95 results about "DevOps" patented technology

An intelligent regulation and control method for a DevOps platform monitoring period comprises the following steps: firstly, acquiring a multi-dimensional index data stream of a system, and forming an instant multi-dimensional vector through stream-oriented calculation, window processing, feature combination and the like so as to judge the state of the system; and quantifying event information by using an event level model to determine an emergency degree. A confidence vector is obtained through operation of a preset rule base, a corresponding scheduling strategy is triggered, and resources are allocated according to the fuzzy logic and the event emergency degree. Meanwhile, the event priority is determined in combination with expert experience and fuzzy logic, and a flexible management plan is executed and a result is evaluated accordingly. And finally, according to the rule base evaluation, dynamically adjusting the rule weight ratio, and optimizing the judgment on the multi-dimensional indexes of the system. According to the invention, dynamic regulation and control of a monitoring period, accurate evaluation of systems and events, intelligent scheduling and handling are realized, and the stability, high efficiency and self-adaptability of a DevOps platform are improved.

The invention relates to a DevOps-oriented containerized test environment deployment system and method, and belongs to the technical field of Internet. A multi-service test environment is defined through a YAML file, environment variables are managed in combination with an. Env configuration file, dependency is optimized through dependency layering and a cache library, a container mirror image is constructed, and a container cluster is started; managing multiple tasks through a centralized scheduling architecture, and realizing resource isolation of a container cluster by using Cgroup; adjusting the number of container instances in real time based on a capacity expansion and contraction mechanism; a bridging network is used for automatically distributing a container IP, a built-in domain name system analyzes a service name dynamic discovery service, and rapid reconstruction of a containerized test environment is achieved. According to the method, a YAML and environment variable separation mechanism is adopted, real-time loading of development, testing and production environment configuration is supported, the deployment time is shortened to the second level, the resource utilization rate is increased to 90% from 60%, the multi-service communication difference is eliminated, and efficient interconnection between containers is ensured.

The invention discloses a programming assistant framework, a programming assisting method, equipment, a medium and a product, and relates to the technical field of artificial intelligence, and the programming assistant framework comprises a DevOps platform integration module, a DevOps platform integration module, a DevOps platform integration module, a DevOps platform integration module, a DevOps platform integration module and a DevOps platform integration module, wherein the DevOps platform integration module is used for receiving input information sent by any link in links for code development and maintenance; the input information is sent to a code completion and dialogue function module; the code completion and dialogue function module is used for acquiring code related problems in input information, reading source codes in a project code library, and performing abstract syntax tree analysis on the source codes to obtain an abstract syntax tree; and the RAG retrieval enhancement generation module is used for performing semantic recognition on code related problems to obtain a user intention, traversing the abstract syntax tree according to the user intention, determining code context information, obtaining retrieval information according to the user intention and the code context information, and outputting code suggestions generated according to the retrieval information. The overall development efficiency in the development process is improved.

The invention relates to the technical field of artificial intelligence and software development, in particular to a DevOps assembly line intelligent generation system based on natural language description. According to the intelligent generation system for the DevOps assembly line based on the natural language description, an interaction layer is responsible for visually editing an interaction interface and outputting a generated executable script and a flow chart to a user; the intelligent processing layer takes Python and Go as development languages, fuses a knowledge graph technology, supports a dynamic code generation capability and realizes intelligent business process management and automatic execution; the infrastructure layer comprises three core modules, namely a metadatabase, a sandbox environment and an execution engine, and is responsible for providing basic support and resource guarantee for system operation. According to the intelligent generation system for the DevOps assembly line based on the natural language description, the configuration threshold of the DevOps assembly line is remarkably reduced, the cross-platform compatibility is ensured, the safety and reliability of the assembly line are greatly improved, and production accidents caused by manual configuration errors can be effectively avoided.

Systems and methods are disclosed for executing large quantum programs efficiently and securely. It breaks down programs into small, logical components, leveraging runtime analysis and AI-driven labeling. These components are then deployed across distributed quantum hardware nodes based on factors like noise levels, capabilities, and geolocation, optimized by a deep learning engine. Ownership and deployment paths are tracked using non-fungible tokens (NFTs) on a blockchain network, ensuring security and transparency. Outputs from each node are validated and aggregated based on their NFT linkage, resulting in accurate and reliable program results. This distributed quantum DevOps framework promotes scalability, performance optimization, and secure management, accelerating the development and application of quantum computing across diverse fields.

The invention relates to the field of software information, and discloses a DevOps-based process management method, system and equipment. The method comprises the following steps: disassembling a research and development process of a software project to obtain each step in the research and development process, and packaging each step into an independently deployed task node; a to-be-processed software project is obtained, a research and development process and a stage process corresponding to the software project are determined according to the identification information, and the software project carries unique identification information; based on the research and development process and the stage process, screening the task nodes corresponding to the stage process, and constructing the screened task nodes into an assembly line; the assembly line is executed, and the research and development process is completed. According to the method, the software delivery quality is automatically monitored, the workload of research and development testers in the aspect of quality analysis is reduced, a flexible process establishment mechanism is introduced, different research and development processes are flexibly established according to management specifications of different projects, and the fine degree and flexibility of research and development process management are improved.

Systems and methods for orchestrating events in distributed DevOps apparatus leveraging generative AI are disclosed to automate and streamline software development and deployment in distributed DevOps environments using Generative Adversarial Networks (GANs) and other AI techniques. The method involves interpreting UML diagrams, design documents, or the like with generative AI and computer vision to create DevOps tasks, integrating with various DevOps tools for task management, and deploying generated rules for automated event execution. Metadata is generated and processed in order to facilitate AI analysis. The systems and methods reduce manual intervention, increase efficiency, and improve accuracy, scalability, security, and compliance in DevOps workflows.

Solutions for integration and ongoing management of enterprise applications operating in a networked environment are disclosed. An AI transformer extracts, transforms, and manages integration point information from various sources, including documents, UMLs, and other system diagrams. Instead of relying on traditional documentation, the invention utilizes blockchain-based NFTs (non-fungible tokens) to store and manage integration point information. NFTs are created using smart contracts, which define the rules and conditions for the NFT. A metadata engine prepares metadata for generating the Integration Point NFTs. An NFT manager creates the integration point NFTs. A change analyzer determines the impact of changes to integration points on different applications and functionalities. A release compliance component is integrated with DevOps and is responsible for ensuring that integration point changes are incorporated into all impacted applications according to the release defined in the NFT.

In the DevOps process, testing teams use automated testcases to test a product in a regression testing running daily, which generate thousands of log files per run, in various distributed environment with different formats. Solutions to errors depend on SMEs and the impact of solution leads to rework of defect / errors and high degree of heterogeneity. This leads to huge bottleneck in automation of DevOps process leading to loss of productivity and agility. Present disclosure addresses the challenges by providing systems and methods that auto capture the log files of different formats efficiently in a scalable, extendible, and plug-able way. The system then mines and parses the log files based on given identifiers to standardize and de-duplicate to create unique error records with detail description including cause, position, module, timestamp, etc. The system predicts the solutions leveraging the database containing solutions and errors by using a modified Smooth Inverse Frequency technique.

The invention relates to the field of DevOps intelligent execution, in particular to an MCP-based DevOps task intelligent arrangement and execution method and system, and the method comprises the steps: obtaining a tool function of each MCP server to form a global tool library; receiving a natural language task instruction of a user, and inputting the natural language task instruction into the large language model; performing task decomposition on the natural language task instruction to generate a structured tool calling plan; the client side executes the structured tool calling plan and calls target tool functions on the corresponding MCP server in sequence or in parallel through the MCP according to the execution logic and the dependency relationship; monitoring execution of each tool function, determining a subsequent process or triggering a preset rollback operation according to an execution state, and feeding back a final execution result to the large language model; and generating a natural language abstract containing the overall task execution state based on the execution result, and outputting the natural language abstract to a user. And cross-environment seamless operation automation is realized.

Techniques are provided for visual generation of software deployment pipelines using recommendations of development and operations (DevOps) blueprints. One method comprises obtaining information characterizing a technology type and a programming language for a selected software development project; recommending a DevOps blueprint for the selected software development project based on a technology type and a programming language for the selected software development project, wherein the DevOps blueprint comprises reusable software development resources for the technology type and the programming language; providing a graphical interface to present a visual representation of the reusable software development resources; obtaining information characterizing interactions of a user with the graphical user interface to select the reusable software development resources for a pipeline associated with the selected software development project; and automatically generating software code in a target programming language of the DevOps collaboration tool based on the reusable software development resources in the pipeline.

This invention relates to systems and methods for real-time dynamic classification and orchestration of test automation components in distributed DevOps environments. The system features an Auto Identify Automation (AIA) engine that leverages supervised learning, Multi-Modal Artificial Intelligence (AI), and Generative AI technologies. It includes a Smart Scenario Designer interface that allows users to author test scenarios using handwriting and voice inputs, which are processed in real-time by AI-driven handwriting recognition, voice recognition, and Natural Language Processing (NLP). The system dynamically suggests relevant automated components via a smart bubble pane, facilitating rapid scenario creation. The architecture is tool-agnostic and scalable, with a Shared Workbench Engine that supports real-time collaboration and conflict resolution. The system continuously adapts and improves, ensuring that the automation suite remains consistent, up-to-date, and aligned with evolving software requirements, enabling efficient and user-friendly management of complex test automation processes.

The invention discloses a DevOps-based multi-choice component convenient deployment method, which comprises the following steps: interactively acquiring cluster configuration items from a user, including basic parameter configuration of a cluster and starting parameter configuration of cluster selectable components, to generate a main configuration file, analyzing the main configuration file to obtain an installation file for cluster deployment, executing the installation file, and executing the installation file for cluster deployment. A cluster environment and optional components are integrally and conveniently deployed; the method comprises the following steps of: configuring a trigger mechanism for a code warehouse, pushing an update code to the code warehouse by the developer, triggering an assembly line task of an automatic tool, performing automatic code compiling and mirror image construction on a selectable component code, and automatically pushing a mirror image to a mirror image warehouse by the developer; according to the method, mirror image updating of the selectable components is executed through an automatic tool to complete automatic deployment of the selectable components, so that the efficiency of cluster management and component updating deployment is improved, and the delivery efficiency and quality of a software system are improved.

A distributed computing system may include a code repository server configured to store code, a code deployment server configured to receive a deployment of the code, and a development and operations (DevOps) server configured to construct a pipeline between the code repository server and the code deployment server. The DevOps server may be configured to execute a source code management program to receive a request for information regarding a target resource that has been deployed using the pipeline, generate a permissions model for the target resource, the permissions model including one or more permissions, each permission authorizing a managed identity to execute an action related to the target resource, determine a permissions usage history of the permissions contained in the permissions model, filter the permissions model based on the permissions usage history, and generate a list of events determined to have occurred as the filtered permissions model.

The invention discloses a DevOps-based dynamic component security management method and system, and belongs to the technical field of information security detection. Component information data and vulnerability information data are matched by using a matching engine, potential security vulnerabilities are identified, and the security vulnerabilities of the component information data and the vulnerability information data are matched; according to the severity of the vulnerability, the deployment environment of the project, the involved user data volume and the involved sensitive data volume, risk assessment is carried out on the security vulnerability, automatic rating and classification are carried out on risk levels, the priority of the security vulnerability and a corresponding repair strategy are confirmed, a risk report is generated, and the risk report is displayed or exported through a user interface. According to the dynamic component security management method and system based on DevOps disclosed by the invention, potential security vulnerabilities can be accurately identified, automatic risk rating is carried out, vulnerability repair priorities can be ranked according to project risk levels, and later project vulnerability management is facilitated.

The invention provides a micro-unit code branch management method based on GitLab and DevOps, and belongs to the field of research and development test management.According to the method, GitLab is adopted for branch management, Dev serves as a trunk for development, function development is conducted through a derived branch MyDev, and after testing, the MyDev is merged to a Release branch, and Tag issuing is conducted. And aiming at an online problem, repairing from a Release branch and printing a new Tag. Meanwhile, in combination with DevOps practice, automatic testing, code review, safety review and automatic packaging deployment are carried out, each large process and link are divided into micro-units, and smooth and efficient automatic implementation of the version control and release process is ensured.

The invention provides a dynamic collaborative management method and system for intelligent configuration of DevOps in the power industry, and belongs to the technical field of operation and maintenance management of a power information system.The method comprises the steps that a natural language instruction of a user is analyzed, a target application, a target environment identifier and a target version are recognized, and a deployment request is generated; in response to the deployment request, calling the security baseline configuration library to execute static code compliance check to generate an application version package; deploying the application version package to a target environment, and synchronizing deployment data to a knowledge graph; after deployment is completed, a dynamic configuration change request is generated, a security baseline configuration library is called to execute dynamic configuration compliance verification, and dynamic configuration is updated to a target environment; and the running state of the target application is monitored in real time, dynamic configuration is automatically adjusted when the target environment changes, and fault positioning and repairing are executed based on the knowledge graph when the running state is abnormal. According to the invention, full-life-cycle management from a user instruction to application deployment, configuration management, operation monitoring and fault processing is realized.

Systems and methods allow users to query a software-application-based knowledge base to retrieve information. The user creates queries and the specified information is returned through a user interface. In alternative embodiments, the system is able to create follow-up queries independent from or directed by the user. Such embodiments may also provide critical information related to the original query. The knowledge base contains information about the application and is able to provide the appropriate answer to user queries through machine-learning-based tools, such as large language models (LLMs) or other natural language processing (NLP) based techniques.

The invention provides an incremental code optimization method based on the combination of DevOps and AI, belongs to the technical field of software development and optimization, and provides an incremental code optimization scheme based on GitLab and AI large models aiming at the requirement of code quality improvement in modern software development. According to the scheme, code submission operation is monitored through the Webhook technology of GitLab, incremental code changes are captured in real time, and contrastive analysis is conducted on the incremental code changes and original codes. By means of the strong analysis capability of the AI large model, the system can quickly identify potential problems in codes and generate targeted optimization suggestions. The optimization suggestions are notified to a submitter through mails, the submitter can evaluate the suggestions, and meanwhile, the codes before and after optimization are comprehensively evaluated regularly. Based on real evaluation feedback, the system further adjusts parameters and a knowledge base of the AI large model, and continuous optimization and improvement of the model are achieved.

The invention discloses an intelligent customer service implementation method and device for a DevOps platform and a medium, and the method comprises the steps: collecting real-time operation data of the DevOps platform, inputting the real-time operation data into a preset large model, and analyzing the real-time operation data through the large model to obtain a platform operation state corresponding to the DevOps platform; according to the historical operation record of the user and the current task parameter of the DevOps platform, filling the user question to generate a corresponding question and answer statement; inputting the question and answer statement into the large model, analyzing the running log of the DevOps platform and the question and answer statement through the large model, and outputting a plurality of candidate abnormal descriptions corresponding to the question and answer statement; performing mode matching on the running log through a preset knowledge base to output a corresponding target exception description; and under the condition that the candidate exception description is matched with the target exception description, acquiring an exception repair strategy corresponding to the candidate exception description based on the knowledge base, and pushing the exception repair strategy to the user.

The invention discloses a software full-life-cycle intelligent collaborative optimization method and system based on a large model, belongs to the technical field of artificial intelligence and DevOps crossing, and aims to solve the technical problem of how to eliminate LLM model performance degradation caused by development, test and production environment data splitting. According to the technical scheme, the method comprises the steps that a large language model is called through a gRPC interface to generate a program code, and a requirement specification with a version mark is obtained; calling a large language model to generate a boundary value test case based on an extension plug-in of a JUnit framework, and obtaining a defect report with a severity label; an anomaly detector integrated with a Prometheus monitoring tool calls a large language model to predict a container fault, and obtains a resource utilization rate time sequence report; and constructing a structured data lake: storing development, test and operation and maintenance data by using Elasticsearch, and establishing space-time association among developers, test cases and container instances through a Neo4j graph database.

The invention discloses a DevOps-oriented low-code front-end application construction and delivery method. The method comprises the steps that a developer carries out metadata design through a low-code development platform; front-end page design and function building are carried out through a visual designer; creating an application, and using an application visualization designer to connect a plurality of designed single pages in series according to business logic to obtain a visualization design result; a low-code platform, Vite, Vue3 and a TS technology stack are deeply fused, a visual design result is converted into a runnable code conforming to a modern front-end engineering specification through a rear-end driven intelligent code generation engine, and a final application engineering source code is output; and submitting the generated application project source code to a DevOps integrated management platform, and carrying out online construction application deployment and access test. The high efficiency of low-code development and automation of DevOps are fused, and full-process automation from application development to deployment is achieved.

The invention discloses a method for realizing DevSecOps in a container arrangement cluster. The method comprises the following steps: S1, Jenkins dynamically generates an assembly line container in the container arrangement cluster; s2, mounting network storage on an assembly line construction container, decompressing the Gitleaks and Dependency-Check installation packages, then putting the Gitleaks and Dependency-Check installation packages into a network storage directory, and enabling the assembly line to use the Gitleaks and Dependency-Check installation packages in a sharing mode; s3, the SonarQube is integrated with a Dependency-Check display report, and the Dependency-Check display report is integrated with the SonarQube; and S4, writing JenkinsFile, and connecting all tools in series for use. According to the method, DevOps in a traditional virtual machine environment is transformed into DevSecOps in a container orchestration cluster environment, so that the efficiency and accuracy of security examination are improved, the labor cost is reduced, the high efficiency and security of software delivery are realized, and the container orchestration cluster is protected from being damaged by security threats.

The invention discloses an AI-driven multi-industry DevOps whole-process intelligentization method and device and a medium, and the method comprises the steps: carrying out the butt joint of a DevOps tool chain, extracting the key data of a whole process, and constructing a structured data asset pool; building a layered framework combining the basic model and the expansion model; a multi-industry characteristic plug-in is built in, the plug-in is deployed and dynamically updated in a one-key mode according to a service scene, and the plug-in interacts with the AI model in real time to output a result conforming to industry characteristics; a user operation log is monitored, a research and development scene is identified, knowledge assets are retrieved from the data asset pool, content is actively pushed, and a source identifier is attached; and analyzing potential risks in combination with associated data, establishing a risk association model to predict a subsequent influence range, generating an early warning and pushing the early warning to a related role, and tracking a risk processing state to form closed-loop management. Through data integration, hierarchical AI architecture, active knowledge enabling and risk closed-loop management, DevOps full-process intelligence and multi-industry adaptation are realized, and research and development efficiency and process stability are improved.

The invention relates to a front-end component generation method and device based on a VUE architecture, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps that according to a JSON Schema file, a basic component list, a composite component assembling rule and a DevOps environment variable mapping relation are obtained through analysis; loading the basic component list to a basic component layer, and generating an atomic-scale Vue component set; loading the composite component assembly rule to a configurable composite component layer, reading the composite component assembly rule, and calling a Makefile tool to render the atomic-scale Vue component set to obtain a composite Vue component; loading the DevOps environment variable mapping relation to a DevOps component layer, subscribing a global EventBus event stream, and injecting the DevOps environment variable mapping relation into a DevOps Vue component to obtain an environment sensing type DevOps component; and carrying out cross-layer communication on the atomic-scale Vue component set, the composite Vue component and the environment sensing type DevOps component. By adopting the method, repeated development of the component can be eliminated, and component configuration can be automatically adjusted according to the environment variable.

Computer Console for DevOps Incident Management

A DevSecOps integration system that can analyze a source repository and provide recommendations on security services, along with supported security tools, that can be included as part of the DevOps pipeline. The DevSecOps integration system can automatically fit the selected security tools into the appropriate stage of the pipeline to optimize security protection. In some embodiments, the DevSecOps integration system automatically generates various and multiple CI / CD-specific integrations, scripts, and security code. In one example, the security tools are selected in response to user selection(s), such as an initial input file and code parameters, and automatically identifies where the scripts should be inserted in the development process.

Systems and methods provide novel inventions for integrating mainframe applications into modern Continuous Integration / Continuous Deployment (CI / CD) pipelines using BEARDS (Build Engineering And Rapid Development System), a Java-based interface. BEARDS facilitates the automation of build, test, and deployment processes for mainframe applications by managing source code, dependencies, and artifacts in a technology-agnostic manner. The system dynamically generates Job Control Language (JCL) jobs to compile mainframe code, integrates with CI / CD tools like Jenkins, JFrog Artifactory, and Ansible, and ensures secure, compliant deployments through detailed instructions and verification processes. BEARDS also supports large artifact management, incremental builds, and seamless integration with cloud platforms, providing scalability and flexibility for complex applications. By bridging the gap between legacy mainframe systems and modern DevOps practices, the invention enables organizations to automate and streamline their mainframe development processes, reducing costs, improving efficiency, and maintaining the reliability and security of critical applications.

The invention discloses a method, device and equipment for automatically arranging a multi-version data extraction task for DevOps, and a medium, and the method comprises the steps: recognizing the data flow direction among a plurality of modules of a DevOps platform, and generating a first-level number of a cross-module service data table; generating a second-level number of the business data table in combination with the dependency sequence of the table built in the module; constructing a table association relation graph, automatically arranging an execution sequence of data extraction tasks according to a sequence in the graph, and realizing task grouping; the extraction frequency is dynamically adjusted in combination with a module load, and multi-version tasks are isolated by using a data space. According to the method, the problems of high coupling degree, difficulty in version management, static extraction frequency and the like of a traditional data extraction task are solved, the DevOps data processing efficiency and reliability are improved, and the method is suitable for data governance in the whole life cycle of software development.

The invention discloses a version subscription and automatic delivery method and device based on DevOps, equipment, a medium and a product, and relates to the technical field of software version publication. Through a version subscription mechanism provided by the invention, key information such as a version number, branches, plan publication time, a person in charge and demand change can be recorded in a centralized manner; and an automatic notification and progress tracking mechanism is combined, so that all roles can obtain accurate and consistent version states in the same platform in real time. Therefore, the problem of information asynchronization is solved, the progress visualization level is indirectly improved, the exception discovery time is shortened, reliable data support is provided for responsibility division, and the version release efficiency and quality are remarkably improved.