106 results about "YAML" patented technology

The invention provides a semantic-based migration and consistency verification method, system and device and a medium, and relates to the technical field of databases. The method comprises the following steps: performing metadata topology scanning analysis by obtaining metadata, including generating an abstract syntax tree and constructing a semantic graph; according to a metadata topology scanning analysis result, performing data mapping and conversion, including loading a YAML rule and generating corresponding type mapping and constraint conversion; data migration is carried out through primary key fragmentation parallel migration, batch writing optimization and real-time double-writing verification; through structure-data-business three-layer verification, simulation business SQL comparison and automatic difference repair, the integrity of migrated data and business compliance are ensured, the semantic gap problem in heterogeneous database migration is solved, high-precision and high-efficiency database migration is realized, the migration efficiency is improved, and the data migration efficiency is improved. The method is suitable for scenes with strict requirements on data consistency and migration efficiency, such as financial, government affair and enterprise-level data centers.

The invention relates to a DevOps-oriented containerized test environment deployment system and method, and belongs to the technical field of Internet. A multi-service test environment is defined through a YAML file, environment variables are managed in combination with an. Env configuration file, dependency is optimized through dependency layering and a cache library, a container mirror image is constructed, and a container cluster is started; managing multiple tasks through a centralized scheduling architecture, and realizing resource isolation of a container cluster by using Cgroup; adjusting the number of container instances in real time based on a capacity expansion and contraction mechanism; a bridging network is used for automatically distributing a container IP, a built-in domain name system analyzes a service name dynamic discovery service, and rapid reconstruction of a containerized test environment is achieved. According to the method, a YAML and environment variable separation mechanism is adopted, real-time loading of development, testing and production environment configuration is supported, the deployment time is shortened to the second level, the resource utilization rate is increased to 90% from 60%, the multi-service communication difference is eliminated, and efficient interconnection between containers is ensured.

The invention provides an FaaS edge-center deployment decision-making system based on a large language model. The FaaS edge-center deployment decision-making system is used for solving the intelligent deployment problem of an FaaS module in Serverless application. The system comprises a Serverless application FaaS module feature extractor and an edge-center deployment decision maker based on a large language model. The feature extractor extracts key features of the FaaS module from the YAML configuration file, wherein the key features include function runtime, memory requirements, timeout setting, trigger types and the like. And the deployment decision maker generates edge or center deployment decisions through a large language model by using the extracted features. The system further comprises a context-aware decision framework which integrates environmental factors such as a current system state and network conditions and adopts a multi-objective decision method to balance technology and service constraints. In addition, the system is equipped with a feedback learning module which records an actual deployment effect and is used for optimizing future decisions. The method has the beneficial effects that the accuracy and flexibility of FaaS deployment are improved, the resource utilization efficiency is optimized, the decision quality can be continuously improved, and an intelligent deployment solution is provided for Serverless application.

The invention provides a method for carrying out model training based on k8s, which relates to the technical field of model training, and comprises the following steps: creating a service queue in a k8s cluster, allocating training tasks to a corresponding queue, identifying training stage characteristics and collecting performance data by a user-defined controller, determining an initial batch allocation value according to the characteristics and the data, and carrying out model training according to the initial batch allocation value. And continuously adjusting the batch allocation value based on the actual calculation time delay, finally updating the resource demand information in the yaml file, and selecting the training node meeting the resource demand to execute the model training task. According to the invention, resource allocation can be optimized, training efficiency is improved, and resource waste is reduced.

The invention relates to the technical field of software engineering management, in particular to a multi-project group Bug statistical management system based on a YAML configuration file and a low code, which comprises a YAML configuration file analysis core module, a low code personnel information acquisition core module, a Bug data visualization core module, an intelligent notification engine core module, a multi-project centralized management core module and the like. Multi-project configuration information is dynamically read through the YAML configuration file analysis module, and the low-code personnel information acquisition module is automatically associated with a Bug responsible person; the multi-dimensional visualization of the Bug data is realized by means of Apache ECharts; the intelligent notification engine performs hierarchical notification based on rules and dynamically adjusts priorities; and the multi-project centralized management module uniformly stores and updates cross-project data. According to the method, intelligent and centralized management of the multi-project Bug is realized, the data processing and display capability is improved, the resource allocation is optimized, the team cooperation efficiency is enhanced, the project risk is effectively reduced, and the overall quality and management level of software development are improved.

The invention provides an automatic testing method and system for AI vision, and is applied to the field of software UI automatic testing. According to the method, mobile terminal UI automatic test requirements are processed based on a visual processing model and a large language model, a JSON format test case is generated, and a YAML format test case for system execution and an XMIND format test case for test personnel to check and edit are generated after conversion and integrity check; the test equipment information is processed based on Appium service and WebDriver, and equipment interface screenshot information is obtained; processing the equipment interface screenshot and the element description information based on a visual processing model to generate element relative coordinates, and generating an operation result through coordinate conversion and operation processing; performing screenshot verification processing on the operation result to generate a test result record; and processing the test result record, the test case in the YAML format and the test case in the XMIND format to generate test report information.

The invention relates to the technical field of data processing, and provides a configurable and visualized data processing system based on a YAML file, and the data processing system comprises a YAML configuration analysis module which is used for analyzing a YAML configuration file; the data processing engine is used for sequentially executing data processing operation according to a configuration rule and generating an intermediate processing result; the visual interface module is used for displaying an intermediate processing result of the data processing engine in real time; the configuration management module is used for storing, loading and exporting the YAML configuration file; the visual interface module, the configuration management module and the data processing engine cooperate to form a dynamic synchronization mechanism. According to the method, the data processing flow is defined through the YAML configuration file, so that the user can intuitively understand the data processing process, the visual interface reflects the data processing effect in real time, and the user is helped to efficiently complete the data processing task.

The embodiment of the invention provides a business system container environment automatic configuration method and device and computer equipment. The method comprises the steps that a building task is created; a standard environment yaml file stored on the container cloud platform is backed up through a gitlab and pulled to a local server; judging whether a to-be-built environment exists on the container cloud platform or not; if yes, modifying the reference environment yaml file according to the to-be-built environment yaml file by utilizing a machine learning model; if not, modifying the yaml file of the reference environment by utilizing a machine learning model according to the associated information of the reference environment and the to-be-built environment; the modified standard environment yaml file is uploaded to an environment project to be set up of the gitlab; and configuring an operation and maintenance tool to deploy the environment to be established by using the modified reference environment yaml file. According to the method, the configuration of the existing container environment can be reused, and modification is carried out by utilizing the machine learning model, so that the labor cost is reduced, and the building efficiency is improved.

The invention discloses a vulnerability scanning method and device based on a template cluster and a storage medium, and the method comprises the steps: responding to a vulnerability scanning request initiated by a user, and determining a scanning target of the vulnerability scanning request; loading at least one YAML classification template adapted to the scanning target to form a template cluster; performing global request merging and path optimization based on the address information of the scanning target and the request specification of each YAML classification template in the template cluster to obtain a detection request set; sending a detection request set to the scanning target, and receiving a response data set returned by the scanning target based on the detection request set; extracting a response matching rule of each YAML classification template in the template cluster, and performing matching analysis on the response data set through the response matching rule; and generating a vulnerability detection report according to a matching analysis result. According to the method, through template rule packaging and global intelligent optimization, scanning request magnitude reduction and efficient resource utilization are realized, and the detection efficiency and accuracy are remarkably improved.

A data stream transformer maps a source data stream model of a source application to a set of one or more destination models of consumer applications. Each destination model is described using a JSON schema. A configuration YAML file having one or more templates is created, with each template being based on the JSON schema and defining methods that the data stream transformer uses to map the source data stream model into the respective destination model. The JSON schema defining the destination model is compiled to create a set of destination model classes. The source application creates source objects forming the source data stream. The data stream transformer uses reflection to retrieve the values of the source objects and uses the template and destination model classes to build destination model objects based on the values of the source model objects. The destination model objects are output to the consumer application.

The invention discloses a finite-state machine coverage rate statistical method of a VCD (Video Compact Disc) file, which is characterized in that a mapping relationship is established between a signal name and state transition information by directly analyzing a YAML (Young Attribute Markup Language) format file of finite states and transition behaviors among the states compiled in a design stage and adopting a hash table mapping technology, so that the constant-level efficiency of reducing time complexity to O (1) is realized. When the VCD file generated by simulation is processed, two strategic file reading methods are adopted, and the file processing flow is simplified. And the uniqueness and accuracy of data processing are ensured through a self-defined Hash algorithm. In simulation data processing, screening is carried out according to the rule of the first character of the data, and only key state transition information is reserved. According to the method, the defects of processing the VCD file and counting the coverage rate of the finite-state machine under the condition of limited resources in the prior art can be effectively overcome, the counting speed of the coverage rate of the finite-state machine is increased, the efficiency of the verification process is improved, and remarkable performance improvement is brought to the verification stage of integrated circuit design.

The invention discloses an infrastructure and application management system based on a cloud native technology, and the system comprises a command line tool which is used for carrying out the communication with a server through an MQTT protocol, and executing the cluster management operation; the multi-tenant private cloud cluster module is used for realizing resource isolation and quota management among tenants; the mirror image warehouse module is used for storing and managing application mirror images; the declarative management module is used for defining a resource expectation state through a YAML configuration file; the CI / CD assembly line module is used for automatically constructing, testing and deploying applications; the gateway resource scheduling module is responsible for load balancing and flow control; the user authorization and authentication module is used for realizing authority control based on an RBAC model; the security module comprises data encryption and network isolation; and the asset library management module is used for managing a program component and a platform library. By integrating a cloud native technology stack, a set of efficient, safe and extensible infrastructure and application management system is constructed, and the deployment efficiency and the operation and maintenance capability of enterprise-level applications are remarkably improved.

The invention provides an automatic test method and device and electronic equipment, and relates to the technical field of automatic test.The method comprises the steps that in response to starting of a test command, at least one YAML use case matched with a tested item is searched; converting the YAML file of the at least one YAML use case into an executable use case script; starting a use case execution stage, and executing the use case script according to the test logic in the use case execution stage; and in response to completion of execution of the case script, generating a test report of the tested item. According to the automatic testing method and device and the electronic equipment, the threshold of automatic script writing is greatly reduced, powerful support can be provided for testers, rapid regression testing of various tested item types can be achieved, meanwhile, the testers can more efficiently cope with a rapid iteration application environment, and the testing efficiency is improved. And the software quality is ensured to be continuously and effectively guaranteed.

The invention discloses a resource packaging and deploying method, a product, equipment and a medium, and relates to the technical field of cloud computing and artificial intelligence, and the method comprises the following steps: converting a structured resource depended by an AI application into a target structured resource in a standardized YAML format, storing a block path of an unstructured resource depended by the AI application, and generating a metadata index; constructing a resource dependency graph according to the metadata information of the target structured resource, the block path information in the metadata index and the resource dependency relationship between the unstructured resource; packaging each block file, the metadata information, the metadata index and the resource dependence graph formed by block compression of the target structured resource and the unstructured resource into a single resource packet; and converting the single resource packet into a target cloud platform instruction through an interface translation engine, so as to perform resource deployment on the current target cloud platform based on the target cloud platform instruction. And unified packaging and automatic cross-cloud deployment of the full-stack heterogeneous resources are realized.

The application provides a method for optimizing a target detection network, comprising the following steps: S1, modifying a network file yolov7-tiny.yaml; S2, training a model; and S3, comparing the precisions before and after modification and showing the effect. Through the method, the calculation amount can be greatly reduced while the precision is maintained.

The invention discloses a resource configuration synchronization method, a cloud edge cooperation system, a medium and a product. The resource configuration synchronization method applied to a center side comprises the following steps: acquiring a to-be-synchronized resource configuration item and a configuration item parameter value corresponding to the resource configuration item; calling a configuration change interface of an edge cluster according to the resource configuration item and the configuration item parameter value, so that the edge cluster updates an original configuration item parameter value corresponding to the resource configuration item in an edge configuration table according to the configuration item parameter value; and calling a configuration redeployment interface of the edge cluster to enable the edge cluster to generate a yaml file according to the yaml template and the updated edge configuration table, and performing application deployment on the yaml file, thereby solving the problem that the synchronous update of the cloud edge collaboration system needs manual maintenance, and improving the operation and maintenance efficiency and stability of the cloud edge collaboration system.

This application provides a method and apparatus for generating random instruction test cases, relating to the field of automated testing technology. First, target instructions or instruction sequences are encapsulated into C inline functions using inline assembly or macro definition, forming a unified function library. Next, a function parameter constraint system is constructed, defining parameter types, address ranges, instruction combination methods, and value range constraints for each encapsulated C inline function. Then, a multi-level nested structure is built based on the C inline functions to organize the instructions, where each level node is associated with a corresponding C inline function name and constraints. Finally, based on a YAML configuration file, the target C inline functions are determined from the multi-level nested structure and combined to generate random instruction test cases. The YAML configuration file includes at least the number of iterations, a random seed, a random strength factor, and test content configuration. The solution provided in this application has the advantages of improved verification efficiency and applicability to different scenarios.

The invention provides an AI-agent-based electric power engineering project knowledge base construction method and an intelligent supervision system. The construction of a knowledge base comprises the following steps: step 1, downloading a source code of a knowledge enhancement generation framework Known Augmented Generation (KAG) to the local; step 2, copying an exampleconfig.yaml template file in the source code, and modifying a configuration parameter according to a service requirement of an electric power engineering project, wherein the exampleconfig.yaml template file is a copy of the exampleconfig.yaml template file in the source code; 3, a knext tool in the KAG framework is utilized to create and initialize a project, and the project is modified; step 4, constructing a knowledge index in a build folder in the project which is initialized and modified in the step 3; and Step 5, carrying out retrieval by utilizing the retrieval script in the solver folder of the project. According to the method, all the business reference data form a knowledge base, and the user can obtain a reliable supervision process by inputting his / her own demand, so that the efficiency can be greatly improved. The knowledge base is constructed on the basis of a KAG framework, the steps of knowledge extraction, vector generation, graph database storage and the like are carried out under the assistance of a multi-modal large model, and professional supervision service reference data are provided for enhancing a search agent.

The invention discloses a software test report generation method and device, a storage medium and computer equipment, and the method comprises the steps: responding to a test report generation instruction of target software for a target scene, and obtaining test index data of different data sources through a unified multi-data source adaptation layer interface, the different data sources comprise a Prometheus time sequence data source, a Grafana visual data source and a YAML test case data source; obtaining a test report generation template corresponding to the target scene, and determining an index association rule and a rendering type in the test report generation template; performing association analysis on the test index data of the different data sources on the basis of an index association rule, determining test conclusion data of the target software on the basis of an association analysis result, and rendering the test index data in the Grafana visual data source on the basis of a rendering type to obtain a rendered picture; and embedding the test index data, the test conclusion data and the rendering picture into a test report generation template to obtain a test report corresponding to the target software.

The invention relates to a file editing method and device, electronic equipment and a storage medium. The method comprises the steps that a target file type capable of being edited by a file editing assembly of a file system in an embedded system is obtained; performing file type conversion on the yaml file according to the target file type to obtain a target folder; the file type corresponding to the target folder is a target file type; and editing the file content in the target folder based on the file editing component to obtain an editing result. According to the method and the device, the file content of the original yaml file can be edited by utilizing the file editing component under the embedded system, and the editing requirements of adding, deleting and modifying the content of the yaml file under the embedded system are met.

The application discloses an implementation method, system and device of a serverless computing platform and a storage medium, belongs to the technical field of Internet, and solves the technical problem that the existing serverless computing solution cannot meet the needs. The method is: initializing various components of the serverless computing platform, including a front-end service, a back-end service, an online coding integrated development environment, a construction tool, a container scheduling component, a gateway service, a code repository and a container image repository that need to be called; creating a new serverless computing function on the interface of the front-end service and deploying the serverless computing function into a Kubernetes cluster to meet different development needs. The serverless computing platform built by the Kubernetes cluster can significantly improve resource utilization, optimize the scheduling and resource configuration of the serverless computing function, and conveniently apply the perfect monitoring function of the Kubernetes platform. When starting the serverless computing function, the user does not need to write Kubernetes YAML, thereby reducing the learning cost and difficulty.

The present invention relates to a method for database proxy protection and dynamic configuration. The method includes: obtaining data sources, user configuration information, and rule configuration information by periodically calling an external interface, and dynamically creating database proxy services with different proxy ports; the proxy service periodically obtains relevant data with its own proxy port as a key from the process that created the proxy service, and periodically obtains the current data source, user configuration information, and rule configuration information; after the proxy service obtains the information, it compares it with the last cached information, modifies the data source generation library, and sets the access users of the proxy database according to changes in user data; for users who need to be configured through YAML, the yamlConfig in the context reading part of the source code is modified, so that the information in the Java program memory variables actually read is changed, thereby realizing dynamic modification of the user. Through this solution, a secure database proxy can be realized, and users accessing the proxy database can be dynamically controlled.

The invention discloses a high-performance visual interface configuration service design method based on a yaml format, a visual large-screen interface is provided through database configuration without code writing, and the method comprises the following steps: interface configuration rules: one configuration represents one interface; the data acquisition engine acquires original data according to configuration; the data processing engine processes the original data according to the configuration to generate target chart data; and performing visual display, wherein the web page end displays a corresponding chart according to an interface response result. According to the method, data acquisition and data processing are decoupled, and the data processing flow is completed by the back-end service, so that the network data transmission quantity is greatly reduced, the data processing tasks of the web front-end equipment are reduced, and the rendering speed of the visualized large-screen chart is improved.

The present invention discloses a robotic arm task execution method based on low-code configuration, including defining a status module, which includes the position, pose, kinematic attributes, path constraints, peripheral operations, and exception handling of the robotic arm; creating a low-code task configuration file in YAML or JSON format to represent the hierarchical relationship between different levels of entries; constructing a task tree structure including task arrays or lists, subtasks, status modules and their attributes; defining data structures and node types to convert the text-form task configuration into a data structure that can be processed by a program; parsing the task tree structure configuration in YAML format or JSON format and reading it into a data structure that meets the tree topology structure; executing the task tree data structure, traversing each node, and cooperating with the robotic arm drive to implement task execution operations according to the status module and attributes defined by each node. The present invention simplifies the programming process of robotic arm tasks and improves the flexibility and reliability of task execution.

An embodiment of the present invention provides a deployment method, device, equipment and medium for a network controller with a service-free architecture. The method includes: S101, building a highly available k3s cluster and initializing the Dapr environment; S102, installing the helm binary program; S103, deploying a boundary API gateway as a unified client entry according to the helm binary program; S104, deploying a consul component as a registration center and service discovery center for the network controller according to the helm binary program; S105, deploying a cluster version of the redis component as a state management center and message queue center for Dapr according to the helm binary program; S106, writing deployment files for each microservice module of the network controller in yaml format; S107, completing the deployment of all microservice modules of the network controller according to the deployment files and performing joint debugging on all microservice modules. The present invention effectively improves the reliability and scalability of the network controller in the network data center, thereby significantly improving productivity and reducing operation and maintenance costs.

A diagnostics collection script is included in a container image. When an original instance of the container fails, a new instance of the container is started using the container image using a Docker compose YAML file having the diagnostics collection script as an entrypoint. The diagnostics collection script is configured to collect the logs of the original (now failed) container during the container start operation. In this manner, the container is able to be started in a safe mode and is isolated to only run the diagnostics collection script without disturbing the container's original settings. By recreating the exact environment of the failed container, and running diagnostics on the environment, it is possible to obtain the log files from the original container as well as to implement diagnostic operations on the environment of the original container, which can then be used to help diagnose why the original container failed.

The application discloses a self-adaptive control method and device for cloud native cluster intelligent operation and maintenance, and relates to the technical field of cloud native application.The operation and maintenance demand of a K8s cluster is described through a YAML format file, and the operation and maintenance demand is acquired by analyzing the YAML format file; the planning control logic and the index analyzer are generated according to a rule trigger condition and an operation and maintenance operation supported by the cluster; the index collector type is determined according to an index type and an index parameter, and the corresponding index collector is generated according to a template; the index monitor to be created is determined according to the analyzed operation and maintenance demand, the index collector and the index analyzer are combined to obtain the index monitor, the index is automatically collected through the index collector, and the operation and maintenance planning logic corresponding to the operation and maintenance demand is determined through the index analyzer; the index monitor and the planning control logic are integrated, the code of the K8s controller is generated and deployed into the K8s cluster, and the self-adaptive control for the cloud native cluster intelligent operation and maintenance is realized.

The invention discloses an analysis method and device for centralized bidding result data, equipment and a storage medium. The method comprises the following steps: acquiring a centralized bidding result data set of an electricity market; segmenting the centralized bidding result data set into a plurality of data subsets, and constructing a binary tree to be analyzed by using data elements in the data subsets; the to-be-analyzed binary tree is analyzed according to a preset YAML rule file through a binary tree YAML analyzer, so that a first data analysis result is obtained, the binary tree YAML analyzer comprises a plurality of YAML sub-analyzers, and the YAML sub-analyzers are of a binary tree structure. According to the technical scheme, the YAML rule file of the pre-configured binary tree structure is used for analyzing the multiple data subsets of concentrated bidding of the electricity market, the analysis speed and efficiency of the electricity transaction data are optimized, the electricity transaction data with the incidence relation are accurately interpreted, and the analysis efficiency of the electricity transaction data is improved. The problems of low power data analysis efficiency and poor accuracy are solved.

This invention introduces a versatile system and methodology for configurable video inferencing analysis, utilizing a domain-specific language (DSL) to define detailed specifications for video analysis tasks across various domains, including but not limited to security surveillance, traffic monitoring, industrial automation, retail behavior analysis, and environmental observation. The modular architecture of the system encompasses an orchestrator module, multiple inferencing modules (watchers), a data management module, and a publication module, each tailored to execute configurations delineated in the DSL. This DSL supports a wide array of formats such as YAML, JSON, and XML, catering to diverse user preferences and integration requirements.