258 results about "Cloud systems" patented technology

The invention discloses a multi-level cache optimization method and system based on data popularity self-adaptive adjustment, relates to the technical field of data storage, and aims to solve the problems that a cache mechanism in an existing desktop cloud system is static, initialization is slow, and cold and hot data are difficult to recognize. The adopted scheme comprises the steps that a three-level cache structure of a local cache L1, a distributed cache L2 and a database L3 is set; constructing a data popularity scoring model based on the access frequency, the time decay and the user priority; dividing the data into hot data, temperature data and cold data according to the popularity score, and dynamically determining migration and elimination strategies of the data in L1, L2 and L3; the hot data are loaded to the upper-level cache T in advance through a prediction mechanism, and container-level cache preheating is supported; a cache preheating strategy is integrated to a Kubernetes life cycle, and hot data loading and cold data degradation are achieved; visual strategy configuration and monitoring during operation are supported, and cache strategies are managed in real time. The method is suitable for an access optimization scene of a large-scale desktop cloud system.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

Systems and methods to securely retrieve, process, and archive (e.g., via cloud systems) vehicle events, vehicle status, vehicle owner data, and historical data. The system provides real-time, or event-based, vehicle data acquisition to facilitate verifiable and accurate information. True and accurate vehicle data can be accessed by various companies, entities, and government agencies under a subscription service, or on a per-event basis.

Techniques for a centralized vulnerability security scanning and distributed detection system are disclosed. Some techniques set forth a set of operations including receiving, in a first cloud environment of a cloud system, image scan results from a second cloud environment of the cloud system, receiving container identity data from a particular deployed container of a set of deployed containers in the first cloud environment, based on a comparison between the image scan results and the container identity data, determining that the particular deployed container of the set of deployed containers is running a vulnerable software product, and generating, for presentation on a graphic user interface (GUI), information associated with the vulnerable software product. The image scan results correspond to a vulnerability scan of a plurality of software products running in the set of deployed containers.

Systems and methods for a context aware Artificial Intelligence (AI) assistant for troubleshooting network issues includes operating an Artificial Intelligence (AI) agent system that includes an agent core connected to memory, one or more tools, and a planner; receiving a request from a user; and generating, via the AI agent, an answer to the request using a plurality of inputs related to user experience of one or more users associated with a tenant of a cloud-based system.

The invention relates to a different intelligence traffic subject interaction information model construction method based on a three-dimensional digital model. The method comprises the following steps: S1, constructing a multi-level scene based on spatial levels of a road traffic system and designing functions of the multi-level scene; s2, defining autonomous levels of the traffic system, and describing the perception capability, decision logic and execution precision of each terminal device of the vehicle road cloud of each level; s3, according to different attributes of traffic entities, designing a perception-transmission-decision-control four-stage traffic information circulation process and realizing unambiguous collaboration among different intelligence subjects; and S4, converting the standardized and defined information model into a plurality of instantiation units, and deploying the instantiation units into computing entities of various end-edge-cloud traffic subjects to support actual operation of a traffic service scene. According to the method, the problems of difficult cross-domain collaboration, non-uniform information models and insufficient global optimization caused by non-uniform intelligent level of traffic subjects in the prior art are effectively solved, and methodological support is provided for design, verification and implementation of a vehicle-road cloud integrated system.

The invention is suitable for the technical field of computers, and provides a training method and device of a detection model and a service quality detection method and device.The training method of the detection model comprises the steps that a first feature data set is obtained, and the first feature data set comprises multiple first input features and corresponding service quality; taking all the first input features and the corresponding service quality as a model input sample and a model output sample respectively, and training the detection model to obtain an intermediate detection model; the intermediate detection model is used for learning effective features related to the service quality in the first input features; determining a second feature data set based on the first feature data set and an intermediate detection model; and training the intermediate detection model based on the second feature data set to obtain a target detection model. Through the training method, the target detection model accurately predicts the service quality of the monitoring data, and cluster resource scheduling in the cloud system is ensured to be matched with the service quality demand.

A mobile robot system is described in having a mobile robot and cloud system. The mobile robot leverages cloud computing to offload Neural Radiance Fields (NeRF) based 3D scene reconstruction. The mobile robot advantageously adopts techniques for view filtering and next-best view selection that optimize the image collection process necessary for training an NeRF model with the cloud system. These techniques enable the mobile robot to discard redundant images that do not provide significant new information about the environment. Additionally, these techniques enable the mobile robot to strategically select next-best views that maximize the information gain, while minimizing a total number of images required and the time required to capture the images. These techniques provide a significant reduction in the overall bandwidth required for providing image data to the cloud system and can result in a more accurate and higher quality 3D reconstruction of the environment.

To provide a radio communication system and a communication method capable of safely relaying communication between a radio station connected to the Internet network and a radio station connected to a closed network.SOLUTION: The closed area system 11 and the cloud system 21 included in the wireless communication system 1 include a closed area side gateway server 13 and a cloud side gateway server 22. When receiving an authentication request from a radio station 41,42, a closed area side gateway server 13 and a cloud side gateway server 22 store activation information including unique information of the radio station 41,42 and generation information of an encryption key in a storage device 27. Regardless of whether the wireless station 41,42 is connected to the closed network 10 or the Internet network 20, the communication between the wireless station 41,42 and the closed system 11 or the cloud system 21 is protected by the encryption key generated based on the generation information included in the activation information.SELECTED DRAWING: Figure 1

Systems and methods for intelligent network incident management, Root Cause Analysis (RCA), and automated remediation include receiving metrics, graphs, and historic logs associated with network performance of a tenant of the cloud system; identifying a network issue based on the received metrics, graphs, and historic logs; performing an automated RCA to determine a cause of the network issue; and remediating the identified network issue based on the determined cause. Various embodiments include training specialized Large Language Models (LLMs) for performing the automated incident identification, RCA, and remediation.

A method of generating and applying combining weights to Physical Uplink Shared Channel (PUSCH) symbols for Open Radio Access Network (O-RAN) fronthaul interface between O-RAN radio unit (O-RU) and O-RAN distributed unit (O-DU) includes: calculating, by the O-DU, a codebook; sending, by the O-DU, the codebook to O-RU; generating, by the O-RU, an RU combiner matrix, using the codebook; applying, by the O-RU, the RU combiner matrix to In-phase and Quadrature (IQ) data to generate compressed IQ data; sending, by the O-RU, the compressed IQ data to O-DU; calculating, by the O-DU, a DU combiner matrix from compressed demodulation reference signal (DMRS); and applying, by the O-DU, the DU combiner matrix to data.

The application provides a resource scheduling method and device, electronic equipment, storage medium and edge cloud system. The resource scheduling method comprises: in the case of identifying the resource requirement of a target workload, determining whether there is an edge node in the edge cloud cluster that meets the resource requirement; in the case that there is an edge node in the edge cloud cluster that meets the resource requirement, deploying the target workload on the edge node that meets the resource requirement; in the case that there is no edge node in the edge cloud cluster that meets the resource requirement at a specified time, or according to at least one of the resource requirement, the resource state of the edge cloud cluster, the running monitoring information of the edge cloud cluster and the current traffic corresponding to the target workload, performing global scheduling on the load deployment state of the edge cloud cluster. The technical scheme of the application can improve the resource utilization rate of the edge cloud cluster.

The application provides a data transmission method and device, electronic equipment and storage medium, which are applied to a cloud system. The method comprises the following steps: in response to a transmission request of first data, the first data is received, wherein the cloud system is deployed in a local area network; in response to the received first data, the first data is encrypted to obtain second data; in response to a data sending request, the second data is decrypted, and third data is generated based on the decrypted second data; and the third data is sent to a transmission target. The application can improve the security of data transmission.

The invention relates to the field of intelligent network connection automobiles, in particular to a high-precision time synchronization method based on a vehicle and road cloud cross-domain complex system, and the method comprises the steps: configuring clock source priorities and synchronization protocol parameters of all nodes in a vehicle and road cloud system; synchronizing cloud platform time according to the satellite signal and the public NTP server; according to the satellite signal and the cloud platform time, synchronizing the vehicle end time through a gPTP protocol; performing time synchronization on the roadside equipment according to the NTP protocol and the cloud platform; monitoring and processing time synchronization abnormity of multiple nodes in the vehicle and road cloud system; and the synchronization precision is verified based on timestamp comparison, and automatic calibration is carried out when a threshold value is exceeded. According to the method, the full-link high-precision time synchronization precision of the cross-domain complex system is effectively improved.

The invention discloses a cloud manufacturing optimization system for a linear cutting workshop. The cloud manufacturing optimization system comprises a user layer, a service center layer, a scheduling entity layer and a user feedback layer, the user layer is provided with a cloud terminal and is used for exchanging data with the service center layer; the business center layer comprises a model center, a data center, a search center, a computing center and a planning center; the scheduling entity layer comprises an enterprise level, a workshop level and a production line level, three levels of automatic scheduling systems exchange data with the service center layer respectively, and distributed automatic scheduling systems are arranged in the three levels of automatic scheduling systems respectively; the user feedback layer feeds back the work summarization condition to the user layer; according to the method, a cloud system of a whole wire cutting product is integrally redesigned by utilizing a new computing mode, a business mode and an application mode of cloud computing according to a design concept of'cloud centralized management of discrete manufacturing resources and on-demand service of overall planning of cloud resources.

The application discloses an unmanned aerial vehicle intelligent sensing and intention reasoning system and device, which comprises image acquisition, target detection, knowledge graph, multi-modal large model, airborne computing power and edge-cloud cooperation module. The system is guided by target detection, enhances scene knowledge through the knowledge graph, improves the analysis ability of the multi-modal large model, and realizes the intelligent monitoring of the unmanned aerial vehicle based on the RK3588 platform. Real-time monitoring is realized by using the maneuverability of the unmanned aerial vehicle, which can not only analyze the scene, but also infer and predict the behavior intention and situation, thereby realizing early warning. The local and cloud systems are equipped with a historical database, a visual interface and a large parameter model, and the returned information is deeply analyzed. The system can be applied to the scenes of border patrol, city security, animal protection and the like.

A system and method for analyzing screen-recorded personalized digital content using on-device computer vision and generative AI. A user captures content from a recommender system interface, such as screen activity or browser-rendered content, optionally with concurrent voice commentary. On-device processing generates intermediate representations using CLIP-style embeddings, OCR text, and transcribed audio tokens, flagging advertisements, changes in content, diversity in content, and similarities in content. A compact generative AI model produces metadata summaries, which users may annotate with tags or comments. A composite metadata package is transmitted to a cloud system, while raw media is deleted. The invention enables privacy-preserving, bandwidth-efficient insight into recommender-based media and user feedback.

A system includes a key service, that is configured to authenticate requests from an external manufacturing system (“EMS”), receive a request for generating a keyset, generate the keyset, send the keyset to the EMS, and send the keyset to a persistent cloud storage system, wherein an external cloud system accesses the keyset for use in secure communication with a hardware device.

The embodiment of the invention provides a hypergraph-based resource allocation method and system in a hybrid cloud management platform. The method is applied to the technical field of cloud computing, and comprises the following steps: performing virtual extension on a matched individual by adopting an F matching algorithm according to a service request, and performing one-to-one matching conversion; on the basis of the individuals subjected to one-to-one matching conversion, constructing an optimal distribution model by adopting a hypergraph theory; and solving the optimal allocation model by adopting a heuristic algorithm of local search to obtain a resource allocation scheme. In this way, virtual machine resources can be effectively allocated, and the cloud system can achieve the maximum performance.

This application discloses a logical node management method and cloud system based on cloud computing technology, belonging to the field of cloud service technology. The method includes: a cloud management platform creating a target logical node in the infrastructure, wherein the target logical node includes a virtual CPU, a first virtual extended processor, and a virtual bus network, the virtual CPU and the first virtual extended processor being logically connected to the virtual bus network respectively; if the cloud management platform confirms that at least one first extended processor in the extended processor device pool has failed, it unloads the first virtual extended processor from the target logical node; the cloud management platform then attaches a second virtual extended processor to the target logical node, the second virtual extended processor implementing all or part of the functions of at least one second extended processor in the extended processor device pool, the second virtual extended processor being logically connected to the virtual bus network. This application improves the utilization rate of cloud resources.

Embodiments of the present application provide an edge resource expansion method, an edge cloud system, a device and a storage medium. In the embodiments of the present application, a center service node in a center cloud can request a server producer to produce a target server based on an edge resource expansion request of a target user; and install an operating system on the target server and deploy a cloud service; then, a logistics transportation party can be instructed to transport the target server to a target edge cluster corresponding to the target user, so as to deploy the target server in the target edge cluster and expand an edge node in the target edge cluster. In the embodiments, a whole-link expansion process integrating the user's edge resource expansion request, server production, transportation and edge cluster deployment is implemented, and the edge node expansion is integrated and end-to-end edge node expansion is implemented.

This invention proposes a dynamic permission generation system and method for private clouds, relating to the field of cloud computing permission management. The method constructs a ternary graph structure to represent the relationships between tenants, resources, and context; it uses graph search algorithms (such as Depth-First Search) to reverse-engineer legitimate access paths; it automatically generates permission sets based on the path results; and it implements permission merging, least privilege, and signature verification mechanisms; enabling its application in cloud systems. This invention effectively solves the problems of mixed permission usage among tenants, configuration lag, and fine-grained access control, improving the dynamism, security, and scalability of permission management.

The invention relates to the technical field of remote disaster recovery, and particularly discloses a resource dynamic scheduling system and method for a remote disaster recovery cloud system, and the system comprises a historical data analysis module which is used for analyzing the historical data based on the historical tracing data of a plurality of system state indexes of a system to be subjected to disaster recovery, generating a multi-dimensional static state matrix and a multi-dimensional dynamic action matrix of the system to be subjected to disaster recovery; the fluctuation risk determination module is used for predicting the multi-dimensional state fluctuation risk of the to-be-subjected-to-disaster-tolerant system in a future scheduling period based on the multi-dimensional static state matrix and the multi-dimensional dynamic action matrix of the to-be-subjected-to-disaster-tolerant system; the backup path adjustment module is used for obtaining a resource dynamic scheduling result of the remote disaster recovery cloud system based on the multi-dimensional state fluctuation risk of the system to be subjected to disaster recovery in the future scheduling period and the real-time load states of all remote disaster recovery backup nodes under all levels in the remote disaster recovery cloud system; therefore, reasonable resource allocation during remote disaster recovery backup is realized, and the resource utilization efficiency is improved.

The utility model discloses terminal equipment of a DHR system based on radio frequency identification, which comprises an electronic tag provided with an antenna and a first inductor, and the antenna and the first inductor are connected to form a first induction loop; wherein a first capacitor is also arranged in the first induction loop; the reader-writer is in signal connection with the antenna; the terminal machine is in signal connection with the reader-writer, and the output end of the reader-writer is in signal connection with the input end of the terminal machine; the terminal machine is also provided with a first communication module; and the display device is in signal connection with the first communication module. According to the utility model, through a radio frequency reading technology, the device can rapidly identify information of employees or articles and upload the information to a cloud system in real time. According to the rapid and automatic information processing mode, the manual input requirement and the related error probability are greatly reduced, and the data processing efficiency and accuracy are improved.

A hybrid AI cloud system includes at least one distributed node configured to execute an artificial intelligence (AI) service, and a hybrid server configured to monitor the workload of a first distributed node configured to execute the AI service, and prepare in advance at least one second distributed node configured to assist or replace the first distributed node according to the result of the monitoring. The second distributed node is selected from a previously prepared distributed node pool including distributed nodes of the hybrid AI cloud system, or from a distributed node of an external cloud system. Through this, if there is a shortage of distributed nodes capable of executing AI services in the distributed cloud, distributed nodes included in a centralized cloud can be utilized, thereby achieving the advantages of both the distributed cloud and the centralized cloud.

The utility model relates to the technical field of micro-grids, in particular to a micro-grid coordination controller based on an MQTT protocol, which comprises a processing module comprising a central processing unit electrically connected with an input and output module; the input module comprises a serial port module and a network port module, the serial port module adopts an RS485 mode to establish communication with a field device, the network port module is connected with a field network communication device, and the serial port module and the network port module transmit read data to the central processing unit; the output module comprises a cloud system, and the cloud system establishes two-way communication with the central processing unit through a wireless transmission module; wherein the central processing unit converts data acquired from the input module into an MQTT protocol data form, and sends the MQTT protocol data form to the cloud system. According to the utility model, the communication architecture in the micro-grid is greatly simplified, the complexity and cost of the system are reduced, and the intellectualization and adaptive capability of the micro-grid are enhanced, so that the bandwidth utilization rate is high and the delay is small.

The invention discloses a resource adjusting method and device and a storage medium, and belongs to the field of cloud computing. The method comprises the following steps: acquiring the number of resources used by a first user; when the number of resources used by the first user reaches an upper limit value of a first resource quota range corresponding to the first user and the number of idle resources of the first tenant is not less than a first threshold value, a third resource is added to the resources used by the first user, and the idle resources of the first tenant comprise the third resource; when the number of idle resources of the first tenant is smaller than a first threshold value, resources are reduced from the increased resources used by the first user, and the reduced resources used by the first user at least comprise the first resources. The resource utilization rate of the cloud system can be improved.