64 results about "Image repository" patented technology

The invention discloses a method and a system for simplifying large model reasoning service deployment in a heterogeneous cluster environment, mainly relates to the technical field of service deployment, and is used for solving the problems of repeatability cost of heterogeneous adaptation, dependence on manual allocation of containers to specific nodes during containerization deployment and version library compatibility in an existing scheme. And crash is caused, and dynamic resource allocation based on load characteristics is lacked. Comprising the steps of performing preset mirror image condition matching on a node hardware feature tag and a model service demand tag of an implementation node and a pre-built reasoning service container mirror image in a mirror image warehouse; when the pre-constructed reasoning service container mirror image with the matching degree of 100% does not exist, dynamically constructing a customized reasoning service mirror image, and storing the customized mirror image in a mirror image warehouse as the pre-constructed reasoning service container mirror image for implementing node matching; and pulling a pre-constructed reasoning service container mirror image for implementing node matching from the mirror image warehouse, mounting a standardized model file mirror image, and starting a reasoning service container.

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: performing natural language processing to process a text string of a user, wherein the text string specifies characteristics of a container image to be built; processing, with use of natural language processing, instances of text-based data that describe respective ones of a plurality of container images stored within a container image repository; selecting, in dependence on a result of the performing natural language processing, and the processing, a base image from the plurality of container images; and presenting prompting data to the user that prompts building of a new container image, wherein the prompting data references the base image.

Systems and methods for retrieving property using machine learning based image matching. In some embodiments, images from a user request can be provided to a matching model configured to retrieve similar images and corresponding property listings. In some embodiments, this allows a user to find additional similar listings based on photo gallery images of existing property listings. In some embodiments, this allows a user to upload a new image to search for existing properties. By accessing a machine learning model, such as a matching model, the property listing system can retrieve a number of matching images associated with property listings to be scored, ranked, and presented to a user.

The application discloses an automatic test method and device based on picture comparison, equipment and a storage medium. The method comprises the following steps: importing original picture data into a picture library, defining a key field replacement area and a random replacement field of the original picture data; replacing the key field in the key field replacement area with the random replacement field to generate replaced picture data; calling an OCR test interface to test an OCR picture recognition system by using the replaced picture data, and obtaining actual return data of the OCR test interface, wherein the actual return data comprises corresponding fields of each key field in the replaced picture data and values corresponding to each key field; matching and comparing the replaced picture data with the actual return data to obtain a field matching success rate and a field recognition accuracy of the replaced picture data, and obtaining a test result of the OCR picture recognition system. The embodiment of the application greatly improves the test efficiency of the OCR picture recognition system.

The application relates to the technical field of multi-angle camera video picture synchronization, and discloses a multi-angle camera video picture synchronization method and a computer readable storage medium, the method is as follows: acquiring a picture library, pictures in the picture library have time stamps; traversing the picture library, establishing an original coordinate list according to the maximum time stamp and the minimum time stamp; mapping the pictures in the picture library to the original coordinate list to generate a reference coordinate list and a plurality of synchronization coordinate lists; synchronously comparing the original coordinate list and the synchronization coordinate list, extracting all the pictures successfully synchronized from the picture library according to a synchronous comparison result, and storing the pictures in a result file; and uploading and storing the result file to a synchronization library. The application is used to solve the problem that in the prior art, only time stamps are used to synchronize the video frame pictures of multiple angles, due to the excessively large picture data, when synchronization is performed, each reference picture needs to traverse all the pictures once, and the time cost of traversing all the pictures multiple times is large.

This invention relates to the field of containerized deployment technology, specifically providing a Docker-based image management and update method and system. It packages middleware involved in the application system into an image and builds an automated mechanism. When critical files within the application system change, it automatically initiates the image building and compilation process according to the Dockerfile, generating the latest Docker image and pushing it to a designated image repository. Specifically, it includes: a file monitoring module responsible for real-time monitoring of changes to critical files within the application system; an image building triggering module receiving file change events sent by the file monitoring module and determining whether to trigger the image building process based on preset rules; an image building and compilation module building the Docker image locally according to the execution instructions in the Dockerfile; and an image push module pushing the built and compiled Docker image to the designated image repository. Compared with existing technologies, this invention enables efficient deployment and automated updates of application systems.

This application relates to the field of computer software technology, providing an image management method, system, and computing device. The method is applied to a computing device equipped with a software integration system, including: responding to a code repository corresponding to the software integration system receiving a commit instruction, detecting the change status of the image configuration file; the change status is used to identify whether any instruction text in the image configuration file has changed; if the change status is "changed," loading the changed image configuration file; generating an image build instruction based on each instruction text in the image configuration file; generating an image file according to the image build instruction; and storing the image file in the image repository of the software integration system. Based on this solution, the computing device utilizes changes to the image configuration file to trigger the automatic generation and updating of image files, decoupling the image file generation process from the software package delivery request, avoiding repeated image building for each package delivery, and improving software build efficiency.

The application relates to the technical field of furniture sales, in particular to a store sofa sampling detection method and system and a storage medium, which comprises the following steps: generating a corresponding product list according to a sales plan; obtaining plan sofa information in the product list, and obtaining a corresponding plan sofa model from a product database according to the plan sofa information; updating and adjusting the product database according to a convolutional neural network training method; obtaining an exhibited sofa image in a store, and storing the exhibited sofa image in a product picture library; identifying and comparing the plan sofa model obtained from the product database and the exhibited sofa image in the product picture library, and obtaining a corresponding comparison result; and judging whether the store sampling is qualified according to the comparison result. The application has the effect of improving the timely detection management of the enterprise end on the store sampling of sofas according to the sales plan.

The present application provides a data processing method and a computer device, which can be applied to the field of data processing. The method comprises: accurately tracking I / O requests in a container task starting process, constructing a mapping relationship between each I / O request and data (i.e., target data) necessary for container starting, and sending valid I / O requests (i.e., m I / O requests) generated in the starting process and the mapping relationship to an image repository. Each time a same container task is started, valid I / O requests generated are the same. Therefore, when the container task is started again, the number of I / O requests sent to the image repository in the container task starting process can be reduced (because m I / O requests are known, the m I / O requests can be sent to the image repository at a time after being aggregated). In addition, when the container task is started, corresponding m pieces of target data can be pulled from the image repository at a time on the basis of a pre-constructed mapping relationship, without the need for multiple pulls, thereby improving the efficiency of container task starting and reducing the data pull volume.

Techniques for migrating a workload between two container clusters (i.e., source and destination container clusters) that use different processor architectures are provided. In one set of embodiments, these techniques involve implementing a migration container cluster that (1) creates a backup of the workload from the source container cluster, where the backup includes metadata regarding one or more objects or resources of the workload, and (2) restores the backup on the destination container cluster, where the restoring causes a worker node of the destination container cluster to automatically retrieve, from an image repository, a container image for the workload that is specific to the second processor architecture and deploy the container image as a running container on the worker node.

The invention discloses a cloud mobile phone testing system and method based on an ARM native architecture and intelligent environment arrangement, and relates to the technical field of automatic testing, and the method comprises the steps: obtaining an operation node based on a dynamic scheduling plan, matching an Android container mirror image from an Android container mirror image warehouse according to a multi-dimensional parameter in a Kubernetes framework, pulling the Android container mirror image to the operation node, and carrying out the cloud mobile phone testing according to the operation node. The method comprises the following steps: acquiring an Android container instance by instantiating an Android container mirror image, mounting an application installation package, an automatic test script and a data acquisition agent, constructing a cloud mobile phone test environment, running the automatic test script in the cloud mobile phone test environment, controlling a cloud mobile phone to simulate a real user behavior, and acquiring multi-modal test data and storing the multi-modal test data as an original test data set; the automation degree, the efficiency and the resource utilization rate of cloud mobile phone testing are improved, and the requirements under different hardware and software environments are met.

The application discloses an automatic mechanical hand based on a Transformer model and a Mediapipe library and a control method thereof. The mechanical hand comprises a mechanical hand wrist, one end of the mechanical hand wrist is connected with a rotating seat, a mechanical hand palm is arranged on the rotating seat, five humanoid hand mechanical fingers are arranged on the mechanical hand palm, a camera is arranged on the rotating seat, and a power supply, a computer and a microprocessor are arranged on the mechanical hand palm. The target object is scanned through the camera, the pictures in the picture library in the computer are matched through the Transformer model, the corresponding optimal action combination is called through the function calling corresponding to the matched pictures, and the action combination is sent to the microprocessor. The microprocessor controls the finger module to grasp the object according to the received action combination, and the grasping process is monitored in real time through the Mediapipe, so that the target object is more stably grasped. The application has reasonable design, fast and accurate finger execution speed and low manufacturing cost.

The invention relates to the technical field of artificial intelligence image recognition and data mining, and discloses an image data mining processing system and method combining a visual large model and a visual small model. The method aims at solving the problems that in the prior art, the image recognition recall rate and accuracy are difficult to consider, the calculation cost and the processing speed are unbalanced, the data management efficiency is low, and the model generalization ability is insufficient. The system comprises a data input module, a frame extraction deduplication module, a picture library construction module, a visual large model judgment module, a visual small model judgment module, a label preprocessing module, a manual inspection module, a training data generation module, a model training module and a data security module. According to the method, the generalization ability is guaranteed through preliminary screening of an LPM (Deic) large model, missing detection is complemented through secondary verification in cooperation with a YOLO small model, false alarms are eliminated in combination with manual verification, meanwhile, double models are optimized through targeted sample training, the pain point of missing detection or misjudgment of a single model is effectively solved, and accurate and comprehensive consideration of target recognition is achieved.

The invention relates to the technical field of intelligent household appliances, in particular to a dishwasher cleanliness detection method adopting T-Faster R-CNN, comprising the following steps: constructing a tableware picture library; de-noising the images of the tableware picture library by using a nonlinear bilateral Wiener filtering method; dividing the denoised tableware picture library into a tableware training set and a tableware testing set; marking the images in the tableware training set; constructing a T-Faster R-CNN (Convolutional Neural Network) model, and performing feature extraction by adopting a T-SR network; the tableware training set is sent to a T-Faster R-CNN model for training, and a trained T-Faster R-CNN model is obtained; a G-Focal cleaning loss function is calculated, and a final T-Faster R-CNN (Convolutional Neural Network) model is obtained; and testing the final T-Faster R-CNN by using the tableware test set, and outputting a detection result to obtain the cleanliness of tableware cleaned by the dish washing machine. By fully utilizing the feature representation capability of the neural network, the dirt in the dish-washing machine can be accurately detected, the dependence on human intervention is reduced, and the robustness and adaptability of the algorithm are improved.

The invention relates to the technical field of picture rendering, and discloses a Flutter cross-platform picture rendering method, device and system based on FFI and external texture fusion, and the method comprises the steps: obtaining a request parameter of a to-be-rendered picture through a preset Flutter user interaction layer; determining a rendering path matched with the to-be-rendered picture according to the request parameter; sending a rendering data request to a native layer corresponding to the to-be-rendered picture through a preset cross-platform communication layer to enable a Flutter user interaction layer to obtain the to-be-rendered picture, and returning image rendering data of the to-be-rendered picture to the Flutter user interaction layer through the native layer and the rendering path; and rendering the to-be-rendered picture through the Flutter user interaction layer according to the image rendering data to obtain a rendered picture. It can be seen that by implementing the method, the native network picture library can be integrated, and the picture rendering and loading speed can be effectively increased in the weak network environment; and meanwhile, a multi-level caching strategy of the original picture library is utilized, so that the flow is effectively saved, and the server pressure is reduced.

A method executed by a host computer system with a processor system involves initiating a guest context that depends on a filesystem image stored in a remote image repository. A reflector disk is generated for the filesystem image, representing data blocks without storing them. Upon receiving a read request at the reflector disk specifying an offset and length within the filesystem image, a set of data blocks is retrieved from the remote repository corresponding to the specified range. The reflector disk then provides the retrieved data blocks to the requester, enabling efficient access to filesystem data without storing the actual data blocks locally.

Disclosed systems and methods may retrieve a base image of a plugin for a hyperconverged infrastructure (HCI) platform and create a Docker image for an HCI platform plugin based, in part, on the base image. The HCI platform plugin enables an extended function not native to the HCI platform. Disclosed methods may include registering the Docker image within an HCI platform instance running on an HCI appliance associated with a customer, and saving the Docker image to a Docker image repository within the HCI platform instance. The extended HCI platform function may be any suitable function including, as an illustrative example, a function for provisioning a third party storage device within the HCI platform. Disclosed method steps may be performed by an independent third party developer that lacks authorized access to an HCI platform instance.

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: performing natural language processing to process a text string of a user, wherein the text string specifies characteristics of a container image to be built; processing, with use of natural language processing, instances of text-based data that describe respective ones of a plurality of container images stored within a container image repository; selecting, in dependence on a result of the performing natural language processing, and the processing, a base image from the plurality of container images; and presenting prompting data to the user that prompts building of a new container image, wherein the prompting data references the base image.

The application is suitable for the field of computers and provides a system for automatically generating pictures from text, which comprises a text information import module arranged in the system and used for importing description text information into a picture calling model; a picture calling module used for the picture calling model to call pictures consistent with the description text information from a picture library to form a calling picture set; a parameter acquisition module used for acquiring a random value, an iteration number and an update step distance; a basic picture setting module used for selecting a picture from the calling picture set as a basic picture according to the random value; a basic picture iteration module used for importing the basic picture into a picture generation model and regenerating the picture according to the iteration number and the update step distance; and a target picture export module used for exporting the generated target picture from the picture generation model when the iteration number is completed. Different target pictures can be directly generated and exported according to the input description text, and the workload of manual drawing is reduced.

The application relates to a multi-dimensional dynamic album recommendation method and system based on a large model and belongs to the technical field of intelligent storage management. The application comprises the following steps: constructing an album recommendation model based on an AI large model, wherein the album recommendation model comprises a user interest modeling module, a similarity calculation module, an incremental updating module and a dynamic optimization module connected in sequence; inputting a user interest feature vector and a picture feature vector into the album recommendation model; generating a user interest model according to the user interest feature vector through the user interest modeling module; calculating the similarity between the picture feature vector and the user interest model through the similarity calculation module; collecting interactive behavior data of a user on an initial recommended album list; pushing the interactive behavior data to the AI large model in real time through the incremental updating module; and reordering candidate albums in a preset picture library through the dynamic optimization module and the user interest model updated incrementally to generate a recommended album list after dynamic optimization.

1. Name of the product in this design: Window body of a dynamic graphical user interface for window switching in electronic devices. 2. Purpose of this design: An electronic device. 3. The key design features of this product are the graphical user interface portion for which protection is sought. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: The overall interface involves a dynamic graphical user interface for switching between the settings module window and the picture gallery window for displaying photos. For example, individual windows can be presented in a three-dimensional environment within the user's field of view as shown in the interface. The protected portion is the window body, including the settings module window body and the picture gallery window body. The user can initiate the above-mentioned transition process by clicking the picture gallery icon in the lower right corner of the main view, so that the interface changes from the main view to the interface transition state diagram 1-4 in sequence, in order to switch to the picture gallery window. In addition, users can also initiate the above conversion process through other interactive methods such as gestures, eye movements, head movements, or voice commands. 6. Other situations requiring explanation: The parts depicted by dashed lines in the view are not the parts that this design patent requires to protect.

The application aims to provide a container packaging method and device into an image. When a user visits a platform corresponding to a kubernetes cluster, a target container to be packaged is determined through a container visualization list interface provided by the platform, and parameter information of the target container is obtained by accessing a kube-apiserver gateway. The gateway creates a container image packaging object of the target container according to the parameter information. When a container controller on a target kubernetes node listens to the container image packaging object as a self-node task of the node, the container controller on the node generates a corresponding new image after performing image packaging on the target container and pushes the new image to a target image repository, so that image packaging can be realized during container running, and the new image can be pushed to a target image repository specified by a user.

The invention relates to a fault injection method and device for a mixed architecture container cloud cluster. Comprising the steps that a fault injection agent component is managed and initialized to a target cluster; fault injection information input by a user is received, and a fault injection request is submitted to the fault injection agent component based on the fault injection information; after the fault injection proxy component receives the fault injection request, a target mirror image is pulled from a pre-constructed mirror image warehouse according to the fault injection request, and a fault injection container is created at a target node; and the fault injection container implements fault injection to obtain a fault injection result. Through the method, fault injection of the mixed architecture container cloud cluster is realized, various architectures can be compatible, the rated types of fault injection can be expanded by designing a new mirror image, the mirror image can be compiled into a new architecture to adapt to the corresponding architecture, and the fault types, the difficulty of architecture type expansion and the maintenance complexity of fault injection are reduced.

This invention provides a container image version upgrade system and method. The system includes a front-end module, a storage module, a task distribution module, an image repository module, a database module, and a task execution module. The front-end module displays the current and latest image version information of the application's containers and receives upgrade commands input by the user for one or more containers. The task distribution module generates multiple upgrade tasks based on the upgrade commands and saves them to a task queue in the database module. The task execution module sequentially retrieves and executes the upgrade tasks from the task queue. In this solution, by displaying the current and latest image version information of each application's containers to the user, and when the user selects multiple containers for batch upgrades, multiple upgrade tasks are generated accordingly. The task execution module sequentially retrieves and executes the upgrade tasks, achieving the goal of batch upgrading container image versions and improving upgrade efficiency.

The invention discloses a containerized application and assembly line deployment method based on an AI agent, and relates to the technical field of artificial intelligence, and the method comprises the steps: analyzing a project deployment demand through the AI agent to obtain deployment parameters, and generating a continuous integration script and application deployment configuration; calling an interface of a continuous integration tool and a code warehouse, and creating an automatic construction task; in response to the code submission event, automatically triggering an automatic construction task, completing mirror image construction and uploading the mirror image to a preset mirror image warehouse; and then, the container arrangement system pulls the target mirror image from the preset mirror image warehouse, and realizes automatic deployment of the application instance in the target cluster based on the application deployment configuration. According to the method, the AI intelligent agent is introduced, so that full-process automation from project deployment demand analysis to containerized application deployment is realized, and the manual intervention cost and the configuration error risk in the traditional deployment process are reduced.

A method is described that includes executing a container image that is configured as an image repository mirror for a remote server and executing an application that is configured to provide a request for one or more images to the remote server. The method further includes reconfiguring the application to cause the application to provide the request for the one or more images to the container image instead of the remote server.

The application relates to the technical field of image processing, and discloses a picture screening method and device, aiming to solve the problems of low efficiency, low accuracy and poor picture quality of the existing picture screening mode, and mainly comprising the following steps: determining picture similarities between pictures in a picture library, dividing the pictures into multiple similar picture groups according to the picture similarities; determining picture qualities of the pictures, determining representative pictures corresponding to each similar picture group according to the picture qualities; performing face recognition on the representative pictures, screening face pictures containing preset faces from the representative pictures according to the face recognition results; performing motion blur detection on the face pictures, and removing the face pictures with a motion blur confidence score greater than a second threshold; and performing expression detection on the remaining face pictures, and screening face pictures with expressions meeting preset conditions from the remaining face pictures. The application improves the efficiency, accuracy and quality of picture screening, and is suitable for batch picture screening.

The utility model discloses an appearance image acquisition device for growing crystals. The appearance image acquisition device comprises an acquisition control box, a supporting platform, a rotary driving mechanism, a centering measuring mechanism, a centering adjusting mechanism, a rolling adjusting mechanism, an acquisition bracket and a high-definition camera. According to the appearance image acquisition device, position measurement and centering adjustment of a crystal can be realized by using the centering measurement mechanism and the centering adjustment mechanism, so that the crystal acquired by the high-definition camera is located in the middle of an image; the rotary driving mechanism can be used for realizing rotary driving of the acquisition bracket, and switching of different shooting angles of the high-definition camera is met; the crystal can be adjusted in a rolling mode through the rolling adjusting mechanism, so that image collection of the crystal in different angle intervals is achieved, rapid and automatic collection and storage of images of the whole body of the crystal are finally achieved, and therefore detection personnel can conveniently conduct magnification checking on a computer display screen and establish a product traceability picture library in the later period.

Examples described herein provide a computer-implemented method that includes downloading a container image from an image repository. The method further includes deploying the container image as a container at a local graph. The method further includes identifying an image layer of the container image of the container as having a patch in error. The method further includes marking, at the image repository, the image layer as having the patch in error by modifying attributes of a manifest configuration for the image layer having the patch in error. The method further includes rolling back the image layer of the container having the patch in error to a parent image layer based on the attributes of the manifest configuration for the image layer having the patch in error.

This application discloses an automated application deployment method based on a hybrid architecture, aiming to solve the problems of high cost of fully cloud-hosted services, unsuitability of lightweight event-driven architecture for the full lifecycle management of complex applications, and low automation and chaotic version management in traditional hybrid architectures. The method is executed by a continuous integration and deployment orchestrator deployed in a local environment, and mainly includes: receiving build instructions, completing application building based on project source code and generating a container image; pushing the image to a designated cloud container image repository; automatically initiating a deployment instruction to the associated cloud container engine service in response to the image push completion event via a trigger preset in the image repository; and causing the cloud container engine service to respond to the instruction, pull the updated image, and complete the service update operation in the container cluster.