65 results about "DevOps" patented technology

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 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.

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.

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 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.

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 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

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.

The present invention discloses a method, apparatus, device, and medium for automatically arranging multi-version data extraction tasks in DevOps. The method comprises: identifying the data flow between multiple modules of a DevOps platform and generating first-level numbering for cross-module business data tables; generating second-level numbering for business data tables based on the dependency order of built-in tables in the modules; constructing a table association relationship diagram, automatically arranging the execution order of data extraction tasks according to the order in the diagram, and implementing task grouping; dynamically adjusting the extraction frequency based on module load, and utilizing data space to isolate multi-version tasks. The present invention solves the problems of high coupling degree, difficult version management, and static extraction frequency in traditional data extraction tasks, improves the efficiency and reliability of DevOps data processing, and is suitable for data governance throughout the entire life cycle of software development.

This invention relates to the field of server technology and discloses a DevOps-based method and system for remote server firmware upgrades. The system includes: a data acquisition unit that matches the target firmware version based on device identification information and constructs a state parameter set corresponding to the server; a processing unit that performs weighted calculations on the normalized data, generating an execution instruction and sending it to the out-of-band management interface if the upgrade capability boundary value meets the execution conditions, and generating a restriction instruction and writing the upgrade task into a pending queue if the upgrade capability boundary value does not meet the execution conditions; an execution unit that performs firmware upgrades on the server based on the out-of-band management interface and controls the firmware upgrade based on offset generation process instructions; and an update unit that acquires the upgrade result data, associates it with the updated state parameter set, and writes the adjusted weighted calculations into the policy configuration. This invention ensures the stability of remote server firmware upgrades.

The invention discloses an authority management system and method for multi-source authority aggregation and dynamic inheritance decoupling, and belongs to the technical field of DevOps, the system comprises an authority storage module for realizing user affiliation judgment of constant time complexity through non-expansion storage and bitmap indexing; the reverse index module is used for asynchronously updating the authority in batches through a secondary hash reverse index and a message queue; the permission aggregation module is used for obtaining an aggregation permission list through union set operation; the source tracing module is used for obtaining directly added permissions through difference set operation; the inheriting and decoupling module is used for realizing dynamic inheriting and decoupling through an inheriting flag bit and a user-defined mask; and the cache synchronization module realizes increment synchronization through three-layer version number and inheritance chain propagation. According to the method, the problems of difficulty in source tracing, low dynamic decoupling efficiency, poor batch change performance and poor cache consistency are solved, and efficient, accurate and extensible multi-source authority management is realized.

Interactive CloudOps Dashboard Interface for Real-Time DevOps and Compliance Monitoring

This invention discloses an AI-driven DevOps adaptive fault repair method and system. The method specifically includes: extracting semantic features from code and logs generated during system runtime to generate semantic code fingerprints; using the semantic code fingerprints as initial node features to construct an error propagation graph based on the software system's call relationships; utilizing graph convolutional networks to perform high-order topological feature learning on the error propagation graph to accurately deduplicate error information and locate the root cause of the fault; generating elastic scaling strategies and resource configuration optimization schemes for cloud-native clusters based on the deduplicated error information and the system's real-time load pattern; and generating corresponding repair plans and matching the optimal responsible party based on the located root cause of the fault and the real-time status characteristics of team members. This invention automates the entire DevOps fault repair process, significantly reducing the mean time to recovery and improving system stability and operational efficiency.

Apparatus for a dynamic development operations (“DevOps”) pipeline generation may include the current build and release of the source code files and related changed units within a data repository. The data repository may include a plurality of data associated with the current build and release of the source code file. A software system may collect the data from the repository. The collected data may be input into an artificial intelligence or machine learning (“AI / ML”) module. The AI / ML module may use a language learning module (“LLM”) to create a plurality of nodes from the data. The LLM may create a knowledge graph from the nodes. The knowledge graph may be input into a quantum computing system to create attention matrices. The attention matrices may be input into a quantum annealing system to determine the DevOps plan. A transformer neural network (“TNN”) may output and execute the DevOps plan.

The application discloses a kind of DevOps platform monitoring cycle intelligent regulation and control methods, first, system multidimensional index data stream is acquired, forms instant multidimensional vector by stream computing, window processing, feature combination etc., to judge system state. Then, the emergency degree is determined by quantifying event information using event level model. Then, the confidence vector is obtained by operating the preset rule base, the corresponding scheduling strategy is triggered, and the resources are allocated according to fuzzy logic and event emergency degree. At the same time, the event priority is determined by combining expert experience and fuzzy logic, and the flexible management plan is executed and the result is evaluated. Finally, the rule weight ratio is adjusted dynamically according to the rule base to optimize the judgment of system multidimensional index. The application realizes dynamic regulation and control of monitoring cycle, accurate evaluation of system and event, intelligent scheduling and disposal, and improves the stability, efficiency and adaptability of DevOps platform.

The invention discloses a multi-dimensional analysis model construction and automatic verification method oriented to DevOps self-evaluation, and relates to the technical field of DevOps self-evaluation analysis, and the method comprises the following steps: constructing a DevOps full-dimensional database, and recording stage data; performing range bundling processing on the stage data in the DevOps full-dimensional database to obtain effective data; calculating normalized data of the stage data based on effective data in the DevOps full-dimensional database; calculating a stage score and a comprehensive score of the research and development team in each research and development operation and maintenance process, and evaluating the DevOps process of the research and development team based on the stage score and the comprehensive score; automatically verifying whether the improvement of the research and development operation and maintenance process is valid; the method is used for solving the problems that the existing DevOps self-evaluation analysis technology is not intelligent enough in the evaluation process and cannot provide accurate reference data for improvement of a research and transportation team, so that the applicability of DevOps self-evaluation is low.

The invention discloses an application method of DevOps pipeline business code auditing, and belongs to the technical field of business code auditing, the application method comprises a code auditing system composed of a code warehouse unit, a design management unit, an LLM code auditing unit and an auditing auxiliary unit, and the design management unit is composed of a design management module and a rule engine. According to the method, combined application is carried out based on the LLM model and the rule engine in the code architecture process, the auditing code efficiency and auditing quality can be greatly improved, the auditing code efficiency and auditing quality can be improved through proxy auditing of the LLM auditing module in the auditing process, the auditing labor cost and the auditing time cost are greatly reduced, and the auditing efficiency and the auditing quality are improved. And the LLM code auditing unit is used for directly marking problems and modification suggestions of the codes to specific code lines or code blocks, developers can confirm the problems and the modification suggestions step by step one by one and complete problem correspondence by referring to the modification suggestions, and the problem solving efficiency is also greatly improved.

Real-Time DevOps and Compliance Monitoring Graphical user Interface with Interactive CloudOps Dashboard

A computer-implemented method includes processing, by an Intelligent Root Cause Analyzer (IRCA), an error message. The error message is categorized by an Error Categorizer of the IRCA as an Error Category. A code change related to the Error Category is searched for by the IRCA. An ID of an impacted application or service and a Change Category is received by a Code Repository and Build and Pipeline. The Error Category and the Change Category are compared by the IRCA. A Service Dependency Graph is searched by a Dependency Reader of the IRCA for services that are called by the impacted application or service. A Monitoring system is queried by the IRCA for potentially different error messages and derived Error Categories from the services called by the impacted application or service.