13 results about "Release management" patented technology

A method and apparatus for autonomous container management configuration changes to container clusters during runtime and autonomous configuration-based release orchestration. A release manager manages a staggered feature release that includes staggers, stagger order, and container clusters included in each stagger. A logging service manages logs generated by the container clusters and / or app containers. An update service determines container management configuration changes based on analysis of data provided by the logging service. A shared engine attempts to implement instructions provided by the release manager and the update service at different times. The release manager receives an indication of success or failure of the attempted deployment of the feature release to the current stagger. The release manager, responsive to the indication of success or failure, determines to perform one of a plurality of actions, including attempting to deploy the feature release to the next stagger, and rolling back.

The application discloses an application updating method and device, and relates to the field of system release management of Fintech (Fintech). The method comprises the following steps: selecting a server as a first server from at least one server associated with a first front-end system; updating an application of a first version deployed in the first server into an application of a second version; testing an optimized function of the application of the second version relative to the application of the first version; establishing a second front-end system according to the first front-end system and the application of the second version; associating the second front-end system with the first server; testing a newly-added function of the application of the second version relative to the application of the first version; and if it is determined that the test is passed, determining that the application updating is successful. The method improves the verification efficiency of application updating and improves the stability of system use.

A method performed by one or more computers for managing software distribution, comprising assigning computing devices into batches based on a plurality of criteria, including a software environment and a virtual machine cluster; obtaining a deployment schedule that associates each software release with a deployment date for each batch; generating a release scope list that indicates, for each computing device, an associated user account and a local deployment time from the deployment schedule; resolving conflicts in the deployment schedule to generate an updated deployment schedule based on the release scope list; deploying one or more software releases to the computing devices; transmitting a script to one or more of the computing devices, instructing determining a current status of any user account logged into the computing device and a message display type based on the current status, the message enabling installation of a software release of the one or more software releases.

This invention discloses a system for implementing blue-green deployment in Kubernetes, belonging to the field of computer technology. The system includes: a custom resource for blue-green deployment; a workload webhook module for monitoring new version releases and performing pre-processing for blue-green deployment of workloads; and a custom resource controller for executing deployment control logic on the corresponding workloads based on the custom resource for blue-green deployment, to achieve the desired blue-green deployment behavior. Specifically, when the webhook module detects a new version release, it checks whether the release should be allowed. If allowed, the release is temporarily paused so that the custom resource controller can manage the deployment later. This invention performs blue-green deployment of the target workload in a bypass manner, without changing the workload's metadata before and after deployment; it does not require the creation of additional workloads, simplifying the management of the deployment process, and supports deployment of existing workloads.

The embodiments herein provide a method and a system for security risk identification and controlling release management of software application. The method provides security risk identification in a software application as well as integrated tools to direct the efforts of developers to build and maintain secure software. The method provides a unique approach of defining a plurality of Service Level Agreement (SLA) to control a release build for a software application by checking vulnerabilities. Furthermore, the method is flexible enough to adapt in most complicated enterprises and different Software Development Life Cycle (SDLC) processes.

The invention relates to the technical field of model services, and particularly discloses a MaaS multi-model release management method and system and a storage medium, and the method comprises the steps: matching a processing model in a preset processing model library according to demand processing information, generating feedback information, and sending the feedback information to a demand side; carrying out statistics on demand processing information of all demand sides in a preset time period, determining a global information range, carrying out statistics on historical demand processing information of each demand side, determining an individual information range of each demand side, and constructing a model group of each processing model; the demand processing information is processed based on the processing model library, the global demand processing information in the preset time period and the individual demand processing information of each demand side are determined according to the demand processing information in the processing process, and then the lightweight model suitable for different demand sides is determined; the demand processing information is processed through the lightweight model, and the response speed is higher.

The invention provides an artificial intelligence whole-process automatic learning system based on an assembly line. Comprising an assembly line arrangement unit used for performing metadata association, state synchronization and task scheduling on each function unit in the same assembly line; the data management unit is used for acquiring the training data from the heterogeneous data source and performing format standardization, preprocessing and versioning storage on the training data; the training management unit is used for executing multiple rounds of model training and parameter tuning on the computing resource cluster to generate a temporary model file; the model evaluation and management unit is used for extracting a corresponding temporary model according to the identifier of the temporary model file, and performing evaluation index calculation and index comparison on the temporary model; and the release management unit is used for constructing a service package and pushing the service package to a preset DevOps mirror image warehouse to trigger a deployment process. According to the method, data version traceability, training process standardization, continuous retention of evaluation indexes, transparent model release and easy rollback can be realized.

Methods, systems, and computer program products are provided for automated software package release management. Software package development, testing, and release information is automatically captured, aggregated, organized and stored for accessibility and association with released software packages. Automated software package information management increases productivity and accuracy by removing most manual notation tasks from developers, testers, approvers, etc. A software release manager (SRM) may perform automated information collection, aggregation, approval management, compliance checking, change log generation, package compare, package deployment, notification, etc. for multiple software packages. For example, software package information can be distributed in notifications or accessed individually in a user interface. Users can make menu selections or query the software package manager, for example, to automatically and quickly determine and indicate software package-related information, such as the differences between released or unreleased packages, project status, approvals, test results, compliance, component change histories, etc.

The application discloses a software version release management method and system, a storage medium and equipment, and relates to the technical field of software release management.The method comprises the following steps: detecting release information corresponding to a software program filled by a user through a front end, and a release request corresponding to the software program submitted based on the release information; when the release request is received by a back end, code data in the software program is extracted according to the release request and the release information, environment variables and configuration files corresponding to the code data are generated, and a CI / CD pipeline of the back end is triggered; the code data is subjected to code compilation, code testing and code packaging through the CI / CD pipeline, a program package is obtained, and the program package is pushed to a container image warehouse; and deployment configuration in a Kubernetes cluster is automatically updated according to the container image warehouse, so that synchronous updating of multiple places and multiple clusters is realized.The application aims to solve the problems of abuse of authority, disorder of approval process and untimely information transmission in the prior art.

The application discloses a software abnormal behavior early warning and intelligent tracing method and system for a secret-related environment, relates to the technical field of software development, and solves the technical problems that the abnormal escape rate is high, risk early warning lags behind, decision-making lacks basis, release management and control is extensive, tracing and positioning is inefficient, and it is difficult to adapt to the precise early warning and intelligent tracing demand of software abnormal behavior in the secret-related environment; the features extracted from the abstract syntax tree from the syntax level can get rid of the interference of the surface form of the code, accurately capture the structured nature of the secret-related code; the actual running path of the code is restored from the execution dimension through the control flow graph, and the quantifiable execution logic complexity is combined with the logic to enhance the features. The logic complexity analysis can quickly locate the high-risk features in the secret-related environment, and provide bottom feature support for the abnormal behavior early warning. The correlation analysis is based on the CFG and variable dependence; core quantitative indicators are set for each release link to realize link-level risk decomposition.

The invention relates to the technical field of human resource management and cloud computing, in particular to a human resource information release management system based on cloud collaboration, and aims to solve the problems that in the prior art, multi-dimensional ability vectors of employees cannot be constructed, behavior characteristics cannot be fused to generate individual ability representations, and the performance is poor. End-to-end automatic analysis from behavior data to matching decision is difficult to realize, and objectivity, accuracy and intelligent level of talent configuration are reduced. According to the method, multi-dimensional ability vector construction of employees is executed through a behavior situation ability adaptation module, individual ability representation is generated by fusing behavior characteristics, a structured demand vector is generated by combining post demands, and the person-post adaptation degree is output through vector similarity calculation, so that end-to-end automatic analysis from behavior data to matching decision is realized; and objectivity, accuracy and intelligent level of talent configuration are improved.

The invention relates to the technical field of video release management, and discloses a video release management method and system based on artificial intelligence. The invention discloses a video release management method based on artificial intelligence. The method comprises the following steps: a video uploading step: receiving an original video file uploaded by a user; and an intelligent analysis step: calling a pre-trained AI model to carry out content analysis on the original video file, and generating video content understanding data which comprises a content classification label, a risk confidence score and automatically generated metadata. And a release strategy generation step: based on the video content understanding data and a pre-configured platform rule, automatically generating a personalized release strategy for a target release platform. According to the invention, by introducing an intelligent analysis and dynamic scheduling mechanism driven by artificial intelligence, the defects of low auditing efficiency and extensive resource allocation in the prior art are effectively overcome.

The invention relates to the technical field of AI generation, in particular to a standardized full-chain AI auxiliary management platform, which comprises a demand analysis module used for identifying a standardized demand direction; the project establishment evaluation module is used for generating project establishment priorities; the draft auxiliary module is used for generating a standard draft based on the standardized template and the standardized demand direction; the expert review module is used for reviewing the standard draft based on review experts matched in the standardized demand direction to obtain a release standard; and the release management module is used for generating interpretation information according to the release standard and pushing the release standard and the interpretation information to the target user. According to the method, standardization requirements are recognized through the AI model, project establishment evaluation is quantified, a draft is generated in a templated mode, expert review is intelligently matched, and published content is accurately pushed, so that scientificity, efficiency and consistency of standardization work are effectively improved, and an intelligent management system of a standard full life cycle is constructed.