48 results about "DevOps" patented technology

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.

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 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 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 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.

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.

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.

The application discloses an application method of DevOps pipeline business code auditing, belongs to the technical field of business code auditing, and 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, wherein the design management unit is composed of a design management module and a rule engine. In the code architecture process, the application is combined based on the LLM model and the rule engine, the code auditing efficiency and the auditing quality can be greatly improved, in the auditing process, the auditing code efficiency and the auditing quality can be improved through the agent auditing of the LLM auditing module, the auditing labor cost and the time cost are greatly reduced, the problems and the modification suggestions of the code are directly marked on specific code lines or code blocks through the LLM code auditing unit, the developer can confirm and complete the problem corresponding to the modification suggestion by referring to the modification suggestion step by step, and the problem solving efficiency is greatly improved.

The invention discloses an intelligent continuous integration / continuous delivery implementation method and system based on Jenkins, and belongs to the technical field of industrial application software, the implementation of the method comprises the following steps: an administrator configures Jenkins server connection information, a basic construction template and mirror image warehouse authentication information; creating a product and configuring basic information as a basic unit of subsequent management; product access and operation authorities are distributed to team members, and safety control is achieved; adding each component under the product, and configuring a code warehouse, branches and component types; multiple sets of environments are configured for the product, and mirror image warehouses and architecture requirements of all the environments are specified; selecting a component and environment creation construction task, and automatically generating initial configuration by the system; and after the user confirms or modifies the construction parameters, the construction is triggered. According to the method, standardization and automation of the construction and deployment process can be realized, the DevOps threshold is greatly reduced, and the software delivery efficiency and quality are improved.

The application discloses an intelligent code analysis system and method based on a graph database, and the system realizes accurate modeling and efficient query of code complex dependence by constructing a multi-dimensional code relation graph and utilizing a graph database. The system supports incremental analysis triggered based on a GitLab Webhook event and full-scan mode triggered manually or at a fixed time, and simultaneously integrates multiple AI models, dynamically calls lightweight or deep analysis models, and optimizes AI calling cost through intelligent context filtering and token rate control mechanism. The application significantly improves the accuracy, efficiency and real-time performance of code analysis, and the AI analysis accuracy is improved by more than 30%, the analysis time of large projects is shortened by 60%, and the AI calling cost is reduced by 50%, which is suitable for enterprise-level code quality management and control and DevOps process integration.

The invention discloses an intelligent log analysis method based on a DevOps assembly line, and relates to the technical field of information. A BERT deep learning model based on a Transform architecture is trained, and a matched solution is output; context information is dynamically extracted based on error log features, the interception range is determined through a self-adaptive window algorithm, and the window size is dynamically adjusted according to error types and historical data; a user feedback mechanism is introduced, the solution probability is weighted and corrected according to the number of likes / treads, and model parameters are dynamically optimized through incremental training; and forming a closed-loop improved system, feeding back an unmatched problem to the knowledge base for analysis, and updating model training data at the same time. And intelligent log analysis and real-time problem positioning are realized.

A method for generating and orchestrating software test data for use with distributed DevOps is provided. The method leverages generative AI and homomorphic encryption. The method includes receiving, at an AI engine, a test data request; receiving from an API interface information for responding to the test data request; receiving a plurality of requirement-based knowledge graphs; transferring shared knowledge information from the AI engine to the orchestration rule engine in order to perform a threshold check, to validate a type of the test data; and to pull a plurality of rules from a rule mapper for use in engaging a dynamic smart contract builder. The method includes creating a smart contract for transmitting the test data request to nodes selected from a distributed blockchain of blockchain nodes and for receiving one or more responses to the request for test data and then forwarding the responses to the API interface.

The invention provides a project process backtracking method and system based on a timeline and a graph database, and aims to solve the problems of difficult data backtracking, complex integration and lack of full-process visualization caused by tool chain splitting of an enterprise IT team. The method comprises the following steps: accessing heterogeneous DevOps tool data through a plug-in mechanism; modeling the accessed original data, defining a relationship among a data field, a filtering rule and a data object, and constructing a graph database; data visualization is achieved based on a graph database and a timeline, data can be inquired through an object and relation mode, and the data relation evolution process is traced back through a timeline. According to the method, an existing tool chain does not need to be transformed, full-process data association and visualization are achieved through fusion of the graph database and the timeline, the project problem backtracking efficiency and the data credibility are improved, and the method is suitable for process tracing and influence analysis of the full life cycle of software development.

The application discloses a one-stop intelligent software development system based on multiple agents, which comprises a one-stop DevOps research and development cooperation platform and a multiple-agent scheduling and cooperation engine. The multiple-agent scheduling and cooperation engine is connected with each sub-module in the one-stop DevOps research and development cooperation platform, including a demand management module, a code library management module, a document management module, a pipeline module, a product library module, a code scanning module, a test management module and a measurement management module. The multiple-agent scheduling and cooperation engine comprises a task analysis and distributor, a multi-role virtual employee intelligent agent pool, a large model calling adapter, a delivery material archiving and integration controller and an artificial confirmation node manager, realizes end-to-end automation from demand description to software delivery, and only needs to fill in a natural language demand description or a prompt word in a task card, so that the system can automatically drive a whole-process task such as prototype generation, code writing, test case generation and construction deployment, and the software delivery cycle is greatly shortened.

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.

Apparatuses, methods, systems, and computer program products provide for third-party DevOps tool data connection in a DevOps environment. A generic container connection model defining a generic container connection protocol is generated. Based on the generic container connection model and interaction with a third-party DevOps tool, a generic container connection is established with the third-party DevOps tool to access DevOps data generated via the third-party DevOps tool. Receive, from the third-party DevOps tool based on the generic container connection, DevOps data associated with a software development project executed at least in part using the third-party DevOps tool.

The application belongs to the technical field of Internet software development and operation and maintenance, and particularly relates to a DevOps continuous integration and delivery system based on a cloud platform, which comprises a project management module and a product library module; through integration of the advantages of elastic computing, high availability and automatic management of cloud computing and the concept of integration of agile development and operation and maintenance of DevOps, a full-automatic system from code submission to production deployment of software development is realized.