437 results about "Cloud systems" patented technology

Systems and methods for unsupervised multi-modal causal structure learning for root cause analysis. System logs of a cloud system can be transformed to time-series data using a log-tailored language model to obtain system log features of the cloud system. A metric causal graph and a log causal graph can be predicted from modality-specific representations and modality-invariant representations of extracted system metric features and system log features, respectively, using the deep neural network. The metric causal graph and log causal graph can be fused to obtain a fused causal graph. Root causes of system failure can be flagged for system maintenance based on ranked entities obtained from the fused causal graph to obtain flagged root causes. System maintenance can be performed autonomously based on the flagged root causes from identified system entities to optimize the cloud system with an updated configuration.

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.

The invention provides a container redundancy scheduling method and system based on a dynamic load, and relates to the technical field of container redundancy scheduling. According to the method and the system, the load change of the industrial edge cloud system is sensed in real time through a comprehensive design scheme of server redundancy, minimum deployable computing unit soft redundancy and application of an anti-affinity cross multi-copy mechanism and a multi-stage heartbeat detection mechanism in a container, and a scheduling strategy of a voting mechanism is combined; and dynamically adjusting the redundant deployment state of the container. For containerized application in an industrial edge cloud environment, dynamic load sensing, real-time fault detection and intelligent resource scheduling are realized, and the requirements of an industrial scene for low time delay and high reliability are met.

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.

Embodiments described herein provide methods and apparatuses for selecting Nnext Fault Management Models, FMMs, where Nnext is an integer value, to run at an edge site in a cloud system during an ith time period. A method in a Fault Management System, FMS includes: in a deployment agent, DA: selecting the Nnext FMMs from a first set of FMMs based at least in part on respective probability functions associated with each of the first set of FMMs, wherein each respective probability function indicates how likely a fault associated with the respective FMM is to occur as a function of time; and initiating running of the Nnext FMMs at the edge site during the ith time period.

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.

A system of a vehicle for processing communication with connected objects is provided. The processing by the system uses a processor of the vehicle and processing and data from one or more servers of a cloud system. The connected objects include objects having communication capabilities that are either fixed at specific locations as infrastructure proximate to where the vehicle is located or locations proximate to where the vehicle is moving. The system includes a global positioning system of the vehicle for generating geo-location for the vehicle and a heading direction. The geo-location is shared with a server of the cloud system using a communications device of the vehicle. The server of the cloud system is configured to receive the geo-location of the vehicle to identify the heading direction of the vehicle. The communications device of the vehicle is configured receive state data from at least one of said connected objects.

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.

The invention provides a database access method based on an end-side cloud system and related equipment. The method comprises the following steps: constructing the end-side cloud 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 end-side cloud system based on customer requirements; receiving a demand prompt word input by a user through an intelligent agent in the terminal; a routing strategy is obtained through an intelligent agent based on deployment situations of an enterprise metadata knowledge base, an AI model and an enterprise database and by taking local gateway processing priority as a criterion; and based on the routing processing strategy, the edge gateway and the cloud server carry out programmed processing on the demand cue word. According to the database access method, the AI model is used for code generation, the development difficulty is reduced, the enterprise metadata knowledge base, the AI model and the enterprise database are flexibly deployed based on customer requirements, the terminal intelligent agent can flexibly route, and the database access method has better adaptability and can better meet the enterprise requirements.

Embodiments of the application may provide a decentralized wireless cloud service system including a distributed ledger database, a consensus algorithm, an exchange wallet, a platform, a distributed cloud, software configured to integrate the exchange wallet, the platform, and even the distributed cloud into a distributed ledger database to create a decentralized wireless cloud system, resource nodes in a decentralized wireless cloud system configured to provide a resource on a decentralized wireless cloud system, wherein a resource may include a network resource from at least some of the resource nodes; and perhaps even a reward system to owners of said resource nodes for a resource.

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 discloses an end-side cloud system-oriented multi-task joint scheduling method, which comprises the following steps of: firstly, constructing an end-side cloud system multi-task joint scheduling model on the basis of uniformly describing nodes and tasks of an end-side cloud system; secondly, by taking maximization of the expected benefit of the multi-task joint scheduling as an optimization target, establishing an optimization problem solving constraint, and realizing construction of the optimization problem of the multi-task joint scheduling of the end-side cloud system; and finally, a mode of combining a heuristic strategy and nonlinear optimization solution is adopted to realize step-by-step decision of task execution nodes and task allocation resource quantity, and final generation of a multi-task joint scheduling scheme of the end-side cloud system is completed. According to the method, concurrent computation-intensive task joint scheduling highly suitable for the end-side cloud system can be realized from three aspects of multi-task joint scheduling model construction, multi-task joint scheduling optimization problem establishment and multi-task joint scheduling scheme generation.

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.

A cloud system includes a computing node, configured to insert a network interface card and run a first virtual instance, where the network interface card is configured to receive a first packet in a first data flow; a network controller configured to send tenant network information of at least one virtual instance on one or more VPCs to a first central node, where the network interface card is further configured to send the first packet to the first central node; and the first central node, configured to generate the first packet processing rule corresponding to the first data flow, where the network interface card is further configured to receive and locally record the first packet processing rule from the first central node in order to process and / or forward a packet after the first packet in the first data flow.

Methods, systems, and computer-readable storage media for receiving a request through a web services API, the request comprising a query to query a database system, retrieving a set of weights that is specific to the web services, determining a factor score for each impact factor in a set of impact factors to provide a set of factor scores, providing a score total for the query based on the set of weights and the set of factor scores, returning a score response including the total score and at least one query suggestion, and receiving a modified request through the web services API, the modified request including the query modified to include at least a portion of the at least one query suggestion.

The invention discloses a vehicle cloud system, a battery pack data sampling method and an abnormal vehicle end determination method. The vehicle cloud system comprises a vehicle end and a cloud end. The vehicle end is used for sampling voltage data and current data of the battery pack in real time to obtain data sampling points; determining the data sampling points meeting a preset condition as effective sampling points, and transmitting the effective sampling points and the corresponding timestamps to the cloud in an associated manner; the cloud end is used for calculating a battery pack SOC value corresponding to the effective sampling point; according to the timestamp, the battery pack SOC value and a preset extreme value, effective sampling points in a preset time window range are selected, an effective sampling point set is obtained, and the battery pack SOC value extreme value of the effective sampling point set is larger than the preset extreme value; calculating and obtaining battery cell consistency parameters of the battery pack according to the effective sampling point set; and determining the vehicle end with abnormal battery cell consistency as an abnormal vehicle end according to the battery cell consistency parameter. According to the application, the accuracy of judging the consistency of the battery cells can be improved, and the network flow cost and the data storage cost are reduced.

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