165 results about "Image repository" patented technology

The invention belongs to the technical field of resource management and scheduling, and particularly relates to an intelligent AI platform resource management and scheduling system based on K8s. Comprising a dynamic resource prediction module, a K8s cluster management and resource scheduling module, a resource monitoring and analysis module, a resource isolation and sharing module, a computing power resource allocation module, a security enhanced mirror image warehouse and a reasoning early warning system. Through the synergistic effect of the modules, the technical problems of inflexible resource scheduling, insufficient resource monitoring, incomplete user resource isolation, limited computing power distribution, complex mirror image management and incomplete operation and maintenance management of an AI platform in the prior art are solved.

The invention discloses a data-driven learning model combination optimization method based on edge cloud collaboration, and the method comprises the following steps: constructing a collaborative computing framework based on cloud nodes, edge nodes and equipment nodes, and distributing distributed tasks and computing resources; constructing a plurality of micro-service interfaces including a platform service interface, an evaluation service interface and a model training service interface of edge nodes, and a model upgrading service interface and a candidate solution evaluation service interface of cloud nodes; a multi-round model combination strategy and a model online enhancement combination training strategy based on integrated learning are constructed, a plurality of micro-service interfaces are packaged into Docker mirror images to be stored in a cloud mirror image warehouse for version management and distribution, and a communication mechanism between cloud nodes and edge nodes is constructed. According to the method, through cooperative work of the cloud and edge computing, the solving efficiency of a complex distributed optimization problem is remarkably improved, and the resource utilization efficiency and the data processing intelligence are improved.

Apparatus for visualization within a three-dimensional (3D) model and methods used therein are described, wherein the apparatus includes a processor and a memory communicatively connected to the processor, wherein the memory includes instructions configuring the processor to receive a query image, extract neural network encodings from the received query image, query a synthetic image repository for at least a matching synthetic image, and display an estimated position and orientation within the 3D model, wherein the synthetic image repository includes a plurality of synthetic images and their extracted neural network encodings, each synthetic image therein corresponds to a slice extracted at a specific position and orientation in the 3D model, and querying the synthetic image repository includes comparing the extracted neural network encodings between the query image and synthetic images.

The invention relates to a device and a method for assisting in detecting depression degree by utilizing emotional picture cognitive deviation, and relates to the technical field of biomedical engineering and medical instruments, and the device comprises an emotional picture library module which is used for storing a pre-selected emotional picture set; the display interaction module is used for displaying pictures to the user and recording classification selection and reaction time of the user; the data acquisition module is used for acquiring user facial micro-expressions and synchronously storing the facial micro-expressions and classification results; the data processing module is used for calculating positive picture error rate related features and normalizing behavior features and micro expression features into feature vectors; the depression index calculation module is used for outputting depression indexes; and the result output module is used for generating a visual report. The depression index is calculated based on behavior data, deviation is avoided, multi-modal features are fused to improve quantization precision, low-cost non-intrusive screening is achieved through a touch screen and a camera, and an embedded processor is integrated to improve the integration level and generalizability.

The invention discloses an application program deployment method and device and a storage medium, and the method comprises the steps: automatically generating a mirror image file of an application program through a continuous integration tool, and automatically pushing the mirror image file to a container mirror image warehouse; and automatically pulling the latest mirror image file from the container mirror image warehouse to a plurality of target servers through a configuration file or a container scheduling tool, and starting container instances on the plurality of target servers in batches, thereby deploying the application programs on the plurality of target servers in a large scale. According to the method, automatic compiling, mirror image generation and containerization deployment of project codes are realized, so that the problem of low automation level in application program deployment in the prior art is effectively solved, and reliable guarantee is provided for stability and high efficiency of a production environment. The method can be widely applied to the technical field of computer operation and maintenance.

An information handling system may include a processor and a container orchestrator comprising a program of instructions configured to, when read and executed by the processor, in a distributed ecosystem comprising a plurality of host systems, in connection with a build of a new container image to be stored in a container image repository associated with the distributed ecosystem: identify, based on existing container images stored in the container image repository, existing dependency layers of the existing container images for which the new container image has dependencies; identify application-specific layers of the new container image unique to the new container image; and build and store the new container image in the container image repository, the image including the application-specific layers and the dependency layers.

Described herein are systems, methods, and instrumentalities associated with medical image classification and / or sorting. An apparatus may obtain a medical image from a medical image repository and further obtain multiple textual descriptions associated with a set of image classification labels. The apparatus may pair the medical image with one or more of the multiple text descriptions to obtain one or more corresponding image-text pairs, and classify the medical image based on a machine learning (ML) model and the one or more image-text pairs. The ML model may be configured to predict respective similarities between the medical image and the corresponding text descriptions in the one or more image-text pairs, and the apparatus may determine the class of the medical image by comparing the similarities predicted by the ML model. The apparatus may then further process the medical image based on the class of the medical image.

An image processing method and information processing apparatus is provided that executes an image processing method. The image processing method includes acquire an image from an image capture device, the image being captured live in real-time, acquire orientation information associated with a subject in the acquired image, using the acquired orientation information to obtain, from an image repository, a previously captured image of the subject in a similar orientation, generating a composite image by inpainting one or more landmarks from the obtained precapture image that are not present in the acquired live image based on a predetermined geometric representing the subject; and displaying, on a display device, the generated composite image.

The invention relates to the technical field of platform migration, in particular to an application component migration method applied to PaaS cross-platform migration, which comprises the following steps: acquiring bottom hardware resource architecture information of a target PaaS platform; uploading the container mirror image to a mirror image warehouse of the target PaaS platform to complete mirror image migration; performing adaptive conversion on the component characteristic parameters based on a migration rule base; respectively migrating an application component and a configuration file of the source PaaS platform to the target PaaS platform according to the migration priority; sequentially migrating file data and object data on which an application component depends, the database middleware and database data to the target PaaS platform; and performing service configuration adjustment and service verification, and switching the service flow from the source PaaS platform to the target PaaS platform. According to the invention, the migration accuracy is improved.

The invention discloses a mirror image file obtaining method and device and computing equipment, and relates to the technical field of virtual machines. The mirror image file acquisition method is applied to the computing device, a target configuration file is stored on the computing device, the target configuration file comprises address information of a private mirror image warehouse, and the private mirror image warehouse is deployed on the computing device or interconnected with the computing device through a local area network. The method comprises the steps of obtaining a first calling request, wherein the first calling request comprises an identifier of a target mirror image file; obtaining address information of a first mirror image warehouse from the target configuration file, wherein the first mirror image warehouse is one of private mirror image warehouses; modifying the first calling request into a second calling request according to the address information of the first mirror image warehouse, wherein the second calling request comprises the identifier of the target mirror image file and the address information of the first mirror image warehouse; and obtaining the target mirror image file according to the second calling request. Therefore, the success rate of obtaining the target mirror image file is improved.

The invention provides a code scanning function testing method, device and system, and the method comprises the steps: obtaining a to-be-scanned two-dimensional code picture of a business program on a tested device, a testing end is connected between the tested device and a code scanning device, and the business program runs on the tested device and the code scanning device at the same time; transmitting the two-dimensional code picture to a picture library of the code scanning equipment; controlling the code scanning equipment to load and scan the two-dimensional code picture; and testing a code scanning function of the business program according to a code scanning result of the code scanning equipment. Through the embodiment of the invention, the technical problem of low efficiency of testing the two-dimensional code scanning function in related technologies is solved, and the testing efficiency and the testing reliability are improved.

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.

The invention relates to the technical field of traffic accident detection, and provides a traffic accident detection data enhancement method, a marking method, a detection method and equipment. The invention discloses a traffic accident detection data enhancement method. The method comprises the following steps: constructing a roadside background picture library and a traffic accident picture library; the traffic accident picture library comprises traffic accident subjects and second traffic accident subjects which are in one-to-one correspondence; randomly selecting a background from a roadside background picture library, and randomly selecting a first traffic accident subject and a corresponding second traffic accident subject from a traffic accident picture library; based on the randomly selected background and the first traffic accident subject, determining a fused traffic accident picture; and generating a first traffic accident picture based on the fused traffic accident picture and the second traffic accident subject. Through the scheme of the invention, a large number of traffic accident pictures which are vivid in form, diverse in background and accord with the actual driving scene of the vehicle can be quickly generated, and the generation quality and efficiency of the traffic accident pictures are improved.

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 invention discloses a self-adaptive virtual sniffing system based on a mirror image warehouse and a deployment method, and relates to the technical field of network space sniffing behavior perception, the self-adaptive virtual sniffing system comprises an information acquisition module, a framework deployment module, a self-adaptive initial module, a detection module, a batch deployment module and a state switching module, the method has the advantages that through mirroring packaging and one-key scheduling deployment, rapid batch deployment of sniffing equipment is achieved, the deployment period is greatly shortened, a script is automatically initialized to adapt to a multi-network environment, the manual configuration link is eliminated, the deployment error risk is reduced, elastic expansion from a single node to a large-scale network is supported, and the deployment efficiency is improved. Through cooperation of active scanning and passive interception, multi-dimensional information such as network topology, service and routing is covered, configuration is completed through self-learning of network parameters, manual operation and maintenance frequency and cost are reduced, detection intensity is adjusted according to real-time flow, and network performance reduction caused by sniffing task overload is avoided.

The invention provides a sequence decision strategy acquisition method and system for high-neuroplasm crowds. The method comprises the following steps: establishing a full-screen drawing canvas, and loading a task set comprising a decision map type, an emotion titer ratio, a reward parameter, a random seed and a sequence control parameter; collecting user gender selection and binding a corresponding emotion picture library; generating a decision map based on a Markov decision principle under the control of the parameter set, defining nodes and connecting edges, randomly scheduling and laying emotion pictures according to a titer ratio, and assigning rewards to form an emotion and reward coupling environment; based on the environment, recording a mouse position and a mouse path in an execution exploration stage, recording key pressing time, an action direction and an award in an execution stage, and establishing step sequence association according to a timestamp to form sequence decision data; a Latin square balance control task sequence is adopted, and the process is propelled under the trigger of a user; and finally, filing the data in a binary system and a text file for subsequent analysis. According to the invention, the authenticity and repeatability of data are improved.

Provided in the present disclosure are a container cold start method, and a device, a system, a program product and a storage medium. Containers run on nodes in a node cluster. The method comprises: in response to receiving a warm-up request for a target container to be cold-started, selecting a warm-up node from a node cluster, and the warm-up node acquiring image data of the target container and storing same locally; and in response to receiving a cold start request for the target container, selecting a target node by means of a selection strategy of preferentially selecting the warm-up node, and the target node first locally attempting to acquire pre-stored image data and then attempting to acquire the pre-stored image data by means of a communicable node, acquiring, when the image data is not successfully acquired, the image data of the target container by means of an image repository, and finally using the acquired image data to start the target container.

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 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 disclosure provides a front-end project packaging method, device, electronic device and storage medium, including connecting to a code repository, obtaining a first file, a second file and a third file; the first file is used to provide project description and dependency configuration information, the second file is used to associate information of the first file, and the third file is used to provide project initial code; generating a container based on a container orchestration engine, and creating a target image in the container; copying the first file and the second file and storing them in the target image; based on the first file, adding a first resource file in the target image, the first resource file is used to provide project dependencies and conversion statements; converting the project initial code in the third file into target code in the target image; and pushing the target image with the target code to an image repository.

The present invention relates to the field of information security technology, and provides a method and device for encrypting and decrypting container images based on trusted computing. The method of the present invention includes: generating a public-private key pair through TKMS, applying for a ciphertext public key certificate from a trusted certificate management service, and synchronously transmitting the private key data to other trusted nodes managed by TKMS; creating a ciphertext image and ciphertext data, merging the created ciphertext image and ciphertext data, obtaining a ciphertext image file and uploading it to a ciphertext image repository; downloading the ciphertext image file from the ciphertext image repository to a trusted node, and decrypting the downloaded ciphertext image file. According to an encryption and decryption method and device for container images based on trusted computing in an exemplary embodiment of the present invention, by improving the security of the key during the encryption and decryption process of container images, the security and trustworthiness of the image running environment are ensured, and the automation degree of image construction and image operation is improved.

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.

The invention relates to a mirror image pulling method and device of a container node, electronic equipment and a storage medium, and belongs to the technical field of container, the method comprises the following steps: in response to a pulling request for a target mirror image file, obtaining a preferred node list for a current container node through a preset proxy plug-in in the current container node, the preferred node list comprises container nodes in the target container cluster, wherein the communication speed between the container nodes and the current container node meets a preset condition; searching a target container node in which the target mirror image file is stored from the preferred node list through the proxy plug-in; and pulling the target mirror image file from the target container node through the proxy plug-in. The problem that the speed is low when the container node pulls the mirror image from the container mirror image warehouse is solved, and the proxy plug-in is configured as a mirror image pulling tool when the container of the current container node runs, so that invasion to codes when the container runs can be reduced, computing resource consumption of the current container node is reduced, and the mirror image pulling speed is further increased.

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 invention provides a remote upgrading method, electronic equipment and a storage medium, the method is applied to a terminal, and the method comprises the following steps: in response to an upgrading notification of a server, downloading a target upgrading mirror image from a mirror image warehouse, the target upgrading mirror image being used for updating at least one application program of the terminal, the application program is deployed on the terminal through containerization; and running the target upgrading mirror image in a local container to update the application program corresponding to the target upgrading mirror image.

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.

A deep learning video detection method for target detection in tunnels with limited viewing angles includes the following steps: Step 1: Constructing a library of annotated and labeled images as a training sample set for a network model; Step 2: Constructing a network model: Building a detection network based on an SSD network, using VGG-16 with a Mish activation function and a batch normalization layer as the backbone of the detection network, using bidirectional multi-scale feature fusion at the jump layer as the neck of the detection network, and using the SSD network head as the detection network head; Step 3: Training the network model, using adaptive online hard-to-distinguish sample discovery based on target size to obtain a detection model; Step 4: Inputting a test sample, and using the trained detection model to predict the target location and category in the tunnel image. This method is suitable for target detection in tunnels and other environments with limited viewing angles, extremely small targets, and environmental interference, and exhibits excellent detection performance.

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.