26 results about "DevOps" patented technology

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.

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.

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 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 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 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.

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.

In some embodiments, an RPA design interface exposes a set of DevOps activities for creating a DevOps pipeline. Exemplary DevOps activities include activities for pulling a target RPA workflow from a code repository, testing, building, and deploying the respective RPA workflow to a client's production environment. The pipeline may then be executed by conventional RPA robots, as any other RPA workflow. Endowing an RPA design tool with DevOps functionality substantially facilitates RPA development by enabling RPA developers to carry out DevOps without requiring specialized knowledge of DevOps software. Some embodiments further enable RPA developers to create complex DevOps pipelines combining DevOps activities with other robotic activities, such as interacting with GUIs, spreadsheets, and electronic communication applications, among others.

This application discloses a web platform for data linkage and unified management of underlying table entries across multiple heterogeneous CI systems based on a DevOps architecture. This platform relates to the fields of software development and operations technology, and includes a front-end interaction layer, an application service layer, a data processing layer, a storage layer, and an API gateway layer. The application service layer includes a data acquisition module, a data unification and backfilling module, and an underlying table entry unified management module. The data acquisition module automatically collects, parses, and converts data in various formats. The data unification and backfilling module performs unified standard data structure mapping and backfilling. During data flow and invocation across CI systems, the underlying table entry unified management module performs field conversion and matching based on established mapping relationships, achieving dynamic maintenance and maintaining global consistency. This application enables unified data management across multiple heterogeneous CI systems under a DevOps architecture, establishing a comprehensive solution for cross-system and global consistency at the underlying table entry level.

The invention discloses a collaboration method for DevOps and detailed design on-line process in a distributed platform, which is characterized in that for high availability, load balancing and service priority requirements of automobile financial service channel scheduling, design process standardization definition and DevOps assembly line automation capability are fused; through standardized data configuration and dynamic weight calculation, underlying linkage of the process and the DevOps assembly line is realized. The method has the advantages that seamless linkage of design and development operation and maintenance links is realized through process decoupling and interface standardization adaptation.

This application provides a DevOps development method for AI algorithm services. By obtaining a compilation configuration file, generating a compilation task based on the configuration file, selecting a first target node from multiple nodes in a Kubernetes cluster based on a preset scheduler, creating a first container on the first target node, obtaining the program code of the target algorithm, importing the program code into the first container, executing the compilation task to compile the program code in the first container, obtaining the compilation result, testing the compilation result, obtaining the test result, and processing the target algorithm based on the test result, this method avoids the isolation between R&D and operation and maintenance, realizing a closed loop of algorithm R&D and operation and maintenance. By combining R&D and operation and maintenance, it can improve the efficiency of algorithm R&D, reduce the difficulty of operation and maintenance, and realize the rapid deployment and large-scale application of algorithms.

The application discloses a DevOps-based pipeline optimization method, device and medium, comprising the following specific steps: S1, pipeline historical data is acquired to obtain node information and influence factors, and node total weight and influence factor weight are determined; S2, pipeline complexity is determined according to the node total weight and the influence factor weight; S3, a Markov model is established according to the pipeline complexity and a greedy algorithm, and a state set generated by each node is acquired; S4, a hidden Markov model is established according to the state set generated by each node and dynamic programming of the Markov model, time distribution of a target node is determined, and workload of the target node is obtained. The Markov model is constructed according to the greedy algorithm, the state set generated by each stage is acquired, the hidden Markov model is established by using dynamic programming on the basis, the time distribution of the target node is determined, and thus the workload of the specific node in the pipeline is obtained, and the construction of the optimal pipeline is realized.

The invention relates to a test task scheduling and resource allocation method based on DevOps, which comprises the following steps of: firstly judging the irreversibility of tasks before scheduling, if the tasks fail to be scheduled, retry or rollback of downstream multiple tasks can be caused, namely identifying the tasks as irreversible tasks and pre-scheduling independently, if the tasks fail to be scheduled, terminating subsequent scheduling, and if the tasks fail to be scheduled successfully, returning to a scheduling queue; for tasks in the queue, a scheduling influence domain is identified according to a dependency relationship and a resource overlapping degree, and when a preset threshold value is exceeded, scheduling window convergence control is triggered, the concurrency scale is limited, and the scheduling density is reduced; before the tasks are matched with the computing resources, the execution performance of each resource node is monitored, and if the nodes have a high failure rate or performance degradation in multiple tasks, the nodes are set to be in a suppression state and are temporarily rejected from the resource allocation pool; in the scheduling process, the affinity is calculated according to the historical execution success rate and stability of the task on the resources, the task is preferentially scheduled to the corresponding resource group if the task exceeds a threshold value, and the binding is removed and the suitability is re-evaluated when the performance is abnormal, so that efficient scheduling and resource optimization configuration are realized.

The present application relates to a method and system for automatic continuous improvement of a DevOps platform. The method comprises collecting defect data of a business, pushing a defect event to a relevant person, determining a root cause category of the defect event and an improvement measure or a preventive measure, judging the effectiveness of the measure, outputting the measure and automatically including the improvement measure in the daily work targets of each team on the DevOps platform, continuously implementing the improvement measure, tracking the implementation result feedback of the measure, quantifying the improvement quality, and further improving the key points in each step based on the improvement quality result. The method fully considers the mutual relationship among the three elements of people, processes and tools in the continuous improvement process of the DevOps platform, guides people to conduct lean root cause analysis of defect events, and on this basis, outputs effective improvement measures based on people, processes and tools on the DevOps platform, and dynamically tracks the implementation results, and dynamically establishes a self-feedback DevOps continuous improvement system according to the implementation results.

The application discloses a multi-scene patient experience intelligent closed-loop management system, which is designed by combining a short message end and a webpage end based on a technology acceptance model of DevOps, combining perceived usefulness and perceived ease of use, embedding an inpatient nursing service experience evaluation scale, an ICU inpatient experience evaluation tool, a surgical patient operation nursing service experience scale and an inpatient unattended care service experience evaluation scale in the short message end and the webpage end, collecting patient experience data at different scene time points, and further realizing automatic analysis of the patient experience data and automatic feedback of the patient experience data, so as to solve the technical problems of the existing patient experience evaluation system, such as insufficient data collection pertinence, low data processing efficiency and difficulty in supporting continuous improvement management.

The embodiment of the invention provides a basic template updating method and device, a storage medium and electronic equipment, and the method comprises the steps: determining a to-be-updated item when a template updating request is received; determining a to-be-loaded basic template corresponding to the to-be-updated item; and updating the basic template identifier in the to-be-updated project based on the to-be-loaded basic template so as to call the to-be-loaded basic template according to the updated basic template identifier in the process of constructing the to-be-updated project. According to the technical scheme provided by the embodiment of the invention, the DevOps platform can call the content of the to-be-loaded basic template according to the updated basic template identifier in the process of automatically deploying the to-be-updated project to complete the construction of the to-be-updated project, so that the basic template of the to-be-updated project is switched to the to-be-loaded basic template; therefore, the purpose of updating the basic template of the project is achieved, then the adaptability between the project and the basic template is improved, and it is ensured that the DevOps platform can meet the deployment requirement when deploying the project.

The invention relates to the technical field of cloud computing, in particular to a distributed product distribution device and method applied to DevOps, the device adopts a three-layer architecture design and comprises an application management layer, a control coordination layer and an execution processing layer, bidirectional real-time communication between a central control node and a distributed Agent is achieved through a long connection communication mechanism based on Netty, and the distributed product distribution device and method are applied to DevOps. By combining an intelligent scheduling algorithm of a multi-dimensional weight calculation model, efficient product distribution in a cross-regional and large-scale distributed environment is realized, and the problems of low communication efficiency, poor real-time performance, insufficient expansibility, low resource utilization rate, complex operation and maintenance and the like in the prior art are solved; the method is suitable for enterprise-level CI / CD platforms and cross-region multi-data-center cloud native application delivery scenes.

There is a method for developing code based on DevOps. The method comprises: receiving a commit from developers (110); creating a CI / CD pipeline for the commit and running the CI / CD pipeline (120); sending information of the failed CI / CD pipeline to a Large Language Model if the CI / CD pipeline is failed (130); generating a new branch, error reason and bug fixing suggestion by the Large Language Model to fix the failed CI / CD pipeline, and sending the new branch, error reason and bug fixing suggestion to the developers (140).