198 results about "Deployment environment" patented technology

The invention discloses an intelligent file service system generation method based on a low-code development mode. The method comprises the following steps: constructing a file service core data model, defining file metadata fields, a storage structure and an association relationship, and forming a standardized data architecture. According to the method, the core data model, the visual page component library and the drag-and-drop workflow template are constructed, linkage binding of data-component-process is achieved in combination with a low-code engine, and the efficiency bottleneck of a traditional development mode is broken through; through a three-way real-time synchronization mechanism, real-time hot repair and dynamic branch judgment, page layout and flow rule adjustment can take effect immediately through visual configuration, and the problem of dynamic demand response lag is solved; through the intelligent resource scheduling logic compiled through environment adaptation, a differentiation scheme is generated for different deployment environments, the configuration difference influence under a private cloud and server mixed deployment scene is remarkably reduced, the deployment failure rate is greatly reduced, and the system online stability is improved.

The invention relates to the field of waterproof sockets, and discloses a multi-scene adaptive intelligent waterproof socket remote regulation and control system and method, and the method comprises the steps: obtaining multi-source sensing data of a socket deployment environment, and constructing a multi-scene adaptive input data set in combination with a parameter fusion strategy and a signal interference compensation mechanism; environment feature recognition is carried out on the multi-scene adaptive input data set, and typical application modes in different scenes are extracted by using a socket state tag extraction algorithm; based on the state mapping relation model, a remote regulation and control strategy optimization algorithm is adopted to dynamically analyze the operation state of the socket, and the matching degree of the current operation mode and the environment scene is identified; an intelligent judgment mechanism is introduced, pre-execution verification is carried out on the multi-scene regulation and control suggestion set, and potential control conflicts are recognized in combination with historical operation data and real-time control instructions; and performing instruction issuing management on the remote control behavior of the socket based on the optimized execution strategy. The invention has the advantage of improving the universality of the intelligent waterproof socket.

The invention relates to the technical field of data transmission of the Internet of Things, in particular to a self-adaptive data compression and transmission method of a low-power-consumption wide-area Internet of Things. According to the method, a three-layer collaborative architecture comprising an equipment layer, a fog computing layer and a cloud computing layer is constructed, data preprocessing and feature analysis are performed on the equipment layer, and data types, entropy values and repeatability information are extracted; the fog calculation layer selects an optimal compression algorithm based on a multi-dimensional decision engine, and reduces redundancy through spatial-temporal correlation analysis and data aggregation; the cloud computing layer collects compression performance data, adopts reinforcement learning and federal learning to train a global optimization model, and dynamically issues strategy parameters; and the fog node adaptively adjusts a compression strategy in combination with the system state to realize the optimal balance between the compression ratio and the reconstruction precision. The method is suitable for field deployment environments with limited electric quantity and network, has the advantages of low power consumption, high efficiency, strong adaptability and the like, and can be widely applied to Internet of Things scenes such as remote monitoring, smart energy and the like.

The invention relates to the technical field of computers, in particular to a dynamic configuration initialization and version synchronization method and system for multi-agent service, and the method comprises the following steps: obtaining a database update lock, constructing a database migration script dependency relationship, determining a current version of a database mode, upgrading and deploying the database mode, and verifying agent versions. The method has the beneficial effects that a rigorous database updating lock control mechanism, perfect migration script dependency chain verification, agent version management, port availability detection, idempotence and transaction safeguard measures are introduced, so that the processes of database structure upgrading and agent version initialization in a distributed deployment environment can be effectively supported; and the data consistency, the operation stability and the service continuity of the system are ensured.

The invention discloses a model deployment method and device combined with hardware deployment, and the method comprises the steps: constructing a hardware topological graph corresponding to equipment nodes according to the equipment nodes in a target model deployment environment and a connection relation corresponding to each equipment node; analyzing a dependency relationship of a target operator in the target model to generate a model calculation graph corresponding to the target operator; calculating a target matching value between the equipment node and the target operator by using a preset evaluation algorithm according to the hardware topological graph and the model calculation graph; and determining a deployment scheme corresponding to the target model according to the target matching value and a preset grading strategy. By sensing a dynamic matching mechanism of a hardware state and a model calculation graph in real time, an optimal deployment strategy is automatically generated, and adaptive optimization is triggered when hardware topology or resource conditions change, so that real-time collaborative adaptation of a model segmentation strategy and a heterogeneous environment is realized, and the deployment automation level and the system elasticity are remarkably improved.

The invention discloses a heterogeneous document structured data extraction system and method based on multi-modal fusion, and the method comprises the following steps: S1, receiving a heterogeneous document, and carrying out the preprocessing; s2, extracting visual and semantic modal information based on the visual backbone network and an OCR module, and fusing spatial coordinates, text content and layout features; s3, receiving a dynamic target mode Schema, executing field-level semantic matching, and calculating semantic similarity; s4, performing consistency verification, verifying and filling the field values, and correcting the associated fields; s5, converting the format into a data file in a specified format, and reserving fields to be mapped with an original document semantic block; s6, automatically switching a cloud mode and a local mode according to a deployment environment; and S7, outputting the data file of the target field. According to the method, high-precision and low-delay structured data extraction of the heterogeneous format document can be realized, and the automatic processing efficiency and the data credibility are improved.

The invention provides a software deployment method which can be applied to the technical field of artificial intelligence. The software deployment method comprises the following steps: in response to received code data uploaded by a user, analyzing the code data to obtain a dependency relationship among software items in the code data, and determining a priority corresponding to the dependency relationship; according to the priorities, the software projects are constructed in sequence to generate target software; according to the deployment requirement of the target software, searching from a pre-constructed knowledge base to obtain configuration information matched with the deployment requirement, and configuring a target deployment environment according to the configuration information; and extracting deployment parameters from the target deployment environment, and deploying the target software to the target deployment environment according to the deployment parameters. The invention further provides a software deployment device and equipment, a medium and a program product.

The invention relates to the technical field of software engineering management, and discloses a computer software development information management system and method. According to the method, real-time multi-dimensional association among four entities, namely a demand entity, a code entity, a test entity and a deployment entity is realized by constructing a dynamic knowledge graph center, and an end-to-end closed-loop control system is formed; a full-link traceable network is dynamically established through explicit binding, implicit capturing and cross-layer traceability mechanisms, demand omission marks are automatically repaired, and accurate attribution of environmental problems is achieved; manual operation blind areas are eliminated through automatic means such as developing and submitting forced association, automatically freezing branches when test fails and deploying environment consistency verification; based on a risk thermodynamic diagram of full-link topology penetration and fusion defect density and change frequency, active early warning of a high-risk module is realized, and controllability and quality assurance capability of a software development process are remarkably improved.

The invention relates to the technical field of data center machine room energy conservation, and discloses a machine room cold channel closed management method and device and terminal device.The method comprises the following steps that the state of a cold channel is obtained by deploying environment monitoring nodes, a heat-flow coupling model and a stability function are constructed, a feedback controller is designed to generate an air door and refrigeration control instruction, and a heat-flow coupling model is established; linkage adjustment of the air velocity and the temperature is achieved, closed-loop control is constructed, and efficient and energy-saving management of a cold channel is achieved; the device comprises an environment monitoring module, a state modeling module, a stability evaluation module, a state feedback control module and an execution linkage module. Through state fusion updating, a component control structure, a stability function driving strategy and a linkage execution mechanism, fine adjustment and efficient cooperative control of the temperature and airflow of the cold channel are achieved, the adjustment stability and response efficiency are improved, energy consumption is remarkably reduced, and the problems that an existing system is poor in control precision, poor in linkage performance and the like are solved.

The invention provides an accelerator card hybrid deployment method and device and electronic equipment, and the method comprises the steps: carrying out the segmentation of an overall calculation graph, and obtaining a segmented calculation graph; inputting the calculation characteristic index of the segmentation calculation graph into a performance prediction model to obtain performance prediction data, output by the performance prediction model, of the segmentation calculation graph executed by various acceleration cards; and based on the performance prediction data of the segmentation calculation graph, carrying out acceleration card hybrid deployment on the target task. According to the method provided by the invention, the integral calculation graph is segmented to obtain the segmented calculation graph; inputting the calculation characteristic index of the segmentation calculation graph into a performance prediction model to obtain performance prediction data, output by the performance prediction model, of the segmentation calculation graph executed by various acceleration cards; based on the performance prediction data of the segmentation calculation graph, accelerator card hybrid deployment is performed on the target task, so that accurate accelerator card hybrid deployment is realized, and then the execution efficiency of executing the target task is improved and the power consumption required by execution is reduced in a hybrid deployment environment.

The embodiment of the invention discloses a security deployment method and device for enterprise-level applications and a medium, and relates to the technical field of security deployment, and the method comprises the steps: obtaining application topological graph data generated by a preset visual development platform, so as to split the application topological graph data into an atomized micro-service unit set through a quantization deconstruction engine, generating a micro-service dependency tree; scanning the target deployment environment, extracting an environment feature fingerprint of the target deployment environment, and generating an environment adaptation layer code according to the environment feature fingerprint; injecting an environment adaptation layer code into each atomized micro-service unit to generate an environment perception type micro-service package, and constructing a cross-cloud communication link matrix based on the micro-service dependency tree; and based on the environment-aware micro-service packet and the cross-cloud communication link matrix, executing a deployment verification test in the sandbox environment, generating a deployment verification report, and according to the deployment verification report, distributing the environment-aware micro-service packet to a target deployment environment node.

In various examples, a declarative configuration format defines a machine learning pipeline using nodes and edges that define the data flow between those nodes. Partial pipelines may be defined independently and referenced by multiple full pipelines. The configuration format may allow for constructing applications for a range of hardware and deployment environments. The system may support both graphical and programmatic interfaces for defining, modifying, and visualizing pipelines. The machine learning pipelines may be implemented on or interact with an underlying machine learning pipeline framework that provides runtime objects and a plugin architecture. The configuration data may be used to generate an application that includes the processing components and corresponding runtime dependencies of a machine learning pipeline in a containerized image.

A method, apparatus, and non-transitory computer-readable medium for packaging and deploying applications using virtualized execution units (VEUs). A development apparatus instantiates a development environment, captures changes to identify build elements, and packages the build elements into a VEU. An execution apparatus receives and parses the VEU to obtain build elements, determines corresponding deployment actions, and applies these actions to instantiate the application in a deployment environment, automating application deployment while preserving state.

The invention discloses a multi-model intelligent management scheduling method and system. The method comprises the following steps: receiving a to-be-registered model and corresponding metadata, and executing integrity check to generate an integrity check result; generating a model identifier for the to-be-registered model according to an integrity check result, and storing the model identifier into a model storage warehouse; receiving a deployment request, and extracting a target model file and target metadata according to the deployment request; selecting a target computing node according to the target metadata; configuring a running environment according to a deployment environment requirement, and combining the target model file with the running environment to obtain a container mirror image; creating a containerized operation instance based on the container mirror image, and configuring a network access endpoint; predicting the page view of the network access endpoint to obtain predicted page view data; and adjusting the number of the containerized operation instances according to the predicted page view data and the system resource use data. By implementing the technical scheme, the problems of hysteresis and lack of perspectiveness of resource adjustment based on a real-time system load are solved.

Container image optimization is provided for a deployment environment. An indication of a plurality of container images to be deployed in a deployment to provide a plurality of services within a computing environment is obtained. The plurality of container images of the deployment has a total layer size of a plurality of image layers of the plurality of container images. At least one container image of the plurality of container images is reconfigured to adjust the total layer size for the deployment. The reconfiguring is based on one or more deployment characteristics of the deployment and includes replacing at least one image layer of a container image of the at least one container image with one or more other image layers configured based on the plurality of container images to be deployed.

Methods and systems for managing services (e.g., the development, deployment, and configuration of services) are disclosed. User intent associated with policy related configurations for computer implemented services are directly and automatically translated into a set of user intended network policy and rules to be applied (e.g., enforced) by the computer implemented services without any need for the user to understand how the policies will be configured and implemented by the service deployment environment.

The invention provides a network security event identification model deployment method and system in the technical field of network security and machine learning crossing. The method comprises the following steps: S1, acquiring historical monitoring data to construct a source domain data set; s2, training the created security event identification model through the source domain data set, and dynamically pruning and quantifying the security event identification model in the training process; s3, performing knowledge distillation on the security event identification model; s4, acquiring real-time monitoring data of a to-be-deployed environment to construct a target domain data set, and performing data drift compensation training on the security event recognition model after knowledge distillation based on the target domain data set and the source domain data set; and S5, carrying out migration deployment on the security event identification model after data drift compensation training through a containerization technology and a shadow deployment technology. The method has the advantages that the flexibility and adaptability of network security event identification model deployment are greatly improved, and the deployment cost is reduced.

The invention discloses a lightweight model transfer learning method based on knowledge distillation, and belongs to the technical field of artificial intelligence security, and the learning method comprises the steps: S1, constructing a security initialization module, and deploying a teacher model in a preset cloud server; s2, a lightweight student model is generated, and a preset processing module receives the teacher model and performs model compression processing on the teacher model; s3, knowledge migration safety protection is carried out, and the lightweight student model is trained; s4, deploying environment security binding, and carrying out binding verification on the trained lightweight student model and preset target operation equipment; and S5, full-cycle audit tracking is carried out, a traceable audit management module is established, and a full-process operation log from teacher model compression to lightweight student model deployment operation is recorded. Through the steps S1-S5, structural information leakage of the original teacher model is avoided, and dynamic permission control is performed on the teacher model in a deployment link.

The invention discloses a Web-based visual editing method, belongs to the technical field of industrial configuration, and aims to solve the problems of complex deployment of traditional configuration software and insufficient Web scheme performance. The method comprises the following steps: constructing a front-end architecture (Vue. Js + Konva.js) containing a graphic element module, a canvas management module and the like; the primitive adopts JavaScript prototype chain inheritance + SVG / Canvas double-layer rendering, and a canvas is divided into four layers and is provided with a quadtree index; an editor and an expression engine are generated by using a factory pattern for attribute configuration; page management supports state capture recovery; real-time communication is based on WebSocket + PubSub, and data aggregation and incremental updating optimization are combined. Cross-platform zero deployment and Web environment near-desktop performance are achieved, editing efficiency is improved, industrial protocols such as OPC UA are compatible, and the method is suitable for the field of energy and intelligent manufacturing.

An MLOps extension method and apparatus in a multi-cloud native environment, and a medium, relating to the technical field of multi-cloud heterogeneity. The method comprises: receiving a user-defined infrastructure template, configuration parameters, and access credentials of different cloud platforms, and collecting cloud computing resources from a plurality of clouds; putting model code and a data processing script into a version control system, and uploading data to a data storage service; pre-allocating computing resources and storage resources, and installing and verifying all dependencies in a pre-deployment environment; creating isolated sandbox environments on the cloud platforms to run comprehensive simulation tests; packaging models and applications, and deploying same in production environments on the cloud platforms; configuring a monitoring tool to monitor the performance of the models and the applications in real time, and on the basis of monitoring data, optimizing model parameters and application configurations. The present invention can reduce incompatibility and failures in a multi-cloud environment, and improve the reliability and efficiency of model services and continuous application deployment.

The invention provides a PXE network card initialization method and device compatible with 64-bit addressing and a medium, and relates to the technical field of network card initialization, and the method comprises the steps: discovering a network card device; reading a PCI configuration space of the network card equipment, obtaining information of an extended ROM base address register, and mapping a network card Option ROM to a memory shadow region; when the access of the memory mapping input and output mode fails, the initialization code performs read-write operation on the hardware register through a self-defined capability structure in a PCI configuration space of the network card equipment; based on the successfully accessed hardware register, network card hardware initialization is completed, and a PXE protocol stack structural body is established in a memory; registering system firmware as PXE starting equipment, and entering a protocol interaction stage with a PXE server; according to the invention, smooth development of a subsequent network boot flow is guaranteed, and the deployment success rate and the system stability in a large-scale batch installation scene are improved.

The present application provides a cloud-edge collaborative 1+6+N smart thermal power plant intelligent infrastructure system and deployment method. The system comprises: a lightweight IaaS layer, a lightweight PaaS layer, and a lightweight SaaS layer. The lightweight IaaS layer is used for a user to independently deploy and run any software or application program, to implement basic computing, storage, and calling of network resources for the smart thermal power plant intelligent infrastructure system. The lightweight PaaS layer is used to configure an application program development and deployment environment for any software or application program deployed by the user, and to access data of a terminal sensing device and a control device of the smart thermal power plant intelligent infrastructure system. The lightweight SaaS layer is used to deploy applications and services used in the smart thermal power plant intelligent infrastructure system in the form of a microservice architecture, to meet various service requirements of the smart thermal power plant intelligent infrastructure system. The technical problems of how to provide an efficient, safe and visual solution for improving infrastructure management, operation and maintenance of a thermal power plant, and how to significantly improve the infrastructure management level and efficiency of operation and maintenance the power plant, are resolved.

In some implementations, a platform as a service (PaaS) orchestrator may receive a set of values for a set of parameters of a software component template for a software component. The PaaS orchestrator may generate the software component using the set of values for the set of parameters. The PaaS orchestrator may execute the software component in a sandbox environment. The PaaS orchestrator may validate the software component for deployment. The PaaS orchestrator may deploy the software component in a deployment environment of the PaaS orchestrator. The PaaS orchestrator may detect a network event relating to the deployment environment. The PaaS orchestrator may identify a network management action associated with the network event. The PaaS orchestrator may one or more instructions to cause the network management action to be performed.

The invention discloses a remote copy link processing method and device, a medium and a program product, and relates to the technical field of data storage, and the method comprises the steps: detecting the health state of a link; the method comprises the following steps: initially setting a link in an online active state and putting the link into an optimal path set, when detecting that the health state of the link exceeds a preset threshold value, timely converting the link into an inactive state and moving the link to a degraded path set, and if the degraded link is recovered to be healthy in a set period, putting the degraded link into the optimal path set again. According to the mechanism, the link quality can be accurately controlled, rapid switching can be realized, and the problems of zero drop or timeout of input and output and the like can be effectively avoided; the link is preferentially selected from the optimal path set for communication, and the optimal link is selected from the degraded path set when the optimal path set is empty, so that the remote replication communication rate can be guaranteed, the complex link deployment environment can be flexibly coped, the remote replication service is ensured to always run on the link with higher quality, and the communication efficiency is improved. And the reliability and the stability of the service are practically ensured.

The invention discloses a Java JAR packet encryption protection method and device, equipment and a medium, and the Java JAR packet encryption protection method comprises the steps: splitting a JAR packet decryption key into two fragments, and statically storing the two fragments in a starter program and a dynamic link library in a nonlinear confusion structure, thereby avoiding the exposure of a key plaintext. During starting, the starter transmits the first secret key fragment to the dynamic link library through secure inter-process communication, and the first secret key fragment is combined with the second fragment in the memory to restore a complete secret key. And during class loading, the Java agent intercepts a request and transmits an encrypted class byte code to the dynamic link library through the JNI, the encrypted class byte code is decrypted by using the restored key, and a plaintext is returned for loading. According to the method, static decompilation and dynamic memory dump attacks are prevented, and the anti-reverse and anti-leakage capabilities of Java software in a privatized deployment environment are remarkably improved.

The invention discloses a dependent packet checking method and system oriented to cloud deployment. The invention relates to the technical field of SAAS platforms. Loading rules in the dependency check configuration library, and preparing resources and environments required by the dependency packet information acquisition module and the automatic detection module; connecting to a cloud platform or an application deployment environment, scanning application items, and identifying and extracting name and version number information of all dependent packages; a dependency list is generated to serve as input data of follow-up inspection; loading rules in the dependency check configuration library, and preparing resources and environments required by the dependency packet information acquisition module and the automatic detection module; connecting to a cloud platform or an application deployment environment, scanning application items, and identifying and extracting name and version number information of all dependent packages; a dependency list is generated to serve as input data of follow-up inspection; receiving a dependency list as input; sequentially reading rules in the dependency check configuration library; for each rule, analyzing a rule condition, and matching the rule condition with a dependency list; and judging whether the rule is satisfied.

Methods and systems for managing onboarding of a data processing system (device) are disclosed. During a first startup of the device, the device may use onboarding data to attempt to onboard to a deployment. An occurrence of a failure event that prevents onboarding of the device may be identified by a management controller of the device. Based on the identification, replacement onboarding data for the device may be requested and obtained by the management controller from a management system. Using the replacement onboarding data, the device may perform a second startup to complete onboarding of the device. After completion of onboarding of the device, a computer-implemented service may be provided using the device and / or the deployment.

The application relates to the technical field of data center computer room energy-saving, and discloses a computer room cold aisle sealing management method, a device and terminal equipment, the method comprises the following steps: obtaining a cold aisle state through a deployment environment monitoring node, constructing a heat-flow coupling model and a stability function, designing a feedback controller to generate a damper and refrigeration control instruction, realizing linkage regulation of air flow rate and temperature, constructing a closed-loop control, and realizing efficient energy-saving management of the cold aisle; the device comprises an environment monitoring module, a state modeling module, a stability evaluation module, a state feedback control module and an execution linkage module. Through state fusion updating, component control structure, stability function driving strategy and linkage execution mechanism, the application realizes fine adjustment and efficient collaborative control of the cold aisle temperature and airflow, improves regulation stability and response efficiency, significantly reduces energy consumption, and solves the problems of poor control precision and weak linkage of the existing system.

Examples of the present disclosure describe systems and methods for providing enhanced security in edge computing environments. A first aspect describes a method for moving security features dynamically applied to an application at a first deployment location to an application at a second deployment location. A second aspect describes a method for locally expanding / contracting an instance of a deployed application. A third aspect describes a method for redirected network traffic associated with detected malicious conduct from a first application deployment environment to a secured second application deployment environment. A fourth aspect describes a method for performing multi-stage network traffic filtering.

The present invention discloses a lightweight model transfer learning method based on knowledge distillation, belonging to the field of artificial intelligence security technology. The learning method includes: S1, constructing a security initialization module and deploying a teacher model on a preset cloud server; S2, generating a lightweight student model, where a preset processing module receives the teacher model and performs model compression processing on the teacher model; S3, knowledge transfer security protection, training the lightweight student model; S4, securely binding the deployment environment, binding and verifying the trained lightweight student model with a preset target operating device; S5, full-cycle audit tracking, establishing a traceable audit management module to record the full-process operation logs from the compression of the teacher model to the operation of the deployed lightweight student model. Through steps S1-S5, the present invention avoids the leakage of the original teacher model structure information and performs dynamic permission control on the teacher model in the deployment link.