365 results about "Cloud systems" patented technology

An AI-driven, cloud-based system (100) for real-time biomedical and pharmaceutical compliance and risk management, including: (a) a compliance knowledge module configured to ingest, interpret and structure regulatory data using natural language processing (NLP) and generate machine-readable compliance rules; (b) a real-time monitoring and event recording module configured to collect and normalise operational data from distributed biomedical and pharmaceutical systems, including laboratory information management systems (LIMS), manufacturing execution systems (MES) and IoT-enabled devices; (c) an intelligent risk assessment and prediction module configured to correlate operational data with compliance rules, calculate dynamic risk scores and predict potential compliance violations using machine learning models; (d) an automated policy and workflow enforcement module configured to initiate remedial actions, assign tasks and log activities based on predefined standard operating procedures (SOPs); (e) an audit readiness and reporting module configured to generate compliance logs, audit trails and standardised regulatory reports in real time; and (f) an adaptive learning and feedback optimization module configured to refine rule sets and predictive models based on feedback, historical data and regulatory updates; g) the modules are integrated into a cloud infrastructure to enable real-time, scalable and predictive compliance and risk management across biomedical and pharmaceutical processes.

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.

The invention relates to the technical field of cloud computing and edge computing, and discloses a low-delay and high-reliability distributed integrated optimization method for a cloud collaborative computer system, which comprises the following steps of: S1, acquiring node state data of a plurality of collaborative nodes, processing the node state data based on a reinforcement learning model, and generating a task allocation strategy and a data routing strategy; s2, receiving local resource optimization parameters generated by each cooperative node based on federated learning, and aggregating the local resource optimization parameters to generate a global resource allocation strategy; and S3, obtaining a task allocation log and a resource scheduling log recorded by the block chain network, and verifying execution states of task allocation and resource scheduling based on the smart contract. According to the invention, the DQN, FedProx and block chain technologies are integrated, the cooperation of task allocation, resource optimization and fault self-healing is realized, the performance of the cloud system is optimized, and the problems of task allocation and resource scheduling separation and low fault recovery efficiency of a traditional cloud system are solved.

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 method is provided for controlling secure access to data in hybrid-cloud environments. A trusted authorisation server controls access to data served by a resource server to client devices in an untrusted environment, to ensure client devices are only able to access data that they are permitted to access. In the present techniques, the trusted authorisation server checks the validity of data access requests received from client devices prior to any such requests being received by the resource server or an authorisation server associated with the resource server. This is advantageous because in many existing hybrid-cloud systems, the resource server and authorisation server are unable to check the individual validity of requests received from client devices. This is particularly problematic for multi-tenant resource servers. The present techniques provide a more secure way of controlling access to data stored and served by cloud-based, off-premises / third party resource servers.

The invention discloses a time sequence prediction and capacity bottleneck early warning method based on deep learning, and relates to a cloud system and a data processing technology, and the method comprises the steps: obtaining static equipment attribute data and a dynamic index of the cloud system; constructing a resource topological map, and establishing a terrain-like map covering each cloud system; dividing a normal equipment group and a capacity bottleneck associated equipment group based on the terrain-like elevation in the terrain-like map; respectively extracting topographic index features of the associated equipment group and the normal equipment group; utilizing parallel LSTM prediction channels to respectively predict the extracted topographic index features, and dynamically fusing the output of the dual prediction channels; and comparing a fusion output result with a preset capacity threshold value so as to execute dynamic capacity bottleneck early warning in the terrain-like map. According to the method, the service calling chain, the data flow direction path and the equipment association state are displayed in a three-dimensional mode in combination with the visualization technology, and multi-node capacity bottleneck early warning is achieved.

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.

The present application provides a task processing method, which can be applied to a cloud system. The cloud system can acquire a directed acyclic graph (DAG) used for describing data processing tasks, wherein nodes in the DAG are used for representing the tasks; and divide the plurality of nodes into at least two task groups, and then process each task group, wherein the number of tasks in each task group is not greater than N, and N is determined by means of the size of machine memory, the size of memory occupied by instances, and the size of memory occupied by output data of the tasks. The in-degree of nodes associated with tasks in each task group is 0, indicating that tasks in a same task group are independent of each other and no intermediate data needs to be generated, so that the usage of the machine memory can be reduced; the number of tasks in a task group is not greater than N, so that the volume of output data of the tasks can be limited, thereby reducing the memory occupied by the output data of the tasks; tasks in a same task group can be processed in parallel, improving the efficiency of task processing.

Anomaly detection in cloud-based systems involves predicting identity behavior using historical activity data. Historical activities and their timestamps are analyzed to determine future intervals when activity is expected. Predictions are generated using weighted historical data emphasizing recent activity, and an anomaly score quantifying risk is calculated for each future interval based on deviation from expected behavior. Inline monitoring may detect and alert administrators or trigger automated responses to unexpected identity behavior. The method includes confidence scoring based on historical validation, visualization via graphical user interfaces, and lightweight, scalable computations suitable for monitoring extensive cloud deployments, enhancing both precision and efficiency in detecting suspicious cloud activity.

A product delivery system includes a product transport vehicle having at least a plurality of tank compartments, internal valves, control valves, fluid property sensors, temperature sensors, overfill sensors, pressure sensors, a system controller, and a memory module. The product delivery system may be operable to communicatively connect with the loading system at a loading station, a fleet management system, or a cloud system to obtain information, such as the amount of liquid product to be loaded into a tank compartment. The product delivery system may be operable to compare the volumes of the tank compartments with the amounts of liquid product to be loaded at a loading station and maintain the internal valve or control valve in a locked state or transition the internal valve or control valve to an unlocked state based on the comparison. Other comparisons based on liquid type or distribution tank volume can also be made.

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.

The invention provides a task processing method, which can be applied to a cloud system, and the cloud system can obtain a directed acyclic graph (DAG) for describing a data processing task; nodes in the directed acyclic graph are used for representing tasks; dividing the plurality of nodes into at least two task groups, and then processing each task group; wherein the number of the tasks in each task group is not greater than N, and N is determined according to the size of a machine memory, the size of a memory occupied by an instance and the size of a memory occupied by output data of the tasks; the in-degree of the nodes associated with the tasks in each task group is 0, so that the tasks in the same task group do not depend on one another, intermediate data does not need to be generated, and the occupation of a machine memory can be reduced; the number of the tasks in the task group is not greater than N, the data volume of the output data of the tasks can be limited, so that the occupation of the output data of the tasks on a memory is reduced, the tasks in the same task group can be processed in parallel, and the task processing efficiency is improved.

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.

Systems and methods for active directory protection in zero trust networks. In an embodiment, steps include performing inline monitoring of traffic associated with a cloud-based system; detecting one or more active directory protocols based on the inline monitoring; classifying the traffic as being associated with any of the one or more active directory protocols; inspecting the traffic associated with the one or more detected active directory protocols; and generating one or more active directory logs based on the inspecting and classifying.

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

The present invention provides a database access method and related devices based on an edge-cloud-end system. The method includes constructing an edge-cloud-end system of a terminal, an edge gateway, and a cloud server; deploying an enterprise metadata knowledge base, an AI model, and an enterprise database in the edge-cloud-end system based on customer requirements; receiving a demand prompt word input by a user through an intelligent agent in the terminal; obtaining a routing policy through the intelligent agent based on the deployment scenarios of the enterprise metadata knowledge base, the AI model, and the enterprise database and with the principle of giving priority to local gateway processing; and programmatically processing the demand prompt word based on the routing processing policy, the edge gateway, and the cloud server. The database access method of the present invention borrows an AI model for code generation, reduces the development difficulty, and the enterprise metadata knowledge base, the AI model, and the enterprise database are flexibly deployed based on customer requirements, and the terminal intelligent agent can flexibly route. The present invention has better adaptability and can better meet the enterprise requirements.

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 network system for providing one or more services to one or more end-user devices communicatively coupled to the network system over a wireless access network. The network system includes a policy enforcement function, a policy element, and a network element communicatively coupled to the policy enforcement function and the policy element, and configured to communicate policy information between the policy enforcement function and the policy element, the policy element includes a virtual policy element instance or thread that executes in a policy element cloud system, and the network element includes a load balancer configured to select or assign the virtual policy element instance or thread for communication of the policy information.

Techniques are disclosed for enabling dynamic routing of identity and access (IA) requests to a cloud identity and access management (IAM) system. In some embodiments, the health (or service quality) of a cloud IAM system is continuously monitored in the background to update routing information for dynamically routing IA requests for client cloud services to the cloud IAM system in the foreground. In some embodiments, background monitoring is performed by sending first-type IA requests to the cloud IAM system to discover traffic patterns. In some embodiments, routing information may be mappings between client cloud services and identity endpoints of service cells in the cloud IAM system with configurable attributes that help rebalance and distribute the IA traffic.

Aspects include bandwidth reduction in a dental cloud system. By receiving input data representing multiple depth images, partial reconstruction on overlapping images is performed, generating a composite image with significantly reduced data size relative to the input data. This process involves associative operations on depth images, selecting suitable images based on temporal or spatial adjacency, and preserving weight sums for optimal combination. The reconstructed model is transmitted to a remote server for final reconstruction, minimizing required bandwidth. The system integrates low-resolution depth images for swift user feedback and performs global optimization for long-span accuracy. The disclosed approach improves efficiency in dental imaging by reducing data transmission burdens without compromising model accuracy, offering a streamlined approach for cloud-based dental applications.

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.

A computer-implemented method for secure policy distribution to a cloud system. The method includes defining an access policy for a set of resources on a cloud computing system, where the access polic

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 invention provides a private cloud dynamic permission generation system and method, and relates to the field of cloud computing permission management. According to the method, a ternary graph structure is constructed to represent the relationship among tenants, resources and contexts; reverse deduction of a legal access path is realized through a graph search algorithm (such as DFS); automatically generating a permission set based on the path result; authority merging, minimum authorization and signature verification mechanisms are realized; the method can be applied to cloud systems. According to the method, the problems of permission mixing, configuration lag and fine-grained access control among tenants are effectively solved, and the dynamic property, the safety and the expandability of permission management are improved.

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.