155 results about "Software deployment" patented technology

The present disclosure pertains to systems, computer program products, and methods for an automated mesh service-based deployment intelligence system. This system is designed to streamline the deployment of software by integrating a processing device and a non-transitory storage device. The storage device contains instructions that, when executed by the processing device, enable the ingestion of data from various sources such as monitoring systems, databases, and application performance management tools. Once ingested, the data is stored and processed to discern deployment patterns and detect any anomalies. Utilizing a machine learning model, the system anticipates potential deployment issues by analyzing this data. It then orchestrates the deployment of software artifacts accordingly, taking into account the insights gained from the machine learning model. Furthermore, the system is capable of real-time optimization of the deployment process to preemptively resolve any predicted issues, thereby enhancing the efficiency and reliability of software deployment operations.

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 invention belongs to the technical field of software deployment, and particularly discloses a Linux system equipment software remote deployment method and system and a program product, a software remote deployment mode is adopted, configuration files and operation commands compiled by a user are analyzed through a client side, all deployment operation commands are responded in sequence, and a Linux system host is remotely connected in a butt joint mode. Corresponding deployment process instructions are compiled and executed, so that deployment operations such as receiving verification, software version replacement and program restarting of software files are automatically completed. According to the method, human errors can be avoided, the software deployment operation process is simplified, time consumed by developers for updating software versions can be effectively saved, the software development efficiency is improved, meanwhile, instructions in the deployment process are transferred and compiled based on the compiling tool, and transmitted software files are verified, so that the software development efficiency is improved. And the security and reliability of the software deployment process can be effectively ensured.

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 utility model relates to an AFE-based battery management system, which comprises a battery equalization driving circuit and an MCU unit, the battery equalization driving circuit comprises an AFE chip, a current balancing circuit, a charging control enabling circuit and a discharging control enabling circuit, the AFE chip is responsible for converting analog signals such as voltage, current and temperature of a battery into digital signals, and the MCU unit is responsible for outputting the digital signals. The MCU is connected with the AFE chip through an I2C bus, the MCU can conveniently read battery data collected by the AFE chip through the I2C bus and issue a control instruction according to needs, and meanwhile, due to the design, software deployment is also facilitated, and through coordinated control of the AFE chip and the current balancing circuit, the battery data can be further processed by the MCU, so that the service life of the battery can be prolonged, and the service life of the battery can be prolonged. And high-efficiency equalization of each single battery in the battery pack is realized through the internal current balancing circuit and the external current balancing circuit, so that each single battery can work in an optimal state, thereby improving the overall performance of the battery pack and prolonging the service life of the battery pack.

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 relates to a software deployment technology, in particular to a remote deployment method for a software platform user interface, which comprises the following steps: constructing an independent service process at a server, and providing an HTTP (Hyper Text Transfer Protocol) service by the service process; the service process communicates with a software platform process in real time to obtain a configuration file of a software platform user interface and operation data of a software platform application program, and the software platform is a third-party software platform; analyzing the configuration file of the software platform user interface through the service process, and locally generating a webpage containing the corresponding user interface at the server according to the information of each user interface in the configuration file; when the webpage is opened in a local browser of the server side, the service process obtains an opening behavior so as to refresh the operation data of the application program of the software platform to the webpage in real time; and / or, the service process responds to the user interface operation on the webpage so as to transmit an operation instruction to the software platform in real time, and then corresponding operation is executed through the software platform.

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.

A method performed by a system including a collector unit, an analyser unit, and a scheduler unit, comprises receiving a data comprising at least one of a hardware data, a traffic data and a service data related to one or more devices and one or more applications, feeding the data a trained model, determining, through the trained model, one or more patterns of the data, providing, an output from the trained model to the scheduler unit, performing, through the network, software deployment on the one or more devices based on the output.

The invention discloses an unmanned surface vehicle multi-source target detection method and related equipment based on a domestic deep learning software and hardware platform. In the method, a data acquisition unit senses the environment by using a visible light camera, an infrared camera and a laser radar multi-source sensor; the single-source data target detection unit performs target detection on each data source, and comprises a target detection algorithm based on a domestic deep learning reasoning framework MNN and a traditional laser radar point cloud target clustering algorithm; the multi-source data fusion analysis unit performs data synchronization and fusion analysis on output results of all algorithms to obtain a final accurate target detection result; the system provided by the invention overcomes the defect of sensing capability of a single sensor, supports all-day and all-weather operation, and improves the accuracy and confidence of a detection algorithm; algorithm software is deployed in a domestic ruggedized computer provided with a Feiteng D2000 processor and a Jingjia micro JH920 display card, and is autonomous and controllable.

The invention provides a software development application updating processing method and system based on cloud computing, and the method comprises the steps: analyzing a dependency relationship between software application components, and generating a software deployment topological graph; preheating the target computing resource on the cloud platform to generate a dynamic resource pool, and executing an application version replacement operation in the dynamic resource pool; when the triggered service is abnormal, switching the abnormal service traffic to a backup resource node, updating the abnormal software application component at the node, and generating an updated software application component; re-accessing the updated software application component into the dynamic resource pool so as to complete software development application updating processing; according to the method, accurate matching of the updating task and the computing resource is realized, the consistency of version replacement is guaranteed, and the problems of low deployment efficiency and insufficient stability caused by confusion of dependence of software updating in a traditional cloud environment are solved.

The invention discloses a computer software development data processing system based on big data, relates to the field of software development, and solves the problem that the computer software development data processing system is poor in application effect. Comprising a development data module used for obtaining the function resource occupancy rate and the function static complexity corresponding to each software function to obtain software development function data, and a resource investment module used for obtaining the fund investment degree corresponding to each software function to obtain fund investment data, the function concurrence module is used for obtaining function concurrence complexity corresponding to each software function to obtain function concurrence data, and the software deployment module is used for performing server selection and deployment on the computer software according to the software development function data, the fund investment data and the function concurrence data. According to the method, the scientificity and applicability of a computer software deployment result can be improved.

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 invention provides a vehicle-based software deployment method and device, electronic equipment and a medium, and the method comprises the steps that a software configuration list of a target vehicle is called, the software configuration list comprises a plurality of configuration items, and each configuration item comprises a software identifier and a processing identifier; for each configuration item in the software configuration list, executing the following deployment processing: searching an actual processing unit corresponding to the software identifier of the configuration item in the software deployment list of the target vehicle, and if the actual processing unit corresponding to the configuration item is different from a preset processing unit, executing the deployment processing; and if not, replacing a processing identifier indicating a preset processing unit with a processing identifier corresponding to a software identifier corresponding to the configuration item in a software deployment list so as to execute software deployment for the target vehicle based on the software deployment list. Through the vehicle software deployment method and device, automatic verification and correction of the mapping relation between the software component and the processing unit are achieved, and the accuracy and execution efficiency of vehicle software deployment are improved.

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.

The invention provides a system and a method for remotely monitoring magnetic suspension equipment. The system comprises management control software, equipment checking software, a magnetic suspension cloud platform and an Internet of Things gateway, the management control software and the equipment checking software are deployed in the terminal; the Internet of Things gateway is mounted on the magnetic suspension equipment; the magnetic suspension cloud platform is in communication connection with the terminal and the Internet of Things gateway through a double-layer encryption transmission protocol; the magnetic suspension cloud platform receives the magnetic suspension equipment alarm information and the magnetic suspension equipment state information sent by the Internet of Things gateway, and analyzes and stores the magnetic suspension equipment alarm information and the magnetic suspension equipment state information; when a viewing instruction sent by the terminal is received, acquiring data corresponding to the viewing instruction and sending the data to the terminal for visual display; and when a control instruction sent by the terminal is received, forwarding the control instruction to the Internet of Things gateway to enable the magnetic suspension equipment to execute the control instruction. The system optimizes the remote monitoring, intelligent diagnosis and automatic adjustment capabilities of the equipment, enhances the data security, and promotes the development of the magnetic suspension equipment towards a more efficient and intelligent direction.

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 invention belongs to the software deployment technology, and particularly relates to a cross-platform interaction method and system of desktop software in a software platform, and the cross-platform interaction method comprises the following steps: taking the software platform as a server, constructing an independent host service process at the server, and providing an HTTP hypertext transfer protocol service by the host service process; the host service process displays an operation interface of desktop software in a child window position area of an interface frame set by the host service process; the host service process also sets a screenshot buffer area for the sub-window to store a screenshot of the sub-window, and sends the screenshot of the sub-window to the client, so that the screenshot is displayed and published on the client through a webpage; and when the screenshot displayed on the webpage is triggered, the host service process obtains a triggering behavior through the client so as to execute the operation of the running interface corresponding to the screenshot.

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.