contracts and trusted media file download and automatic verification services;

[0017] S140, connect generative AI models, smart contracts and trusted media services, build a deployment gateway for a human-computer interaction interface, and integrate generative AI models, blockchain smart contracts, and trusted media file download and query services;

[0018] S150, initiating a deployment operation based on human-computer interaction;

[0019] S160, enabling deployment job logging, the log records are stored locally on the deployment target node or in the enterprise unified log platform, and the log content includes timestamp, node name, operation content, and status;

[0020] S170, monitor the deployment operation log in real time, and intelligently identify and automatically handle errors and abnormal situations;

[0021] S180. After the deployment operation is completed, perform adaptive optimization of the generative AI model and clean up the installation environment.

[0022] Furthermore, the step S110 includes:

[0023] S1101. Collect installation use case data, where the installation use case data includes common commands for different operating systems, Shell scripts and Python programming knowledge, best practice guidelines, standardized configuration templates, and automation scripts covering different operating systems and software.

[0024] S1102, classifying the collected installation use case data by platform, operating system, and version to obtain a classified data set;

[0025] S1103, standardizing and cleaning the classified data set to obtain a standardized data set;

[0026] S1104. Perform instruction set specification mapping on the standardized data set to obtain an AI model training data set, wherein a command string and parameter mapping rule library is established when performing instruction set specification mapping. The mapping rule library includes standard command element mapping rules, parameter option mapping rules, and value mapping rules. The standardized data set is converted and mapped through the mapping rule library to generate a structured instruction set. The semantics and data constraints of each element can be accurately identified by the AI ​​model.

[0027] Furthermore, step S1103 specifically includes: using Python's Pandas and Numpy libraries to remove dirty data, the dirty data including missing values ​​and format errors, deleting comments, formatting codes, and standardizing variable names for rich text data, the rich text data including configuration files and scripts, and using the JSON module to convert the processed data into a standardized data set in JSON format.

[0028] Furthermore, the step S120 includes:

[0029] S1201. Use AI model training data set to train the neural network architecture based on Transformer-Seq2Seq deep learning;

[0030] S1202, encoding the rules and state information into a conditional vector as an additional input of the decoder to ensure that the generated deployment process complies with the predefined rule constraints;

[0031] S1203. Use manual or automated test cases to comprehensively evaluate and score the generated deployment process and instruction set to obtain an evaluation score;

[0032] S1204. Use the evaluation score as a supervisory feedback signal and combine it with the loss function to continue to fine-tune the model parameters, so as to continuously optimize and improve the generation quality and performance of the generative AI model.

[0033] Further, step S140 includes:

[0034] S1401. Design and build a deployment gateway for the human-computer interaction interface;

[0035] S1402. Design and build API interfaces between the deployment gateway and generative AI models, smart contracts on the blockchain, and trusted media file download and query services to achieve secure communication and data interaction.

[0036] Furthermore, the step S150 includes:

[0037] S1501, initiating a deployment process through the human-computer interaction interface of the deployment gateway;

[0038] S1502, establishing mutual trust access between the deployment gateway and the deployment target node;

[0039] S1503, performing an environmental check on the deployment target node;

[0040] S1504, automatically generate an installation pre-configuration file, the pre-configuration file includes: version, target installation path, deployment node IP address list, operating system user and user group running the software, randomly generated 12-digit strong administrator password, configuration parameters, operating system kernel optimization parameters, a list of dependent components that need to be pre-installed, and job parallelism;

[0041] S1505. Review, modify and confirm the installation pre-configuration file, and return the confirmed pre-configuration file to the generative AI model through the deployment gateway;

[0042] S1506. The generative AI model generates structured deployment instructions and configurations based on the contents of the installation pre-configuration file. The deployment gateway receives and parses the deployment instructions and configurations to generate a deployment job set including a deployment job script and a configuration file.

[0043] S1507, review, modify and confirm the generated deployment job set including the deployment job script and configuration file, and the deployment gateway uses tarfile and paramiko libraries to compress the confirmed deployment job set and transmit it securely and in parallel to the deployment target node through the SSH protocol;

[0044] S1508, installation media integrity and security verification;

[0045] S1509: The deployment gateway remotely connects to the deployment target node, decompresses and executes the deployment job set distributed in step S1507, and starts the automated deployment job.

[0046] Further, step S1502 includes:

[0047] S15011. After receiving the task, the smart contract automatically triggers the SSH key lifecycle management and generates public and private keys;

[0048] S15012. Deploy the gateway to obtain the public key and private key generated by the smart contract;

[0049] S15013. Obtain legal access credentials for deployment nodes;

[0050] S15014. The deployment gateway uses legitimate credentials to send the SSH public key to the deployment target node for registration through the paramiko library using a secure transmission protocol, thereby establishing a secure mutual trust access connection between the deployment gateway and the deployment node.

[0051] Furthermore, step S1503 includes:

[0052] S15031. The generative AI model generates a deployment target node environment check job instruction set based on the necessary information provided in step S1501. The job instruction set includes collecting the operating system version, hardware configuration, and installed software list content, and automatically determines whether the deployment target node environment meets the minimum operating conditions officially required by the software. The minimum operating conditions include CPU, memory, and disk available space.

[0053] S15032. The deployment gateway remotely performs an environment check, including: generating an environment check job script, and remotely executing the environment check job script on the deployment target node through the mutual trust access connection established in step S15014. If the environment check fails, the deployment gateway feeds back the result to the human-machine interface, prompting the operator that the current deployment target node does not meet the requirements and needs to reselect the deployment target node or adjust the environment and try again; after the environment check passes, the deployment gateway returns the collected deployment target node information to the generative AI model.

[0054] Further, step S170 includes:

[0055] S1701: Monitor the logs recorded in step S160 in real time to promptly capture abnormal situations that occur during the automated WebLogic application middleware cluster installation and deployment process.

[0056] S1702, the deployment gateway sends the exception information to the generative AI model;

[0057] S1703, the generative AI model automatically generates standard recovery operation processes and instructions, and feeds them back to the deployment gateway;

[0058] S1704, automatically executing the recovery operation after manual review and confirmation;

[0059] S1705. Continue to deploy the job after the recovery is completed;

[0060] In step S1702, the deployment gateway collects exception information and context log data and sends it to the generative AI model for analysis;

[0061] In step S1703, the generative AI model generates corresponding standard recovery operation processes and instructions, including rollback, repair, and retry, based on the exception type and context data;

[0062] In step S1704, the operator reviews the standard recovery operation process and instructions generated by the AI, and takes the following measures according to the actual situation:

[0063] Accept and execute: If the operator believes that the recovery operation process and instructions generated by AI meet the actual needs and safety standards, they will directly confirm the execution;

[0064] Rejection: If the operator believes that the recovery operation process and instructions generated by the AI ​​are insufficient and requires the AI ​​to regenerate them, the operator provides feedback to help the AI ​​model optimize the generated content;

[0065] Manual modification: Operators manually modify the recovery operation process and instructions generated by AI to ensure that they comply with the specific deployment environment and security requirements.

[0066] Further, step S180 includes:

[0067] S1801, Generative AI Model Adaptive Optimization;

[0068] S1802: Installation environment cleaning is further performed, wherein step S1801 includes:

[0069] S18011, the deployment gateway remotely connects to all deployment target nodes and collects job logs;

[0070] S18012. Standardize the log to obtain a feedback data set;

[0071] S18013. Combine feedback datasets with incremental learning algorithms to continuously optimize generative AI models;

[0072] Wherein step S1802 includes:

[0073] S18011. Delete all temporary files generated during the entire deployment process;

[0074] S18012, the deployment gateway releases the SSH public key registered in step S15014;

[0075] S18013. The deployment gateway notifies the blockchain smart contract to cancel the SSH key pair previously generated for this deployment.

[0076] The present invention generates software deployment instruction sets, software configuration parameters, and operating system optimization parameters through AI models, which greatly improves the automation level and efficiency of deployment and ensures the compliance and consistency of results; blockchain technology ensures the security of key management, and automated hash verification ensures the security and reliability of installation media; at the same time, although highly automated, the human-machine collaboration model retains the right to manual review and decision-making. Critical nodes such as exception handling solutions are still manually reviewed and confirmed, ensuring the controllability and security of the artificial intelligence automation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a flowchart of an intelligent deployment method for information system components based on generative AI and blockchain technology in the present invention. DETAILED DESCRIPTION

[0078] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment will be further described below in conjunction with the accompanying drawings of the embodiment of the present invention. The described embodiment is only a part of the embodiment of the present invention, not all embodiments.

[0079] The core content is the most important, and too much lengthy description may blur the focus. Therefore, some details will be appropriately omitted, because for those skilled in the art, based on the existing content of the specification and combined with common sense in the industry, they are sufficient to fully understand and no further explanation is needed.

[0080] In addition, the order of the steps described in the specification is not fixed. Those skilled in the art can optimize and adjust the order of the steps according to the actual needs. The order shown in the specification and the drawings is only for the purpose of making the description clearer, and it is not mandatory to follow this order unless it is clearly stated that a specific order has a special meaning and must be followed.

[0081] Example 1: A deployment method for a WebLogic application middleware cluster based on generative AI and blockchain technology

[0082] WebLogic is an industry-leading application middleware service software, which is widely used in the development and deployment of important information systems in government, finance, telecommunications and other enterprises. Its cluster installation is complex, requires high professionalism, and contains many flexible configuration items.

[0083] In this embodiment, a new automated installation and deployment method of a WebLogic application middleware cluster is implemented based on the technical solution in the present invention. Figure 1 As shown, the steps are as follows:

[0084] S110: Build a comprehensive AI model training dataset;

[0085] Preferably, the step S110 includes:

[0086] S1101—Collect WebLogic installation use case data;

[0087] S1102—classifying the collected WebLogic installation use case data to obtain a classified data set;

[0088] S1103—Standardizing and cleaning the classified data set to obtain a standardized data set;

[0089] S1104—Map the standardized data set to the instruction set specification to obtain the AI ​​model training data set;

[0090] Furthermore, the installation case data described in step S1101 is based on the common commands, Shell scripts and Python programming knowledge of different operating systems, and adds various WebLogic installation scenarios and edge cases. It includes: WebLogic best practice guide, WebLogic standardized configuration template, WebLogic automation scripts covering different operating systems and software. These data are derived from the company's existing operation and maintenance scripts, automation tool configurations, expert knowledge bases and manual test cases, and are strictly quality controlled.

[0091] Furthermore, the classified data in step S1102 is classified by operating system, WebLogic version, etc. After classification, metadata, such as applicable environment, etc., need to be annotated to finally form a complete classified data set.

[0092] Furthermore, the data cleaning described in step S1103 will use Python's Pandas and Numpy libraries to remove dirty data such as missing values ​​and format errors. For rich text data such as WebLogic configuration files and WebLogic deployment scripts, comments will be deleted, code will be formatted, and variable names will be standardized to reduce noise and improve model training efficiency. Finally, the JSON module will be used to convert the processed data into a standardized data set in JSON format.

[0093] Furthermore, in step S1104, when mapping the instruction set specification, a command string and parameter mapping rule base will be established, including rules such as standard command element mapping, parameter option mapping, and value mapping. Through the rule base, the standardized data set is converted and mapped to generate a structured instruction set, and the semantics and data constraints of each element can be accurately identified by the AI ​​model.

[0094] S120: training and optimizing a generative AI model using an AI model training dataset;

[0095] Preferably, the step S120 includes:

[0096] S1201—Use AI model training data set to train the neural network architecture based on Transformer-Seq2Seq deep learning;

[0097] S1202—encode the rules and state information into a condition vector as an additional input to the decoder to ensure that the generated WebLogic deployment process complies with the predefined rule constraints;

[0098] S1203—Use manual or automated test cases to comprehensively evaluate and score the generated WebLogic deployment process and instruction set.

[0099] S1204—Use these evaluation scores as supervisory feedback signals and combine them with the loss function to continue fine-tuning the model parameters, and continuously optimize and improve the generation quality of the generative AI model.

[0100] After step S120 is completed, the trained generative AI model will play a core role in the WebLogic automatic deployment process. When WebLogic needs to be deployed, the generative AI model intelligently generates the WebLogic deployment instruction set, WebLogic configuration parameters, and operating system optimization parameters based on the node environment information to be deployed.

[0101] S130: Build private blockchain, smart contracts and trusted media file download and automatic verification services;

[0102] Optionally, the step S130 includes:

[0103] S1301—Select a suitable blockchain platform and build a private chain or alliance chain environment;

[0104] S1302—Design and deploy smart contracts;

[0105] S1303—Design and deploy trusted media file download and query services;

[0106] Optionally, step S1303 includes:

[0107] S13031—Use Nginx to build a trusted information system component installation media file storage and download server in the enterprise intranet;

[0108] S13032—Use Mysql to build database service in the above server;

[0109] S13033—obtain the official SHA-156 hash value of the WebLogic installation media through a web crawler or manual method, generate media metadata (including WebLogic version, operating system, publisher, intranet media file download address, hash value, etc.) and store it in the database constructed in S13032 above;

[0110] After step S130 is completed, the blockchain smart contract is used as the core component for managing the SSH key life cycle. The immutability and encryption characteristics of the blockchain can effectively ensure the security of the SSH key. The trusted media file download and query service is used to ensure the integrity and security of the WebLogic installation media.

[0111] S140: Connects generative AI models, smart contracts, and trusted media services to achieve secure and reliable human-computer interaction;

[0112] Optionally, step S140 includes:

[0113] S1401—Design and build a deployment gateway for the human-computer interaction interface;

[0114] S1402—Design and build API interfaces between the deployment gateway and the generative AI model, smart contracts on the blockchain, and trusted media file download and query services

[0115] Furthermore, the deployment gateway described in step S1401, as a core hub component, provides access verification and a friendly and intuitive visual interface for deployment task management, file distribution, task execution status monitoring, information feedback and other functions. The deployment gateway also gives users the final decision-making power, allowing operators to manually confirm and modify the software deployment instruction set, configuration parameters and operating system optimization parameters output by the generative AI model to ensure the security and reliability of the entire deployment solution.

[0116] Furthermore, after step S1402 is completed, the deployment gateway can input information into the generative AI model through the API interface and parse the job instruction set and configuration output by the generative AI model. Smart contracts on the blockchain can monitor tasks initiated by the deployment gateway through the API interface. The deployment gateway can also obtain media metadata in the trusted media file download and query service through the API interface.

[0117] After step S140 is completed, a deployment gateway with a human-computer interaction interface will be built, integrating generative AI models, blockchain smart contracts, and trusted media file download and query services, effectively ensuring the security of deployment operations and achieving efficient collaboration in all aspects of the entire process.

[0118] S150: Initiate WebLogic cluster deployment job based on human-computer interaction;

[0119] Preferably, the step S150 includes:

[0120] S1501—Initiate the deployment process through the human-computer interaction interface of the deployment gateway;

[0121] S1502—Establish mutual trust access between the deployment gateway and the deployment target node;

[0122] S1503—Perform an environmental check on the deployment target node;

[0123] S1504—Automatically generate installation pre-configuration files;

[0124] S1505—review, modify and confirm the installation pre-configuration file;

[0125] S1506—Automatically generate job instruction sets and configurations through AI;

[0126] S1507—review, modify and confirm the sending of the job instruction set and configuration;

[0127] S1508—WebLogic installation media integrity and security check;

[0128] S1509—Execute the WebLogic cluster installation and deployment operation.

[0129] In step S1501, the operator will provide necessary information (including WebLogic version, deployment target node IP address, weblogic domain mode, weblogic domain name, weblogic management node IP, weblogic cluster name) through the human-computer interaction interface to initiate the deployment task;

[0130] Further, step S1502 includes:

[0131] S15011—After receiving the task, the smart contract automatically triggers the SSH key lifecycle management and generates public and private keys;

[0132] S15012—Deploy the gateway to obtain the public key and private key generated by the smart contract;

[0133] S15013—Obtaining legal access credentials for deployment nodes;

[0134] S15014—The deployment gateway uses legitimate credentials to send the SSH public key to the deployment target node for registration through the paramiko library using a secure transmission protocol, thereby establishing secure mutual trust access between the deployment gateway and the deployment node.

[0135] In step S15013, the legal access credential information can be obtained by integrating the enterprise's existing operation and maintenance audit system, or by directly filling in the user and password in step S1501.

[0136] Furthermore, step S1503 includes:

[0137] S15031—Generate an environment inspection job instruction set through a generative AI model;

[0138] S15032—Deployment gateway remote execution environment check;

[0139] In step S15031, the generative AI model generates a deployment target node environment check job instruction set based on the necessary information provided in step S1501. The instruction set includes the collection of operating system version, hardware configuration, installed software list, etc., and automatically determines whether the deployment target node environment meets the minimum operating conditions officially required by WebLogic (such as CPU, memory, disk available space, etc.);

[0140] Further, step S15032 includes:

[0141] S150311—After receiving the job instruction set of the generative AI model, the deployment gateway generates an environment check job script;

[0142] S150312—Remotely execute these scripts on the deployment target node through the mutual trust connection established in step S15014. If the environment check fails, the deployment gateway will feed back the result to the human-machine interface, prompting the operator that the current deployment target node does not meet the requirements and needs to reselect the deployment target node or adjust the environment and try again;

[0143] S150313—After the environment check passes, the deployment gateway sends the collected deployment target node information back to the generative AI model;

[0144] In step S1504, the generative AI model automatically generates a pre-configuration file for WebLogic cluster installation and deployment based on the data information collected in step S150313, taking into account WebLogic official best practices and expert experience data. The pre-configuration file includes: WebLogic product version, WebLogic installation path, WebLogic cluster node IP address list, WebLogic domain mode (development / production, etc.), WebLogic domain name, WebLogic management node IP address, WebLogic service port, WebLogic cluster name, operating system user and user group running WebLogic, randomly generated 12-digit strong administrator password, JVM version and initial memory configuration parameters, operating system kernel optimization parameters, list of dependent components that need to be pre-installed, job parallelism, and other information;

[0145] In step S1505, the deployment gateway passes the pre-configuration file to the human-computer interaction interface, and the operator will review the pre-configuration file and modify it according to actual needs and security policies. For example, the operator can adjust the WebLogic installation path, modify the JVM configuration parameters, modify the operating system kernel optimization parameters, modify the port number, or set a stronger administrator password. The confirmed pre-configuration file will be returned to the generative AI model through the deployment gateway.

[0146] In step S1506, the generative AI model generates structured deployment instructions and configurations based on the content of the pre-configuration file. The deployment gateway receives and parses the information and generates a deployment job set including the WebLogic deployment job script and configuration file;

[0147] The deployment operation set in step S1506 further includes:

[0148] Deployment and installation scripts: perform a series of automated deployment steps, including software decompression, configuration of environment variables, creation of running users, installation of dependent components, optimization and adjustment of operating system kernel parameters, and installation of security patches.

[0149] WebLogic domain configuration response file: contains parameters such as administrator username and password, node information, and access port, and is used to create and configure a WebLogic domain.

[0150] Management script set: control scripts for quickly starting / stopping WebLogic cluster management nodes and managed nodes

[0151] In step S1507, the deployment gateway uses tarfile and paramiko libraries to compress the confirmed deployment job set and transmit it securely and in parallel to the deployment target node through the SSH protocol;

[0152] Furthermore, step S1508 includes:

[0153] S15081—The deployment gateway obtains the download address and hash value of the WebLogic installation media and patch according to the WebLogic version information through the API interface constructed in step S1402;

[0154] S15082—The deployment gateway generates a media hash verification instruction through a generative AI model using the download address and hash value of the WebLogic installation media and patch obtained;

[0155] S15083—The deployment gateway automatically executes the WebLogic installation media and patch download instructions remotely on the deployment target node, and downloads the corresponding WebLogic installation media software and patches from the media server constructed in step S1303;

[0156] S15084—The deployment gateway automatically executes the installation media hash verification instruction remotely on the deployment target node to perform hash verification on the downloaded WebLogic installation media software and patches;

[0157] S15085—If the hash check fails, the deployment gateway will automatically re-execute the download. Once the check fails three times, the deployment gateway will automatically send an alert to the operator, indicating that the media check failed, and suspend subsequent installation and deployment until the problem is fixed.

[0158] In step S1509, the deployment gateway remotely connects to the deployment target node, decompresses and executes the deployment job set distributed in step S1507, starts the automated WebLogic application middleware cluster installation deployment job, and implements a series of deployment steps such as WebLogic automated installation, domain creation, cluster configuration, and vulnerability repair.

[0159] S160: Enable WebLogic cluster deployment job logging;

[0160] Preferably, the step S160 implements detailed log records for the automated WebLogic application middleware cluster installation and deployment operations. These logs will be stored locally on the deployment target node or in the enterprise unified log platform, and the recorded content includes core information such as timestamp, node name, operation content, and status.

[0161] S170: Real-time monitoring of WebLogic cluster deployment job logs, intelligent identification and automatic processing of errors and abnormal situations;

[0162] Preferably, the step S170 includes:

[0163] S1701—real-time monitoring of the logs recorded in step S160 to promptly capture abnormal situations that occur during the automated WebLogic application middleware cluster installation and deployment operation;

[0164] S1702—The deployment gateway sends the exception information to the generative AI model;

[0165] S1703—The generative AI model automatically generates standard recovery operation processes and instructions and feeds them back to the deployment gateway;

[0166] S1704—Automatically execute the recovery operation after manual review and confirmation;

[0167] S1705—Continue to deploy the job after recovery is completed;

[0168] In step S1702, the deployment gateway collects exception information and context log data and sends it to the generative AI model for analysis;

[0169] In step S1703, the generative AI model generates corresponding standard recovery operation processes and instructions, such as rollback, repair, retry, etc., based on the exception type and context data.

[0170] In step S1704, the operator reviews the standard recovery operation process and instructions generated by the AI. The operator can take the following measures based on the actual situation:

[0171] Accept and execute: If the operator believes that the recovery operation process and instructions generated by AI meet actual needs and safety standards, they can directly confirm the execution.

[0172] Rejection: If the operator believes that the recovery operation process and instructions generated by the AI ​​are insufficient, they can ask the AI ​​to regenerate them. The operator can provide feedback to help the AI ​​model optimize the generated content.

[0173] Manual modification: Operators can manually modify the recovery operation process and instructions generated by AI to ensure that they comply with specific deployment environments and security requirements.

[0174] S180: After the WebLogic cluster deployment job is completed, perform adaptive optimization of the generative AI model and clean up the installation environment.

[0175] Optionally, the step S180 includes:

[0176] S1801—Generative AI model adaptive optimization;

[0177] S1802—Installation environment cleanup;

[0178] Furthermore, step S1801 includes:

[0179] S18011—Deployment gateway remotely connects to all deployment target nodes and collects job logs;

[0180] S18012—Standardize the logs to obtain feedback data sets;

[0181] S18013—Combining feedback datasets with incremental learning algorithms to continuously optimize generative AI models;

[0182] Further, step S1802 includes:

[0183] S18011—Delete all temporary files generated during the entire deployment process;

[0184] S18012—Deployment gateway releases the SSH public key registered in step S15014;

[0185] S18013—The deployment gateway notifies the blockchain smart contract to cancel the SSH key pair previously generated for this deployment.

[0186] The present invention integrates generative AI and blockchain technology to provide an intelligent, efficient, safe and reliable solution for WebLogic cluster deployment. With the integration of domain knowledge base and best practices, the AI ​​model absorbs and learns a large amount of practical experience data, official guidelines and expert experience of WebLogic deployment, providing knowledge support and quality assurance for automated deployment. The software deployment instruction set, software configuration parameters and operating system optimization parameters are generated by the AI ​​model, which greatly improves the automation level and efficiency of the deployment and ensures the compliance and consistency of the results. Blockchain technology ensures the security of key management, and automated hash verification ensures the security and reliability of the installation medium. At the same time, although it is highly automated, the human-computer collaboration mode retains the right to manual review and decision-making. At key nodes such as exception handling solutions, they are still manually reviewed and confirmed, ensuring the controllability and security of the artificial intelligence automation process.

[0187] Those skilled in the art can realize all functions of the above modules by means of computer programs. The program can be stored in a computer-readable storage medium, including but not limited to: a computer disk, a centralized storage device, a cloud storage platform, a distributed storage system, etc. The operation and maintenance personnel only need to load and execute the program on the computer system to automatically realize the above technical solutions.

[0188] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for intelligent deployment of information system components based on generative AI and blockchain technology, characterized in that: The steps include: S110. Build a comprehensive AI model training dataset; S120, training and optimizing a generative AI model using an AI model training data set; S130, building private blockchain, smart contracts and trusted media file download and automatic verification services; S140, connect generative AI models, smart contracts and trusted media services, build a deployment gateway for a human-computer interaction interface, and integrate generative AI models, blockchain smart contracts, and trusted media file download and query services; S150, initiating a deployment operation based on human-computer interaction; S160, enabling deployment job logging, the log records are stored locally on the deployment target node or in the enterprise unified log platform, and the log content includes timestamp, node name, operation content, and status; S170, monitor the deployment operation log in real time, and intelligently identify and automatically handle errors and abnormal situations; S180. After the deployment operation is completed, the generative AI model is adaptively optimized and the installation environment is cleaned up; The step S150 includes: S1501, initiating a deployment process through the human-computer interaction interface of the deployment gateway; S1502, establishing mutual trust access between the deployment gateway and the deployment target node; S1503, performing an environmental check on the deployment target node; S1504, automatically generate an installation pre-configuration file, the pre-configuration file includes: version, target installation path, deployment node IP address list, operating system user and user group running the software, randomly generated 12-digit strong administrator password, configuration parameters, operating system kernel optimization parameters, a list of dependent components that need to be pre-installed, and job parallelism; S1505. Review, modify and confirm the installation pre-configuration file, and return the confirmed pre-configuration file to the generative AI model through the deployment gateway; S1506. The generative AI model generates structured deployment instructions and configurations based on the contents of the installation pre-configuration file. The deployment gateway receives and parses the deployment instructions and configurations to generate a deployment job set including a deployment job script and a configuration file. S1507, review, modify and confirm the generated deployment job set including the deployment job script and configuration file, and the deployment gateway uses tarfile and paramiko libraries to compress the confirmed deployment job set and transmit it securely and in parallel to the deployment target node through the SSH protocol; S1508, installation media integrity and security verification; S1509, the deployment gateway remotely connects to the deployment target node, decompresses and executes the deployment job set distributed in step S1507, and starts the automated deployment job; Step S170 includes: S1701, monitor the log recorded in step S160 in real time, and promptly capture abnormal situations that occur during the automated WebLogic application middleware cluster installation and deployment operation; S1702, the deployment gateway sends the exception information to the generative AI model; S1703, the generative AI model automatically generates standard recovery operation processes and instructions, and feeds them back to the deployment gateway; S1704, automatically executing the recovery operation after manual review and confirmation; S1705. Continue to deploy the job after the recovery is completed; In step S1702, the deployment gateway collects exception information and context log data and sends it to the generative AI model for analysis; In step S1703, the generative AI model generates corresponding standard recovery operation processes and instructions, including rollback, repair, and retry, based on the exception type and context data; In step S1704, the operator reviews the standard recovery operation process and instructions generated by the AI, and takes the following measures according to the actual situation: Accept and execute: If the operator believes that the recovery operation process and instructions generated by AI meet the actual needs and safety standards, they will directly confirm the execution; Rejection: If the operator believes that the recovery operation process and instructions generated by the AI ​​are insufficient and requires the AI ​​to regenerate them, the operator provides feedback to help the AI ​​model optimize the generated content; Manual modification: Operators manually modify the recovery operation process and instructions generated by AI to ensure that they comply with the specific deployment environment and security requirements.

2. The method for intelligent deployment of information system components based on generative AI and blockchain technology as claimed in claim 1, characterized in that: The step S110 includes: S1101. Collect installation use case data, where the installation use case data includes common commands for different operating systems, Shell scripts and Python programming knowledge, best practice guidelines, standardized configuration templates, and automation scripts covering different operating systems and software; S1102, classifying the collected installation use case data by platform, operating system, and version to obtain a classified data set; S1103, standardizing and cleaning the classified data set to obtain a standardized data set; S1104. Perform instruction set specification mapping on the standardized data set to obtain an AI model training data set, wherein a command string and parameter mapping rule library is established when performing instruction set specification mapping, and the mapping rule library includes standard command element mapping rules, parameter option mapping rules, and value mapping rules. The standardized data set is converted and mapped through the mapping rule library to generate a structured instruction set, and the semantics and data constraints of each element can be accurately recognized by the AI ​​model.

3. The method for intelligent deployment of information system components based on generative AI and blockchain technology as claimed in claim 2, characterized in that: Step S1103 specifically includes: using Python's Pandas and Numpy libraries to remove dirty data, the dirty data includes missing values ​​and format errors, deleting comments, formatting codes, and standardizing variable names for rich text data, the rich text data includes configuration files and scripts, and using the JSON module to convert the processed data into a standardized data set in JSON format.

4. The method for intelligent deployment of information system components based on generative AI and blockchain technology as claimed in claim 1, characterized in that: The step S120 includes: S1201. Use AI model training data set to train the neural network architecture based on Transformer-Seq2Seq deep learning; S1202, encoding the rules and state information into a conditional vector as an additional input of the decoder to ensure that the generated deployment process complies with the predefined rule constraints; S1203. Use manual or automated test cases to comprehensively evaluate and score the generated deployment process and instruction set to obtain an evaluation score; S1204. Use the evaluation score as a supervisory feedback signal and combine it with the loss function to continue to fine-tune the model parameters, so as to continuously optimize and improve the generation quality and performance of the generative AI model.

5. The method for intelligent deployment of information system components based on generative AI and blockchain technology as claimed in claim 1, characterized in that: Step S140 includes: S1401. Design and build a deployment gateway for the human-computer interaction interface; S1402. Design and build API interfaces between the deployment gateway and generative AI models, smart contracts on the blockchain, and trusted media file download and query services to achieve secure communication and data interaction.

6. The method for intelligent deployment of information system components based on generative AI and blockchain technology as claimed in claim 1, characterized in that: Step S1502 includes: S15011. After receiving the task, the smart contract automatically triggers the SSH key lifecycle management and generates public and private keys; S15012. Deploy the gateway to obtain the public key and private key generated by the smart contract; S15013. Obtain legal access credentials for deployment nodes; S15014. The deployment gateway uses legitimate credentials to send the SSH public key to the deployment target node for registration through the paramiko library using a secure transmission protocol, thereby establishing a secure mutual trust access connection between the deployment gateway and the deployment node.

7. The method for intelligent deployment of information system components based on generative AI and blockchain technology as claimed in claim 6, characterized in that: Step S1503 includes: S15031. The generative AI model generates a deployment target node environment check job instruction set based on the necessary information provided in step S1501. The job instruction set includes collecting the operating system version, hardware configuration, and the content of the installed software list, and automatically determines whether the deployment target node environment meets the minimum operating conditions officially required by the software. The minimum operating conditions include CPU, memory, and disk available space; S15032. The deployment gateway remotely performs an environment check, including: generating an environment check job script, and remotely executing the environment check job script on the deployment target node through the mutual trust access connection established in step S15014. If the environment check fails, the deployment gateway feeds back the result to the human-machine interface, prompting the operator that the current deployment target node does not meet the requirements and needs to reselect the deployment target node or adjust the environment and try again; after the environment check passes, the deployment gateway sends the collected deployment target node information back to the generative AI model.

8. The method for intelligent deployment of information system components based on generative AI and blockchain technology as claimed in claim 6, characterized in that: Step S180 includes: S1801, adaptive optimization of generative AI model; Step S1801 includes: The deployment gateway remotely connects to all deployment target nodes and collects job logs; Standardize the logs to obtain the feedback data set; Combine feedback data sets with incremental learning algorithms to continuously optimize generative AI models; S1802, installation environment cleaning; Step S1802 includes: Delete all temporary files generated during the entire deployment process; The deployment gateway releases the SSH public key registered in step S15014; The deployment gateway notifies the blockchain smart contract to cancel the SSH key pair previously generated for this deployment.

Images