101 results about "Software deployment" patented technology

Techniques are provided for software deployment pipeline generation using generative artificial intelligence (AI). One method comprises obtaining a trained generative AI model, trained using a plurality of continuous integration / continuous deployment (CI / CD) configuration files; obtaining information characterizing a selected software development project; and applying at least some of the information characterizing the selected software development project as one or more system prompts to the trained generative AI model, wherein the trained generative AI model predicts a portion of a software deployment pipeline associated with the selected software development project. The CI / CD configuration files used for training may be associated with an organization that is associated with the selected software development project. The predicted portion of the software deployment pipeline may comprise pipeline jobs, an automatic code completion and / or a correction of a syntax and / or a structure of at least one CI / CD configuration file being edited.

The invention discloses an agent-free honeypot mapping method and device, and relates to the technical field of network security. The method comprises the following steps: analyzing metadata of core switching equipment, and establishing a domain-address mapping matrix; periodically detecting an address space, constructing an IP asset dynamic portrait and generating an asset state map; deploying a cross-domain traffic forwarding protocol stack and a virtual domain identifier mapping rule; deploying a software-defined virtual switching unit and accessing the virtual honeypot probe cluster; allocating idle addresses for the probes and establishing a binding relationship; and periodically detecting and triggering a self-healing process when an address conflict occurs. The device comprises a core exchange and domain management unit, an IP asset detection unit, a network protocol configuration unit, a probe virtualization management unit, an address resource scheduling unit and a system cooperative control unit. According to the method, agency-free and automatic deployment of the honeypot is realized, and the problems that a traditional scheme depends on agency software, deployment is complex and IP conflict processing lags are solved.

The invention discloses a demand development method, device and equipment. The method comprises the following steps: calling a demand analysis agent to analyze a natural language demand into a structured function demand unit comprising an input specification, an output specification and an execution constraint; the calling code generation agent generates source codes corresponding to the development requirements according to data type definitions and function calling specifications in the structured function requirement unit and the local project code library; calling a code test agent to generate and execute a test case based on the source code and the structured function demand unit; and after determining that a software deployment condition is met based on a test result, calling a code deployment agent to generate a deployment script and configuration information, and submitting the deployment script and the configuration information to a deployment execution engine to complete automatic deployment. According to the application, end-to-end automatic development from natural language requirements to application deployment can be realized.

The techniques described herein provide a risk assessment framework that enhances the functionality of software deployment systems in cloud-based platforms. Generally described, the present techniques evaluate and consolidate various risk scores to classify a given computing cluster within a software deployment strategy. In various examples, a deployment system collects node-level feature data from the computing cluster to generate a dataset to train a prediction model to calculate constituent risk scores. In another aspect, the deployment system aggregates constituent risk scores to determine an overall risk of software failure. Likewise, the deployment system considers diverse criteria such as virtual machine size and virtual machine density to determine an overall impact of software deployment failure. The deployment system then calculates a composite risk score for the computing cluster as a function of the risk of software deployment failure and the impact of software deployment failure.

The invention discloses an automatic deployment method, device and equipment for Linux environment software and a medium, and relates to the technical field of software deploy.The method comprises the steps that Linux environment information is collected on the basis of a programming language and in combination with a Linux system command, and the collected Linux environment information is stored as structured data; creating an improved model, and training by adopting pre-collected Linux environment software deployment case data, so that the trained improved model outputs a deployment strategy based on the Linux environment information; and according to the output deployment strategy, downloading an installation package of the software to be deployed and carrying out analysis processing on a software installation dependency relationship so as to realize automatic installation and deployment of the software based on scripts. According to the method, the software deployment in the Linux environment can be efficiently realized, the deployment error rate is reduced, and the adaptability is enhanced.

The invention discloses an incremental configuration file synchronous release method and device, computer equipment and a storage medium, and the method comprises the steps: executing an environment configuration operation on a code warehouse to generate an initial configuration file; carrying out construction processing on the configuration file in a deployment platform to form a task file which can be used for automatic deployment and automatic release; writing a newly added configuration item into the configuration file to generate a target configuration file; and according to the task archive, executing a synchronous publishing operation on the target configuration file on a deployment platform. By implementing the method disclosed by the invention, a tester does not need to manually debug and deploy scripts and verify environmental parameters, so that invalid human input during multi-environment adaptation is avoided, the software deployment period is effectively compressed, the possibility of operation negligence is reduced, deployment failure caused by operation sequence deviation during manual deployment is avoided, and the deployment efficiency is improved. The technical scheme can be applied to the financial field.

Techniques are provided for failure mitigation in software deployment pipelines using generative artificial intelligence (AI). One method comprises obtaining a request to merge code changes associated with a first branch of software code with a second branch of the software code; in response to the request: obtaining information characterizing the software deployment pipeline; applying at least a portion of the information to a classification model to obtain a prediction that an implementation of the request will result in a failure; applying, in response to the prediction that the implementation of the request will result in the failure, at least a portion of the information to a generative AI model, with failure information characterizing reasons for the failure, to obtain mitigation actions to mitigate the failure; and automatically initiating processing steps associated with at least one of the mitigation actions to mitigate the failure.

Systems and methods of determining and providing optimized deployment plans for deploying software to vehicles are disclosed. In some embodiments, a vehicle software deployment system evaluates one or more cost functions to determine relative costs of different deployment configuration options for deploying software to a vehicle, such as resource costs (e.g., bandwidth, compute, memory, etc.), isolation costs (e.g., limited access to input information, limited connectivity to other ECUs, etc.), performance costs, etc. Based on the evaluation of the one or more cost functions, the vehicle software deployment system determines an optimized deployment plan. Also, the vehicle software deployment system receives telemetry data from the vehicle and automatically determines updated optimized deployment plans in response to changes in configuration of the vehicle indicated in the telemetry data.

This disclosure relates to a method and apparatus for obtaining application packages across distributions, belonging to the field of software development technology. The method includes: creating and running a temporary container corresponding to the base image based on a base image adapted to the target application using containerization technology; obtaining the file system of the temporary container and packaging it into an archive format to generate a base root file system; decompressing the base root file system and creating a target isolation environment with the base root file system as the root directory based on root directory switching technology; installing the target application and runtime resources supporting the target application in the target isolation environment to construct the target root file system; obtaining the code corresponding to multiple functional modules and generating an environment distribution package based on the code corresponding to the multiple functional modules and the target root file system. This application can improve the compatibility and convenience of software deployment across distributions.

The invention discloses a software deployment compatibility prediction method and system based on a LightGBM algorithm, and belongs to the technical field of data analysis. The method comprises the following steps: acquiring environmental parameters of a historical deployment task and a dichotomy deployment result; performing sample balance and weight distribution processing on the data to construct a full-amount training set; based on the training set, a two-stage LightGBM architecture is adopted to train a prediction model, a first classifier learns a global mapping relation and outputs an initial prediction probability, and after a boundary fuzzy sample set is screened out according to the initial prediction probability, a second classifier learns fine feature differences of the boundary fuzzy sample set so as to determine a final prediction result. According to the method, the problem of unbalanced category distribution is solved through cooperation of SMOTE oversampling and sample weight adjustment, the discrimination capability of boundary fuzzy samples is enhanced by using a two-stage classification mechanism, and the defect of low prediction accuracy of a traditional method in a complex heterogeneous industrial environment is effectively overcome; and the compatibility evaluation automation level and the prediction reliability before the printing industry software deployment are obviously improved.

The invention relates to the technical field of power grid operation, and provides a power grid operation safety distance accurate sensing device and method in a non-fixed scene, and the device comprises a hardware device and terminal software which is in communication connection with the hardware device. The hardware device is integrated and deployed on a movable support or a holder, and comprises a laser radar sensor which is used for rapidly scanning a power grid operation scene and obtaining three-dimensional point cloud data including three-dimensional coordinates, laser intensity and echo information; the high-definition visible light camera is approximately parallel to the optical axis of the laser radar sensor and is used for collecting real-time video images of the operation scene; an AI visual analysis module and a point cloud processing unit are arranged in the embedded processor; and the terminal software is deployed on the edge computing equipment or the background server and is used for receiving and processing the data from the hardware device. According to the invention, the safe distance can be accurately sensed.

The technology disclosed herein provides a framework for quickly detecting faulty software deployments, using a sequence of different analysis models executed at different time increments after the deployment. The analyses may include different machine learning models. Periodically, the system collects data on each deployment during the given period, and applies a set of labelling functions to generate non-binary classifications. The non-binary classifications are used to generate labels using weak supervision, and the labels are used for training a supervised machine learning model. The trained models may be used in the sequence of different analyses executed for future software deployments.

The invention discloses a Java application lightweight deployment system and method based on dependency stability grading, and relates to the technical field of software deployment, and the method comprises the following steps: collecting dependency metadata, and generating a dependency list description file; packaging the Java application service code and the internal runtime library into a service code file, forming a service code ciphertext file, and generating a lightweight application deployment package; constructing a basic container mirror image, pulling the basic container mirror image and a lightweight application deployment package, decrypting a service code ciphertext file, downloading missing dependency and verifying a dependency fingerprint, and starting a SpringBoot application; and collecting dependency operation data, generating and issuing an incremental updating plan, executing the incremental updating plan, and updating the dependency stability score and the dependency stability level. And the stability of the deployment carrier and the operation reliability of the Java application are improved.

System, method, and software for software deployment. In an embodiment, a software deployment supervisor receives deployment parameters from an entity regarding deployment of software, identifies resource information for resources on a plurality of computing platforms to host the software, select a host computing platform based on the deployment parameters and the resource information, and deploy the software on the host computing platform. The software deployment supervisor implements a redeployment process by iteratively performing: monitoring the resource information to identify updated resource information for one or more of the resources, identifying downtime of the software, reselecting the host computing platform from the plurality of computing platforms based on the deployment parameters and the updated resource information, and redeploying the software on the host computing platform during the downtime of the software.

The application relates to a deployment method, system and device based on an automatic driving vehicle and a storage medium, the method comprising the following steps: M1: based on a vehicle-mounted controller of the automatic driving vehicle, acquiring a change data request of the vehicle-mounted controller, and outputting change data request information according to a ROS workspace of the vehicle-mounted controller; M2: based on the change data request information, checking version file information of the vehicle-mounted controller, acquiring a current version file package corresponding to a current version, comparing the current version file package with an upgraded version file package, and outputting a compensation package associated with the current version file package. The application not only realizes automatic driving software deployment with a higher automation level in a wide range, but also sorts priorities of matched automatic driving software and calls the automatic driving software with the highest priority, thereby greatly improving safety and reliability, and reducing manual participation and labor costs.

A set of software criteria including a condition that a software test be successfully performed prior to deploying a software package to a user device and location information indicating a storage location for status information of the software test is obtained. A testing status of the software package is analyzed using the status information at the storage location and software package is deployed to the user device as a result of the testing status confirming that the condition has been met in response to a request to deploy the software package to the user device.

Techniques are provided for software deployment workforce selection using security-based policy selection. One method comprises obtaining security skillset grades for workforce personnel associated with a software deployment pipeline; obtaining workforce selection policies that specify a security skillset grade required for workforce personnel; identifying, for a given microservice of an application, a given workforce selection policy applicable to the given microservice, wherein the given workforce selection policy is identified based on a security weight assigned to the given microservice, by aggregating one or more security weights for application programming interfaces of the given microservice; selecting workforce personnel to perform tasks related to the given microservice based on a comparison of the security skillset grades for the workforce personnel and the security skillset grade required for workforce personnel specified in the given workforce selection policy; and initiating at least one automated action based on a result of the selecting.

The invention relates to a software deployment optimization method based on a genetic algorithm, and the method comprises the steps: converting a service module of to-be-deployed software into a chromosome structure, representing a deployment position through a gene, carrying conflict sensitive information, and reflecting resource competition and dependency coupling deployment constraints; a self-rejection mechanism is executed after chromosome generation or genetic manipulation, potential conflicts are coordinated through gene fine tuning or local migration, and unrejected individuals do not enter subsequent evolution; after deployment position variation, dynamically monitoring service cascade change, suspending evolution and generating a substitution individual when a jump condition is met, and expanding a nonlinear deployment strategy; a dominant service is identified in evolution, and when the deployment position of the dominant service continuously changes, a structure inversion strategy is adopted to drive overall layout adjustment with the service as a core gene. And a genetic crossover stage: according to a deployment semantic consistency comparison structure, prohibiting direct crossover of conflicting individuals, adopting local window crossover and setting a protection area for a dominant service area to guarantee strategy integrity.

System, method, and software for software deployment. In an embodiment, a software deployment supervisor receives deployment parameters from an entity regarding deployment of software, identifies resource information for resources on a plurality of computing platforms to host the software, select a host computing platform based on the deployment parameters and the resource information, and deploy the software on the host computing platform. The software deployment supervisor implements a redeployment process by iteratively performing: monitoring the resource information to identify updated resource information for one or more of the resources, identifying downtime of the software, reselecting the host computing platform from the plurality of computing platforms based on the deployment parameters and the updated resource information, and redeploying the software on the host computing platform during the downtime of the software.

The application discloses an open source component bloodline analysis method and system, relates to the technical field of software supply chain security, and comprises the following steps: S1, automatically discovering open source components; S2, analyzing software components; S3, open source component fingerprint analysis; S4, generating an SBOM bill of materials; and S5, open source component bloodline analysis. The application realizes comprehensive tracking of a source code data processing process through a cryptographic hash function and a cosine similarity algorithm. When a component is modified, the component can also be quickly identified through a similarity analysis technology. The platform regularly collects file fingerprint features of common open source components, extracts characteristic values in combination with extracted open source software dependency packages, saves the characteristic values for comparison, identifies tampered code details in a jar package, locates open source component risks, and improves controllability and security management of a software deployment package.

Disclosed are systems and methods that provide a novel framework for automatically and / or dynamically managing real-time usage and / or implementation of user-based devices, applications and / or accounts in relation to electronic, digital and / or network-based services and / or platforms. The framework operates to provide innovative mechanisms for cloud-based software deployment and user experience management. The framework leverages artificial intelligence (Al) to create and provide an intuitive, personalized and efficient upgrade process for applications installed and / or executing on user devices. The framework can leverage an AI- driven chatbot to engage with users, trigger application upgrade processes and other computerized mechanisms for user device, application and / or account management.

The disclosed embodiments provide for rules-based deployment of software installations. In some aspects, operational parameters for a computer system are monitored over time to generate a historical database of values for the operational parameters. The computer system may include multiple instances of a software installation. A portion of the multiple instances is updated with a new version of software. The operational parameters are then monitored to quantify whether the new version results in an improvement or degradation of performance of the computer system. The improvement or degradation is based on comparing values of the operational parameters after deployment to their historical values. Depending on the evaluation of the operational parameters after the installation, the installation may be rolled back if a degradation is indicated. Otherwise, the new software version may be propagated to additional installation instances.

The invention relates to the technical field of software deployment, in particular to a software deployment method and system based on a distributed architecture. Comprising the following steps: establishing a plurality of service nodes based on system parameters, and constructing a deployment optimization model according to feature parameters of all the service nodes; obtaining state parameters of each service node, generating a deployment demand based on all the state parameters, and generating a primary deployment strategy according to the deployment demand and the deployment optimization model; establishing a monitoring time axis, and judging whether a correction parameter of a first-level deployment strategy is generated or not according to a preset monitoring time node; according to the invention, aggregation processing is carried out on all service nodes, a plurality of node sets are constructed, and a deployment optimization model is constructed according to the characteristics of each node set, so that the complexity of a deployment task is reduced, and the software deployment efficiency of a system is improved. By performing conflict analysis on the initial sub-strategies of each node set, resource contention and strategy conflicts among different node sets are avoided, global coordination is realized, and stable operation of the system is ensured.

The invention discloses a cross-platform software processing method and a computer readable storage medium, and relates to the technical field of computer system operation and maintenance and automatic deployment. Determining a patch file package capable of processing the customized operating system in a cross-platform manner; wherein the processing comprises software deployment, software upgrading or software repair; determining a target patch file matched with the target hardware platform from the patch file package according to the target hardware feature information of the target hardware platform and the target platform identifier; and under the condition that the target hardware platform passes the pre-check and the current to-be-processed content is successfully backed up, executing the target patch file, and determining whether to recover to a backup state or not according to an execution result. According to the method, software deployment and upgrade repair of the customized operating system are completed in a cross-platform manner, and the method adapts to diversified hardware environments.

This application discloses a method, apparatus, device, and medium for deploying an agentless antivirus component on a cloud platform, relating to the field of cloud platform technology. The method includes: obtaining CPU architecture information of a target node in a target cloud platform to obtain target CPU architecture information; determining the target CPU architecture type of the target node based on the target CPU architecture information, and searching for a target deployment package file of the corresponding target agentless antivirus component in a preset agentless antivirus component library based on the target CPU architecture type; issuing a deployment command for the target agentless antivirus component and sending the target deployment package file to the target node of the target cloud platform, thereby deploying the target agentless antivirus component to the target node of the target cloud platform. This solution enables the automatic deployment of agentless antivirus components on the cloud platform to meet the agentless security protection requirements for virtual machines in a cloud platform under a hybrid CPU architecture.

Systems, devices, methods, and computer readable media for determining potential issues with a software upgrade are disclosed. In one implementation, the disclosed system includes at least one processor and at least one non-transitory memory storing instructions to perform operations when executed by the at least one processor. The operations include training a machine learning model on historic trouble ticket data; receiving data relating to a planned software upgrade; providing the received data to the trained machine learning model, wherein the trained machine learning model generates a tag value; calculating a risk score based on the tag value; and determining a client-specific action to be performed relating to the planned software upgrade and based on the risk score.

Techniques are provided for software deployment pipeline evaluation using a presence of designated functions. One method comprises obtaining information characterizing a set of designated functions; determining one or more of: (i) whether at least one stage of a given software deployment pipeline comprises one or more of the designated functions in the set and (ii) whether an execution of one or more of the designated functions in the at least one stage of the given software deployment pipeline succeeded; and initiating one or more automated actions based at least in part on at least one result of the determining. The designated functions may be specified for different technology types. Job logs associated with the at least one stage of the given software deployment pipeline may be searched for commands that execute one or more of the designated functions.

The invention discloses a self-adaptive multi-mode edge vehicle detection and identification system. The system is composed of a sensor collection module, an edge calculation module, a software deployment module, a communication module and an energy management module, the sensor collection module is used for obtaining images, distances and point cloud information around a vehicle, and the edge calculation module locally executes multi-modal fusion deep learning reasoning to achieve vehicle detection and recognition. The software deployment module dynamically manages reasoning tasks based on a Docker container and a lightweight arrangement system, the communication module has an event triggering mechanism, uploads original data when detection is abnormal or the confidence coefficient is low, and only transmits and recognizes abstracts in a normal state, and the energy management module automatically adjusts computing power and power consumption according to real-time loads, so that low-energy-consumption and high-performance operation is realized. According to the system, vehicle identification and classification can be completed at the edge end, the cloud computing pressure and communication delay are remarkably reduced, and the real-time performance, stability and energy efficiency in an intelligent traffic environment are improved.

Computer-implemented methods, computer program products, and computer systems include program code continuously detecting software versions of custom resources and operators, where the software versions are associated with software provided as a service to the custom resources by an application programming interface (API) communicatively coupled to the operators. Based on the detecting, the program code determines, for each custom resource, whether an operator in the multiple tenant environment is providing a software version of the custom resource. Based on determining for a given custom resource of the custom resources that no operator in the multiple tenant environment is providing the software version of the given custom resource, the program code automatically deploys a new operator in a designated namespace, where the new operator provides the software version of the given custom resource to the given custom resource and the designated namespace is dedicated to the new operator.

This invention discloses a method for obtaining server error information using a BMC (Browser Control Center), relating to the field of fault diagnosis technology. The method includes: mapping the host serial port to the BMC using a CPLD (Port Component Adapter); using the CPLD to assist in determining the server's phase and performing serial port debugging; collecting startup data by the BMC during the power-on startup phase; and collecting data and issuing error warnings based on serial port input or the BMC's input during the runtime phase. This method solves the technical problem of high hardware support and software deployment requirements for server error information collection in existing technologies, achieving a simplified server error information collection process.