38 results about "Container status" patented technology

The invention relates to the technical field of remote direct memory access, in particular to an RDMA shared receiving queue optimization method and device, equipment, a medium and a product. A shared receiving queue is divided into multiple containers, and the multiple containers are connected in series through a logic linked list; when the RDMA data of the queue pair arrives, if the container is not bound, distributing a container for the queue pair; monitoring the release state of the shared receiving queue entry; and when the receiving side software monitors that the target container is in the completely idle state, re-linking the target container into the logic linked list for distribution independent of the states of other containers in the plurality of containers. According to the method disclosed by the invention, dynamic reuse of resources can be realized through resource monitoring taking the container as granularity and an out-of-order recovery mechanism independent of other container states. After all the SRQEs in any container are released, the SRQEs can be immediately put into redistribution, so that the problem of waste of tail idle resources caused by occupation of head resources in a traditional linear queue can be thoroughly avoided.

The invention provides a high-availability system based on a container platform, a container calling method and electronic equipment, and the method comprises the steps: pre-creating and activating a container copy through a container scheduling module according to the resource state of each container node and a priority constraint condition in the early stage of fault occurrence, and then when a fault event occurs, calling the container copy in advance; and in response to the fault event reminding message, calling an interface of the container scheduling module to create a new service instance in the container copy, carrying out synchronous processing on the container state based on the new service instance, and migrating the flow on the fault container instance to the new container instance. Therefore, in a high-concurrency scene, by selecting the embodiment of the invention, the flow is immediately taken over through the pre-activated standby container copy when a fault occurs, so that zero-shutdown container switching is realized, the high availability of a container platform is remarkably improved, and smooth operation of services is ensured.

The invention discloses a user working environment persistent storage method based on a double-layer container. The method comprises the steps that mirror images of all public containers in mirror image private server are pulled to a shared storage space to form a hot loading area; dividing a corresponding working space from the shared storage space, querying a target storage block corresponding to a target public container mirror image from a hot loading area based on a public container mirror image identifier in a creation request from a client, copying the target storage block to the working space to serve as a system disk, and further establishing a double-layer container and starting the double-layer container; in response to a modification request of a client, performing corresponding modification operation on the initial state inner layer container to obtain a target state inner layer container, so as to run a user working environment based on the target state inner layer container to execute a computing power task; and storing a file corresponding to the modification operation in the remaining space except the system disk in the workspace to form a user mirror image layer. According to the invention, the persistent storage efficiency of the container state and the user working environment can be improved, and the rapid restarting of the container can be realized.

The invention provides a data processing method and device based on operation of a quantitative transaction strategy, and the method comprises the steps: pulling a container list comprising container information from a cloud service, the container information comprising a first container state of a container which operates the quantitative transaction strategy; acquiring a locally stored strategy running list comprising strategy running records, wherein the strategy running records comprise a second container state of the container; for any container in the container list, obtaining a first container group matched with the container of the strategy operation record and a second container group not matched with the container of the strategy operation record from the strategy operation list based on the strategy operation list; updating the first container state of the first container group or a strategy operation record according to the first container state of the first container group and the second container state; and according to the first container state of the second container group, generating a first container operation instruction for indicating the cloud service to execute processing operation on the second container group to the cloud service so as to improve the matching accuracy of the quantitative transaction strategy and the container resources.

The application belongs to the field of cloud computing, and is a management method of a computing power cloud architecture combining a container cloud and a virtual machine cloud, a cloud management platform, a host resource management layer and a resource access layer, and comprises the following steps: S1: a host resource management module registers with the cloud management platform; S2: a registration and scheduling module sends a container creation instruction, and a host creates and schedules a container; S3: the host resource management layer reports idle resource information to the cloud management platform, and the cloud management platform triggers the binding and deployment of the container and the host; S4: a virtual machine access module sends a virtual machine creation instruction, and deploys a virtual machine to a specified host; S5: a resource access layer respectively collects and reports the running states of the container and the virtual machine; S6: a container state monitoring module sends a container unbinding and destruction instruction, and a host resource management module executes the operation; S7: a virtual machine host module sends an unbinding instruction, executes virtual machine unbinding, and a resource access layer releases resources.

The invention relates to a container state detection method and device, computer equipment and a storage medium. The method comprises the following steps: deploying an isolation environment in each container of a physical network, and setting a detection tool for detecting the state of the container and a detection script of the detection tool in the isolation environment; when the information of the to-be-detected container is obtained, sending the information to a proxy probe service pre-deployed in a physical network so as to match the to-be-detected container from the physical network; and controlling the detection tool to execute the detection script in the isolation environment of the to-be-detected container through the proxy probe service, and outputting state information of the to-be-detected container determined after the detection script is executed. When the method is used for detecting the state of the container, network isolation can be maintained, and potential safety hazards are avoided.

This invention belongs to the field of transportation technology and is a collaborative planning method for charging and swapping facilities for electric heavy-duty trucks in port collection and distribution scenarios. The method includes: establishing a deployment plan for charging and swapping facilities under the constraint of the system's total budget; establishing a transportation model and an energy consumption model based on container status and flow direction; establishing a macroscopic distribution model of charging demand to characterize the generation of potential charging demand and its spatial allocation among facilities; establishing a queuing time model and setting constraints based on fleet size; establishing an optimization model with the goal of maximizing the system's total profit; relaxing the optimization model and directly controlling the allocation of charging demand at each node through operational scheduling; decomposing the relaxed optimization model into independent node subproblems using Lagrange decomposition; and updating the Lagrange multipliers using a subgradient algorithm, iteratively coordinating each subproblem to converge to the optimal solution. This invention solves the problem of collaborative planning for charging and swapping facilities for electric container truck fleets in port collection and distribution scenarios.

The present disclosure relates to the technical field of electric power, in particular to a model training method and a software-defined terminal operating system evaluation method. The model training method comprises: obtaining container state information, terminal state information, application program state information in a first time period, and power distribution equipment state information in a second time period; obtaining power distribution terminal feature information according to the container state information, the terminal state information and the application program state information, and obtaining power distribution equipment feature information according to the power distribution equipment state information; and training a software-defined terminal operating system evaluation model according to the power distribution terminal feature information and the power distribution equipment feature information to obtain a target software-defined terminal operating system evaluation model. The scheme solves the problem of complex evaluation conditions required for evaluating power distribution equipment management, reduces the cost and improves the accuracy of the evaluation result.

The invention discloses a self-adaptive container resource scheduling system, which comprises a resource monitoring module, a scheduling algorithm module and a container management module, and is characterized in that the resource monitoring module is used for monitoring the load condition and the resource use condition of the system; the scheduling algorithm module is used for selecting a proper scheduling algorithm according to different load conditions and performance requirements of application programs so as to realize self-adaptive scheduling, and the container management module is used for managing life cycles of containers, including creating, starting, stopping and deleting operations of the containers and monitoring states of the containers. The system comprises a resource monitoring module, the resource monitoring module is used for monitoring resources and allocating and scheduling the resources according to needs, the resource monitoring module comprises a monitoring index defining module, a monitoring method calling module, a data processing module and an alarm module, the data processing module is electrically connected with the alarm module, and the monitoring index defining module is electrically connected with the monitoring method calling module. The method has the characteristic of dynamic adjustment.

The application discloses a K8s cluster container state adjustment method, device and equipment and a storage medium. The container address is registered in an Endpoint list when the container is normally running, and it is judged whether the container belongs to a preset disabled Pod list. If the container belongs to the preset disabled Pod list, the container address is removed from the Endpoint list and written into the preset disabled Pod list. When a state adjustment instruction is detected, the container address stored in the preset disabled Pod list is written into the Endpoint list again. Since the container address registered in the Endpoint list is removed and then written into the preset disabled Pod list, the container can be suspended from providing external services when the container is normal, and the online adjustment of the container state is realized. Meanwhile, when the suspended container needs to be restored, the container address stored in the preset disabled Pod list is written into the Endpoint list again, the state switching of the container from the suspended access to the normal access is realized, and compared with the existing container which only has the normal and abnormal states, the above method increases the flexibility of the K8s cluster container state management.

The invention relates to the technical field of GPU resource allocation, in particular to a GPU resource dynamic allocation and cross-node migration method and system based on a containerization environment, and the method comprises the steps: monitoring a container state corresponding to a container in real time, and collecting dimension data corresponding to each dimension in a corresponding data collection dimension set; evaluating the GPU resource use condition corresponding to the container at the current moment, and generating corresponding GPU resource evaluation result data; judging whether the container needs to perform data migration at the current moment or not; if yes, identifying and capturing an incremental data layer generated when the container runs; pre-deploying the same basic mirror image layer on the target node, and configuring a required GPU resource specification; quickly migrating the captured incremental data layer to a target node; and starting a new container on the target node based on the basic mirror image layer and the migrated incremental data layer.

This invention relates to the field of GPU resource allocation technology, specifically to a method and system for dynamic allocation and cross-node migration of GPU resources in a containerized environment. The method includes: real-time monitoring of the container status corresponding to the container; collecting dimensional data corresponding to each dimension in a data collection dimensional set; evaluating the GPU resource usage of the container at the current moment and generating corresponding GPU resource evaluation result data; determining whether the container needs data migration at the current moment; if so, identifying and capturing the incremental data layer generated during container runtime; pre-deploying the same base image layer on the target node and configuring the required GPU resource specifications; quickly migrating the captured incremental data layer to the target node; and starting a new container on the target node based on the base image layer and the migrated incremental data layer.

The invention provides a non-standard sample container abnormity identification method and system, and the method comprises the steps: generating a container state sequence, carrying out the multi-frame superposition of continuous images of a same digital identity container, generating a motion track geometric envelope, extracting geometric feature parameters from the geometric envelope, and carrying out the recognition of the abnormity of a non-standard sample container. Calculating the speed and acceleration of each container based on the container state sequence, obtaining a geometrically determined expected dynamic sequence according to the geometrical characteristic parameters and the current speed and acceleration, and obtaining a residual abnormal characteristic sequence through a difference result of the actually measured dynamic sequence and the expected dynamic sequence; and calculating a cross correlation coefficient between the expected dynamic sequence and the actually measured dynamic sequence, obtaining a geometric envelope offset of the container through the residual abnormal feature sequence, calculating a damping offset based on the cross correlation coefficient, obtaining a label identification confidence coefficient, calculating a comprehensive abnormal score, and determining an abnormal level. For the problem of container geometric morphology diversity in cross-department or cross-hospital sample transfer, the misjudgment rate of cross-institution sample recognition is reduced, and the detection stability is ensured.

The invention discloses a decentralized container service system and a working method, and belongs to the field of computer edge computing. The decentralized container service system comprises a block chain, a decentralized container service management node set, a container working node set and a decentralized storage system. The working method of the decentralized container service system comprises the following steps: receiving a user container leasing order and verifying the legality of an account; generating a distributed scheduling proposal and achieving a global decision through a consensus mechanism; issuing a deployment instruction to the target container working node and executing container creation; the states of the nodes and the containers are monitored in real time, and fault self-healing is triggered; order settlement and reward distribution are completed, and resources are released. According to the method, the problems of single-point failure, low resource utilization rate, scheduling concentration and the like of the traditional centralized container service can be effectively solved, and the method is particularly suitable for container service requirements under the scenes of distributed computing power sharing, edge computing and the like.

The invention discloses a movable plastic container recycling accounting system and method based on AI interaction, and the system comprises a recycling bin which is used for accommodating a plastic container; the AI interaction module is used for generating a system scheduling instruction according to the recovery semantic data of the user and the working condition data of the system; the autonomous moving module is used for controlling the system to move according to the system dispatching instruction; the multi-dimensional feature sensing module is used for acquiring multi-dimensional feature data of the plastic container to be delivered; the edge main control module is used for judging the container state of the to-be-delivered plastic container based on the multi-dimensional feature data and generating a container control instruction according to the container state; the delivery detection and execution module is used for delivering or intercepting the to-be-delivered plastic container according to the received container control instruction and compressing the delivered to-be-delivered plastic container; and the recovery accounting module is used for generating a digital certificate according to the accounting data of the delivered plastic container to be delivered. A high precision digitized removable plastic container recovery accounting system capable of responding to user requirements is presented.

The application relates to a container state recovery method and device, computer equipment, a storage medium and a software robot. The method comprises the following steps: detecting, in real time, a container state of a target container running in a business environment and resource usage of the business environment; when the container state is abnormal, accessing a pre-configured self-recovery environment in the business environment, and performing state self-recovery on the target container according to the resource usage; using state data of the target container to pull up a standby container corresponding to the target container in the self-recovery environment, and transferring traffic of the target container to the standby container for processing; continuously detecting the container state of the target container, and when the container state returns to normal, re-registering the target container in the business environment, and distributing traffic to the target container for processing. The method can intelligently realize self-recovery of a fault container while ensuring continuous traffic service, thereby reducing operation and maintenance labor costs and improving container state recovery efficiency.

The invention discloses an alarm processing method, device and equipment of a cloud platform, a medium and a program product, relates to the technical field of exception handling, can be applied to the financial science and technology field, and comprises the following steps: obtaining container state information of at least one container and host machine state information of at least one host machine in the cloud platform, and setting the state information of the at least one host machine in a set time period; container alarm information of the cloud platform; according to the container alarm information in the set time period, identifying an alarm container and an unprocessed retention container in the set time period; determining an operation container of the cloud platform in a set time period according to the container state information of the alarm container and the host machine state information of each host machine; according to the number of the alarm containers, the number of the operation containers and the number of the unprocessed retention containers, the target abnormal probability of the host machine in the cloud platform is calculated; and processing the cloud platform according to the target abnormal probability of the host machine. According to the method, the abnormal probability of the host machine in the cloud platform is accurately determined, and the abnormal processing efficiency of the cloud platform is improved.

The method can include: determining image data; identifying a container using an object detector; and classifying a container state. The method can optionally include performing an action based on the container state classification. However, the method can additionally or alternatively include any other suitable elements. The method functions to detect and classify the state of bowls (i.e., complete / incomplete) to facilitate ingredient insertion into (unserved, ‘incomplete’) bowls along an assembly line.

Embodiments of the present specification provide a container kernel upgrading method and device based on a programmable container kernel. In the method, in response to receiving a container kernel upgrading request, an application container in which a first container kernel to be upgraded is located is frozen; current container state data of the application container is saved, the current container state data including first container kernel state data of the first container kernel and application program state data of all container application programs running in the application container; the application container is restarted using a second container kernel for container kernel upgrading, the container state data of the restarted application container including second container kernel state data of the second container kernel at the time of restarting the application container; corresponding data fields in a data structure of the restarted container state data are updated using the saved current container state data; and in response to completion of the container state data updating, the container application programs in the frozen application container are resumed to run in the restarted application container.

The present disclosure relates to a method for use in an automated storage and retrieval system, the automated storage and retrieval system comprising a storage grid comprising a plurality of stacked containers, the method includes capturing, by at least one sensor on the robotic container handling vehicle, data indicative of a status of one of the plurality of stacked containers; determining a state of the container based on the data; and controlling operation of the robotic container handling vehicle based on the state of the container.

The invention relates to a multi-attribute constrained online three-dimensional efficient boxing method and system and a storage medium. The system comprises an acquisition module which is used for constructing a three-dimensional voxel grid to carry out structured representation on a container space, capturing multi-scale space features and outputting a container state feature vector; a multi-dimensional attribute representation system is constructed, the multi-dimensional attribute representation system comprises basic geometric attributes, physical attributes and constraint conditions, and article feature vectors are generated through nonlinear mapping; mapping and establishing dynamic association between the container state feature vector and the article feature vector, and generating a joint feature vector; and inputting the combined feature vector into a strategy optimization model, outputting an article placement posture and predicting the placement position of the article. According to the method, a multi-dimensional feature system of physical attributes and engineering constraints is innovatively introduced in an article feature coding stage, and the physical attributes and constraint conditions are included in addition to basic geometric parameters. And the boxing safety and the practical applicability in a complex industrial scene are remarkably improved.

Embodiments are directed toward a system and method relating to checking container status. For example, a system includes at least one antenna and a controller to receive radio frequency (RF) signals. The controller determines a first indication of RF signals received via the at least one antenna at a first time. The controller determines a second indication of RF signals received via the at least one antenna at a second time after the first time. The controller determines a difference between the first indication and the second indication, and compares the difference to a threshold. The controller indicates indicate a status change or no status change of the container responsive to the difference exceeding or falling below the threshold.

This invention discloses a smart storage container status data monitoring and management system and method based on the Internet of Things (IoT), relating to the field of staged status data analysis technology for smart storage containers. The method includes: integrating a multi-sensor monitoring device into the smart storage container body; real-time acquisition of physical state data of the substances inside the container; dividing the smart storage container into several working stages to obtain characteristic state parameters for different working stages; analyzing environmental factors affecting the changes in the characteristic state parameters; fitting the relationship between the characteristic state parameters and environmental factors based on several historical observation samples; determining the best fitting expression through a goodness-of-fit index; calculating the predicted value of the characteristic state parameters under the current environment using the best fitting expression; calculating the characteristic error degree based on a preset characteristic state parameter benchmark value; and issuing a real-time warning when the error exceeds a preset threshold. This method is applicable to various application scenarios such as liquid storage, significantly improving the system's intelligent monitoring level and security capabilities.

The embodiment of the application provides a control method and system for algorithm application of an edge computing device, a container state monitoring module and an artificial intelligence engine development kit are configured for the container in the container; the container state monitoring module is used to determine a first hardware device matched with the algorithm application from a plurality of hardware devices; the container state monitoring module is used to call the artificial intelligence engine development kit to obtain hardware resource occupation data of the first hardware device; the container state monitoring module is used to determine a second hardware device meeting an algorithm application running condition from the first hardware device based on the resource occupation data; the artificial intelligence engine development kit is called to enable the algorithm application in the second hardware device, so that the algorithm application is remotely deployed on the edge device in the cloud, and meanwhile, the hardware resources available for the application can be dynamically allocated according to the load of the device, and the load condition of the edge device can be mastered in real time.

This invention relates to the fields of cloud computing, artificial intelligence infrastructure, and container resource scheduling technology, specifically an Agent-Schedule-based GPU scheduling system. The system includes: a Scheduler Center for receiving resource request and release requests from service callers, maintaining the real-time status of all hosts, GPUs, and containers in the cluster, generating and issuing container operation instructions, and processing execution callbacks from Agent nodes; Agent Resource Nodes for registering their local GPU hardware information, periodically reporting container running status and resource usage, receiving and executing container operation instructions, and sending callbacks of execution results; and a database for persistently storing resource configuration, running status, and instruction flow records, providing data support for global lock concurrency control, instruction execution closed-loop, and container state machine management. This solves the problems of slow resource request response, low GPU memory utilization, and poor scheduling execution reliability in existing technologies.

The application belongs to the field of container arrangement, and is a high-efficiency container resource pool management method and device based on Kubernetes, comprising the following steps: S1: obtaining configuration parameters, performing resource proportioning, constructing a proportioning model, generating idle containers, and preloading high-frequency dependencies, creating a Namespace to complete isolation initialization and entering metadata; S2: matching idle containers through an adaptive model, binding an app and granting minimum permissions according to a preset; S3: verifying a release request of the app, recovering permissions of the app, preloading high-frequency dependencies, and synchronizing to metadata; S4: collecting pool load data, predicting a load condition, executing expansion and contraction strategies, and synchronizing instance quantity and state; S5: defining multi-state flow rules of containers, monitoring and verifying legality of changes in real time, and if illegal, intercepting, persisting state data, and providing a query service; and S6: obtaining container state data and synchronously feeding back to a life cycle management and control module after changing the container state.

The invention discloses a container resource adjustment method and device, equipment, a medium and a product, relates to the technical field of computers and cloud computing, and can be applied to the field of financial science and technology. The method comprises the following steps: when a container set is started, obtaining starting resource configuration information of a target container; resource allocation is carried out on a target container based on the starting resource configuration information, so that the target container is started; determining whether the container state of the target container is changed or not through the container probe; if so, determining a target state of the target container; and according to target system resource information corresponding to the target state, performing configuration adjustment on resources of the target container. According to the technical scheme, the container resources can be dynamically optimized, and the container performance is improved.

The invention provides a low-code-oriented Kubernetes-based lightweight micro-service hosting and integrating method, which belongs to the technical field of micro-services, and comprises four parts: a micro-application abstract model for packaging back-end services into declarative resources with life cycle, version, configuration and dependency; the application controller and state machine is used for managing the operation state of the container and controlling the state conversion of the container; multi-version evolution capability: supporting strategies of micro-application version coexistence, gray release, automatic rollback and the like; and integration of the micro-application container and the platform: integration of application container arrangement and the low-code platform. The problems of high labor cost and low controllability of a low-code platform in dynamic service deployment are effectively solved.

Method and inspection system for monitoring a state of a container (110) adapted and configured to hold wafers or reticles, the container comprising a body (115), a lid (120) and a gasket (122) provided on the body (115) or on the lid (120), the gasket (122) being configured and adapted to ensure a gas-tight seal between the body (115) and the lid (120) in order to provide a closed space within the container (110), which is gas-tightly sealed relative to an outside environment, the method comprising imaging the gasket (122) against a background (106), wherein the gasket (122) and the background (106) are provided with different colours and / or are provided with different patterns.

This invention discloses a method, apparatus, device, and storage medium for managing container status. The method includes: upon receiving a shutdown request for a target container group, receiving a shutdown configuration file containing container group association data for the target container group; querying the server node where the target container group resides based on the shutdown configuration file, creating a temporary execution task on the server node, and issuing a container commit command to the temporary execution task through the container engine's application programming interface; saving all content within the target container group as a container image based on the container commit command; and shutting down the target container group upon completion of the container commit command. This achieves the technical effects of ensuring no loss of content within the container group, improving the flexibility of container usage, and avoiding data loss due to single points of failure.