138 results about "Containerization" patented technology

The invention discloses an AI model automatic deployment platform based on a containerization technology, which relates to the technical field of automatic deployment of an artificial intelligence model, and comprises a container construction and intelligent configuration module, a transmission and cache management module, a security and multi-version warehouse module, a resource scheduling and optimization module and a deployment and interface management module, the container construction and intelligent configuration module adopts a three-layer mirror image construction strategy of a base layer, a framework layer and a model layer. According to the invention, all the modules cooperate to form a closed loop, after the container construction module generates an incremental packet and the incremental packet is subjected to security signature verification, the transmission module distributes the incremental packet according to network quality, the resource scheduling module dynamically adjusts bandwidth and quota, and the deployment module starts the container and monitors the container in real time. The problems of efficiency, safety and resource optimization of model deployment in an edge environment are solved, and cross-platform compatibility and service continuity are improved.

The invention discloses an agent platform resource management method and device based on a cloud native architecture and a medium, and the method comprises the steps: packaging an agent application into an independent container instance based on a containerization technology, and deploying the container instance to a target node; acquiring task demand information of the intelligent agent in real time, and generating a dynamic scheduling scheme by combining the resource state data and through a multi-target optimization algorithm so as to allocate the task to a target container instance; according to a matching function of the capability vector of the intelligent agent and the task demand vector, calculating the integrating degree of the intelligent agent and the task so as to generate a collaborative decision-making result and issue the collaborative decision-making result to the target intelligent agent; the resource utilization rate and the task execution state of the intelligent agent are monitored, an elastic telescoping mechanism or task rescheduling is triggered according to feedback data monitored in real time, and a resource allocation strategy is dynamically adjusted.

The invention relates to the technical field of distributed simulation based on containerized deployment and elastic expansion and contraction, and discloses a distributed simulation method and system based on containerized deployment and elastic expansion and contraction. According to the distributed simulation method and system based on containerized deployment and elastic expansion, real-time resource monitoring and historical load trend data of a simulation task are collected, and unified load evaluation is carried out in combination with a simulation calculation complexity parameter and an I / O density parameter; the refined modeling and resource demand pre-judgment of the simulation task are realized, and the accuracy of task scheduling and allocation is improved; modularized deployment of simulation tasks is achieved through subtask segmentation based on minimum executable units and a standard containerization packaging mechanism in cooperation with a container arrangement platform, and then concurrent execution and elastic scheduling in a multi-node environment are supported.

The invention relates to the technical field of computers, in particular to a containerized scene fault prediction and self-healing method and device, equipment and a storage medium. The containerized scene fault prediction and self-healing method comprises the following steps: obtaining resource use data related to a container in real time by using a sliding window, carrying out noise filtering on the resource use data, generating time sequence characteristics, and obtaining preprocessed data; analyzing the preprocessed data by adopting an isolated forest model to obtain an abnormal score, and judging whether a node of the container has a fault risk or not according to the abnormal score; and carrying out corresponding self-healing operation on the fault node according to the fault type by adopting a pre-constructed self-healing strategy. By analyzing the container resource data, the fault nodes and the types of the fault nodes can be effectively identified, the self-healing operation is pertinently executed, the resource utilization rate is effectively increased, and the core service continuity is guaranteed.

The invention discloses a power intelligent equipment operation and maintenance management method based on artificial intelligence, and the method employs a staged training strategy, combines a LSTM and CNN mixed structure to carry out fault prediction, effectively captures an equipment operation state change trend, recognizes a potential fault mode, remarkably improves the fault prediction precision, and meanwhile, improves the fault prediction efficiency. A flexible self-healing strategy is designed according to the fault type and severity, the adaptability of a self-healing process and the capacity of dealing with complex faults are enhanced, in addition, a trained equipment health assessment, fault prediction and self-healing model is packaged into RESTfu API service in a micro-service mode and is deployed to an edge computing module through Docker containerization, and the service performance of the edge computing module is improved. Intellectualization and integration of the operation and maintenance management system are promoted, the edge calculation module inferes and monitors the equipment state in real time and generates early warning information, real-time monitoring and early warning are achieved, once abnormity is found, self-healing operation is triggered immediately, manual intervention is reduced, and the operation and maintenance efficiency is improved.

The invention provides a resource security isolation method in a containerization environment, which relates to the technical field of containerization, and comprises the following steps: selecting a target container technology meeting a resource security isolation requirement; obtaining a container isolation security policy based on a target container technology, and realizing a resource security isolation operation between the containers; abnormal behaviors of the containers are detected in real time, and regular auditing and risk assessment are carried out to ensure the isolation between the containers. By selecting a container technology meeting the current resource security isolation requirement, a container isolation security strategy is generated in combination with an operating system environment, and resource security isolation operation between containers is achieved; according to the method, abnormal behaviors of the containers are detected in real time, regular auditing and risk assessment are carried out, so that isolation between the containers is ensured, the safety, stability and reliability of the system are effectively improved, and resource utilization and management are optimized.

The invention discloses an operating system containerization kernel compatibility method based on shadow structure mapping, which comprises the following steps of: constructing a multi-dimensional mapping rule base by analyzing the difference of data structures between a container system and a standard system, and creating a shadow structure comprising a data area, a metadata area and an expansion area when the container system runs on a host system; the host system allocates a shadow memory space and a memory pool, establishes mapping between a shadow structure and a physical memory page and bidirectional mapping between the shadow structure and a host structure, intercepts system call of a container application, obtains a target structure and allocates a shadow structure and a host structure memory, and sends the shadow structure and the host structure memory to the host system; according to the method, conversion from the target structure to the host structure is completed according to the multi-dimensional mapping rule base, the host machine completes calling through the host structure, then the calling result is reversely converted into the target structure and returned to the container application, and seamless compatibility of the container system and the host system is achieved under the condition that a user mode program of the container system is not modified.

The invention belongs to the technical field of containerization packaging, and discloses a rapid containerization packaging method, system and device for generative AI applications and a medium. The system comprises a dynamic adjustment decision module, an adjustment decision automatic execution module, a load prediction analysis module and a predictive optimization execution module, carries out dynamic adjustment processing based on a processing data set to obtain an adjustment decision report, executes the adjustment decision report, obtains a decision execution report according to an execution result, and carries out load prediction analysis according to the decision execution report. The method comprises the following steps: performing analysis based on a historical processing data set and a historical decision execution report to obtain a load prediction report, optimizing resource pre-configuration based on the load prediction report, and performing execution according to an optimization result to obtain a predictive execution report. The method has the remarkable advantages of being high in data monitoring comprehensiveness, good in system adjustment response effect and high in prospective resource cooperation capability.

The invention discloses an AI component calling method and device, a medium and a computer program product, and relates to the technical field of container mirroring, the method comprises the following steps: when a first calling request sent by a large model application through a gateway is detected, determining a target AI component container instance corresponding to the first calling request, the AI component container instance is a container instance obtained after containerization packaging processing is carried out on an AI component capable of being deployed independently, and comprises a model file and a protocol adapter; performing format conversion on the first calling request through a protocol adapter in the target AI component container instance according to a preset protocol format to obtain a second calling request; calling a model corresponding to the model file according to the second calling request for task processing to obtain a processing result; and packaging the processing result into a response message through the protocol adapter according to a preset protocol format, and sending the response message to the large model application through the gateway. According to the method, the calling efficiency of calling each AI component by the large model application is improved.

The invention discloses a multi-modal intelligent question and answer system and interaction method based on a privatized deployment large language model. The system comprises a model containerization deployment module, a multi-round question and answer and intention recognition module, a document analysis and multi-modal question and answer module, a knowledge base construction and retrieval enhancement generation module and a system integration and monitoring module. A DeepSeekR1 large language model, an inference service and a dependency library are packaged into a container mirror image through a containerization operation environment Docker, and unified scheduling and dynamic capacity expansion and contraction are achieved through Kubernetes; realizing context understanding and task distribution by adopting a dialogue state management and intention recognition technology; in combination with OCR, an embedded model BGE / SimCSE and a vector database FAISS / Milvus, semantic analysis and intelligent question and answer of multi-format documents such as PDF, Word and images are supported; and a hierarchical knowledge base is constructed and a retrieval enhancement generation technology is fused to improve the answer accuracy. According to the invention, safe, efficient and intelligent management and interaction of enterprise knowledge are realized.

The invention provides a satellite-borne basic mirror image and customized mirror image containerization deployment operation system and method, and the method comprises the steps: packaging a fixed basic service to form a basic mirror image, such as a satellite-borne hardware drive program, an AI resource interface program, a storage resource interface program, a telemetering and remote control bus interface program, and a PCIE / 10-gigabit network interface high-speed bus interface program; and subscribing to a distribution interface program among satellites and the like. The customized mirror image uses the basic mirror image through a docker FROM command; the customized mirror image comprises a remote sensing intelligent interpretation AI model mirror image, an inter-satellite cooperative task planning mirror image, a user data issuing broadcast mirror image and the like; the basic mirror image serves as factory software firmware of satellite development, the customized mirror image can be tested without binding during batch production of satellites, the basic mirror image is printed on the sky, and the customized mirror image is subjected to on-orbit programming according to user requirements, or a service operation mirror image is pulled from a space-based private mirror image warehouse.

The invention discloses a cross-platform desktop application barrier-free test method and system based on a containerization environment, and the method comprises the steps: constructing a test container containing an AT-SPI proxy DBus communication module, enabling the read-only of the test container to mount a host machine DBus socket and limit the right, and enabling a host machine to deploy a DBus bridging proxy as a cross-environment channel; the test container communicates with a host machine, other containers or desktop applications of a sandbox through the channel according to a target type, and the bridging agent dynamically analyzes a virtual DBus address forwarding request in a sandbox scene; the test container sends an AT-SPI request, and interface element capture is completed after returned control data is verified by the bridging agent; and finally executing an automatic test based on the element data and verifying a result. The invention aims to realize dynamic capture and automatic test of desktop application interface elements across host machines and containers / sandboxes.

The invention relates to the technical field of ship manufacturing, and discloses a general assembly construction data service encapsulation and construction method based on a cloud native technology. The method comprises the following steps: dividing a ship final assembly construction data source into design, production, quality inspection, logistics and operation and maintenance data units, and determining a data type and an interface protocol; then, constructing an initial service model based on a micro-service architecture and a containerization technology; then, dynamic service packaging is carried out, and cross-model data routing and load balancing are achieved; dynamically allocating resources and scheduling tasks through a multi-target scheduling algorithm model; iteratively optimizing the service model by utilizing an incremental learning mechanism; and finally, realizing gray release and rolling update by continuously integrating and continuously delivering assembly line deployment to a cloud native platform. The ship general assembly construction data processing efficiency, the system performance and the service quality are improved, and the problems of disordered data management, low processing efficiency, unreasonable resource allocation and the like in a traditional mode are effectively solved.

The invention discloses an LLMOps-based large model efficient operation and maintenance method and system, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of how to realize large model efficient operation and maintenance, ensure that the large model quickly responds to business requirements in the application process and maintain the performance and stability of the large model. Comprising the steps that an LLMOps tool chain is constructed based on a model deployment tool, a model detection tool, a model upgrading tool, a model running tool and a model iteration tool, and the model deployment tool deploys a large model based on containerization and standardization and CI / CD assembly line automation; the model detection tool is integrated with a verification framework and a real-time monitoring system; the model upgrading tool applies a service network technology to implement gray level upgrading and A / B testing; the model running tool realizes elastic expansion and load balancing of the model service based on an automatic capacity expansion and contraction mechanism of the container arrangement platform; and a model dynamic iteration mechanism based on feedback driving is configured in the model iteration tool.

The invention discloses a waste incineration intelligent control system based on artificial intelligence and containerization architecture, and relates to the technical field of waste incineration. Comprising a model reasoning server and a model training server, the model reasoning server and the model training server carry out micro-service packaging of functional modules through a containerization technology, standardized data interaction is carried out between containers based on an HTTP protocol, and the containers are compatible with industrial protocols such as OPC and RTSP to be in butt joint with external equipment. According to the scheme, a distributed containerization architecture design is adopted, a model training server and a model reasoning server are taken as core hardware carriers, micro-servitization packaging of functional modules is realized through a Docker container technology, standardized data interaction is carried out between containers based on an HTTP protocol, and meanwhile, industrial protocols such as OPC and RTSP are compatible to be butted with external equipment, so that the data interaction between the functional modules is realized. And meanwhile, the incineration working condition is predicted in real time through an artificial intelligence model, equipment parameters are dynamically adjusted in combination with a control algorithm, and the incineration efficiency optimization target is achieved.

The invention relates to the field of map data processing, and particularly provides a railway space data processing and integrating method based on a GIS (geographic information system) map, which comprises the following steps: constructing a geographic information system by adopting a distributed resource management technology; a containerization and micro-service architecture is adopted, businesses of the geographic information system are split into independent service units, and the independent service units run in a Docker container; integrating the basic base map data, constructing a basic spatial data resource pool, establishing railway thematic data, loading map data, performing data analysis and processing, and constructing a railway spatial map; dynamic updating and interaction effects of the railway space map are realized by processing user input events; railway facility information is superposed on a railway space map in a layer form by adopting a layer superposition technology, so that visual display of railway space information is realized. Based on a GIS one-map service mode, integration and sharing exchange of spatial information resources are achieved, and all-weather reliable support is provided for railway space application.

System and method are provided where parcels or packages are associated or grouped, into logical group or logical containerization of parcels or packages, without need for physical container, such that parcels or packages can be tracked as a group, for example with a unique group ID. Logical group may be tracked within a specified logical zone, transported, sorted and / or otherwise processed as unique logical group in container, or without need to be contained in physical container. System and method for automated sortation can accumulate set number or set volume of packages, and then process the accumulated set number or volume of packages.

The invention discloses a service adaptation method and device based on a skill dependence graph and QoS perception, and a robot method comprises the steps: responding to a task execution request sent by a client, and according to task demand information carried by the task execution request, presetting the skill dependence graph, and retrieving a plurality of available alternative atomic micro-services; the preset skill dependence graph is composed of a plurality of nodes and edges between the nodes; under the resource constraint of a preset multi-objective optimization model, determining an optimal service combination scheme from the plurality of alternative atomic micro-services; carrying out containerization deployment on each atomic micro-service in the optimal service combination scheme to obtain a deployed micro-service instance for task execution; in the execution process, when it is detected that service performance degradation or resource bottleneck exists according to the QoS index and resource information sensed in real time, the optimal service combination scheme is optimized, and the step of containerization deployment continues to be executed. Therefore, by adopting the embodiment of the invention, the execution efficiency of the task can be improved.

The invention relates to the technical field of research and development of container liquid bag materials, and discloses a composite film for manufacturing a container liquid bag and a preparation process of the composite film, specifically, on the basis of a Friedel-Crafts alkylation reaction mechanism, phenyl-POSS functional polyethylene and phenyl-POSS functional polypropylene are grafted on the surface of alkenyl functional interlamellar spacing stable montmorillonite, and the composite film is prepared through the following steps: grafting phenyl-POSS functional polyethylene and phenyl-POSS functional polypropylene on the surface of alkenyl functional interlamellar spacing stable montmorillonite; the organic and inorganic hybrid high-strength filler used for modifying the polyolefin matrix is prepared by using the organic and inorganic hybrid high-strength filler, the composite film is prepared through an eleven-layer co-extrusion blow molding process, and the film product meets the technical requirements specified in the standard of TB / T 2689.4-2018 Part 4 of Railway Cargo Containerization Transport: Disposable Container Liquid Container Bag, and can be used as an inner bag material of a disposable Container Liquid Bag.

The utility model discloses an end cover packaging and cleaning device which comprises a packaging frame, the packaging frame comprises a storage rack and a packaging cage for containing the storage rack, the storage rack is provided with a plurality of fixing rods, and one side of the packaging cage is provided with a lifting grabbing lug; the turnover device comprises a turnover base and a turnover base table rotationally connected with the turnover base, and a turnover driving device is arranged in the turnover base and the turnover base table in a penetrating mode; the functional cleaning tank comprises an ultrasonic cleaning tank, a first cold water tank, a second cold water tank, a hot water tank and an anti-rust liquid tank which are arranged in sequence; the lifting device is used for lifting the pallet from the turnover device to the functional cleaning tank; the electric vehicle end cover cleaning device can effectively solve the problem that an existing cleaning mode is poor in batch cleaning efficiency, and an efficient and feasible solution is provided for the electric vehicle end cover cleaning industry.

The invention relates to a containerized method for the thermal treatment of contaminated solids, pasteous materials, and waste. The system comprises at least one thermally insulated container designed to minimize heat loss and enable material loading from a liftable top panel or a side panel, as well as at least one heat production unit featuring a burner control system and a combustion chamber capable of achieving oxidizing conditions for the complete destruction of contaminants. The method provides simultaneous thermal treatment of materials and vapor-phase contaminant destruction, supported by a ventilation system and a monitoring system for real-time adjustments. In a second aspect, the invention provides a containerized device with comparable operational features, designed for portability and modular deployment, facilitating on-site remediation in remote locations.

The invention relates to a part boxing feasibility discrimination method based on a graph neural network, and the method comprises the steps: S1, receiving the size information of a to-be-discriminated container, and the size information of all to-be-boxed parts; s2, constructing container nodes and part nodes; s3, generating feature representations of container nodes and part nodes; s4, establishing container part edges for connecting the container nodes and the part nodes respectively, and generating feature representations of the container part edges; s5, respectively establishing part edges connected with any two part nodes, and generating feature representation of each part edge based on the size information of the corresponding two parts; and S6, inputting the heterogeneous graph containing the container nodes, the part nodes, the container part edges and the part edges into the trained discrimination model to obtain a discrimination result. Compared with the prior art, the method has the advantages that the discrimination efficiency can be greatly improved under the condition that the high discrimination accuracy is guaranteed, and the like.

The application discloses an operating system containerization kernel compatibility method based on shadow structure mapping, constructs a multi-dimensional mapping rule library by analyzing the difference between the data structure of a container system and a standard system, creates a shadow structure containing a data area, a metadata area and an extension area when the container system runs on a host system, allocates shadow memory space and a memory pool by the host system, establishes the mapping between the shadow structure and a physical memory page and the bidirectional mapping between the shadow structure and a host structure, intercepts the system call of a container application by the host system, obtains a target structure and allocates the shadow structure and the host structure memory, completes the conversion of the target structure to the host structure according to the multi-dimensional mapping rule library, completes the call by the host computer using the host structure, reversely converts the call result into the target structure and returns the call result to the container application, and realizes the seamless compatibility between the container system and the host system without modifying the user state program of the container system.

The invention provides a power Internet of Things gateway management method and device, a server and a storage medium, and relates to the technical field of computers. The method is applied to a container arrangement platform, and the container arrangement platform carries out independent containerization deployment on services and / or applications on power internet of things gateway equipment based on a container technology to obtain a plurality of containers. The container arrangement platform monitors the operation states of a plurality of containers; when it is monitored that the target container in the multiple containers is abnormal, credible restarting and / or credible repairing are / is conducted on the target container based on the credible platform module. By means of containerized deployment, application and / or service isolation is achieved, the safety of the system is improved, system modularization is achieved in a containerized mode, and the expansibility of the system and the utilization rate of hardware resources are improved. On the basis of containerized deployment, hardware-level security protection measures are realized by using the trusted platform module, so that the operating environment in the container is strictly controlled, the security is further improved, and the safe and stable operation of the Internet of Things gateway is ensured.

The invention discloses an abnormity monitoring method, system, equipment and medium based on a plant station gateway metering point, and relates to the technical field of gateway metering point abnormity detection, and the method comprises the steps: collecting the real-time data of the plant station gateway metering point through an industrial protocol interface, loading a protocol stack in a collection gateway, carrying out the preprocessing of the preliminarily collected data, and carrying out the detection of the abnormity of the gateway metering point; data transmission is carried out on the preprocessed data through a real-time stream processing layer by utilizing a transmission protocol, the real-time data is stored in a memory database, historical data is stored in a distributed file system, data organization and management are carried out, and multi-mode anomaly detection is carried out on the stored data through an anomaly detection service deployed in a containerization mode. And identifying various abnormities, packaging and outputting a detection result, and dynamically rendering through a responsive front-end interface to display the detection result in real time. According to the invention, the multi-mode intelligent detection and containerized elastic architecture are fused, so that the leap-type improvement of the metering abnormity of the plant station from passive alarm to active early warning and deep diagnosis is realized.

The utility model discloses an assembly type highway steel bridge component containerization approach bridge mixed loading container unit which comprises a cross beam, an approach bridge mixed loading hanging scaffold is embedded into the inner side of the cross beam, a central bridge deck is placed on the rear side of a container, and slab lapping corners are placed at the bottom ends of the container and the central bridge deck. Steel bridge components such as cross beams, transition slab corners, central bridge decks, transition slab supports, curbs, U-shaped bolt sets and bridge deck bolt hole dustproof caps are assembled together with auxiliary assembling tools to form a general component assembling unit required by steel bridge approach bridges with different spans, loading is completed within the same time, more than six persons are needed for one component, and only two persons are needed after assembling; single pieces are stacked and easily slide down to cause accidents during truck loading, and can be prevented after container loading; 40 minutes are needed for completing loading of a single piece, and 5 minutes are needed for loading after container loading; the number of single pieces is large, the number of point positions is large, omission is prone to occurring, and only one piece is packaged; a single piece needs to be loaded for multiple times, long-time labor is needed, and one-time loading is needed after container loading.

The invention relates to the technical field of artificial intelligence, and discloses a containerization-based AI model full life cycle management system and method, and the system comprises a data preprocessing module, an environment construction module, a flow arrangement module and a link control module. The method corresponds to the system. According to the containerization-based AI model full-life-cycle management system and method provided by the invention, the independent container mirror image of each stage is constructed, the multi-modal data is preprocessed, and automatic process arrangement and version and deployment management and control are carried out, so that the environment standardization isolation of each stage is realized to solve dependency conflicts, the multi-modal data is uniformly processed to avoid the format compatibility problem, and the management efficiency is improved. Human errors are reduced through full-process automatic cooperation, versions are traceable, the method adapts to various deployment environments, AI model management pain points are solved in a multi-dimensional mode, and development, iteration and large-scale landing of the AI model are efficiently supported.

The invention relates to the technical field of computers, and discloses an automatic execution method and system based on a containerized transformation tool, and the method comprises the steps: determining a target task operation tool and a corresponding container mirror image identifier according to the task operation content in a task operation request, building a mapping relation comparison table between an operation task and a corresponding container mirror image calling position, by accessing an execution machine resource pool management database, a task operation list of each execution machine is extracted, execution association information in an automatic execution time period is determined, and task operation is performed according to the execution association information and task operation association information in a task operation request. According to the method, an automatic execution strategy of each operation task is generated by considering storage space information of a container operation environment corresponding to each execution machine and a network condition of an application platform and each execution machine, and on the basis of meeting network requirements required by container mirror image pulling and container mirror image storage space limitation, an automatic execution strategy of each operation task is generated; and executing the running task by adopting the container mirror image of the latest version as far as possible.

The utility model relates to a kind of installation and fixing device of container radio frequency card, including base, sliding table, clamping mechanism and ejecting mechanism, the base is connected with sliding table, the back of sliding table is equipped with recess, recess is equipped with clamping mechanism, sliding table is connected with ejecting mechanism, the ejecting mechanism includes ejecting slide, ejecting hanging pole, locking slide and ejecting tension spring, the ejecting slide is supported on base, sliding table is slidably connected with ejecting slide, the sliding table moves axially forward and backward along base, the sliding table side edge is hung and is connected with ejecting tension spring, the rear end of ejecting hanging pole is inserted in base pivot hole, and its front end is slid along locking slide, the locking slide includes locking limiting recess and ejecting recess, the ejecting recess is lower than locking limiting recess and is in echelon, and constitute sliding table locking / ejecting mechanism.The utility model can realize the quick locking and ejecting of radio frequency card, solve the fixing problem of informatization label in containerization transportation.

Systems and methods for containerization are provided. Various embodiments provide containerization systems and methods deploying an initial container image associated with a startup script, a pre-service script and post-service script. This deployment includes executing the startup script associated with the initial container image, and the executing including copying configuration information for the initial container image into a volume and generating a marker file for the initial container image. The containerization systems and methods also deploying a main container image associated with a startup script. This deployment includes executing the startup script associated with the main container image and copying the configuration information from the volume to a local system. This deployment also includes executing the main container, including executing the pre-service script associated with the initial container image, configuring the main container, executing a main service, and executing the post-service script associated with the initial container image.