8 results about "DevOps" patented technology

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.

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.

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 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 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.