575 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.

A method of data processing is described. The method includes receiving, at a server host, a login request that includes a clear text password for an account. The method further includes retrieving, from a database, a payload stored in association with the account. The payload may indicate a hash value of a user configured password for the account, a hashing algorithm used for generation of the hash value, an initialization vector, and a key agreement public key associated with the account. The payload may be encrypted using a secret key derived from the key agreement public key and a private key provisioned to a key protection component of the server host. The method further includes transmitting a request that includes the payload and the clear text password. The method further includes receiving a response that indicates whether the clear text password corresponds to the user configured password.

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.

Methods, systems, and devices for data processing are described. A server host may receive a login request that includes a clear text password for an account associated with a tenant of a multi-tenant cloud platform. The server host may retrieve an encrypted payload associated with the account. The encrypted payload may include a hash value of a user-configured password for the account and an indication of a hashing algorithm used to transform the user-configured password into the hash value. The server host may obtain a wrapped symmetric key provisioned by a symmetric key distribution service. The server host may transmit a request that includes the encrypted payload, the clear text password, and the wrapped symmetric key. The server host may receive a response that indicates whether a hash value of the clear text password from the login request corresponds to the hash value of the user-configured password.

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.

The invention provides a power battery thermal runaway safety early warning method based on vehicle cloud cooperation. The method is mainly characterized in that a real-time estimated vehicle-mounted terminal and a cloud control platform of which the state is updated regularly work cooperatively, thermal runaway early warning is realized, and the method comprises the following steps: step 1, a vehicle end collects real vehicle data, extracts related characteristics through a sub-calculation interval matrix at set intervals, and uploads the related characteristics to a cloud end in real time; step 2, the cloud end receives the characteristics calculated by the vehicle end, calculates the health degree of each single battery by using a characteristic set, and divides fault types; and step 3, the fault information calculated by the cloud end is sent to the vehicle end in real time through the server, and the vehicle end gives an alarm and reminds a driver of timely maintenance. And the real-time performance of thermal runaway early warning is improved through a vehicle-cloud cooperation method.

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.

A host system is configured to upload data files to a cloud system and provide another layer of security to the access controls provided by the cloud system. The host system includes a communication interface for communicating with a storage device and a network interface for communicating with a the cloud system. The host system includes a processors configured to obtain a 2D barcode based on an identifier of the storage device and a network address of an authorizing device associated with an owner of the storage device. The processor is further configured to, responsive to a request to upload a data file to the cloud system, embed the 2D barcode into the data file (the 2D barcode configured to cause a client device attempting to access the data file to send an access request to the authorizing device) and transmit the data file to the cloud system.

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 cloud computing system is provided for testing a Distributed Energy Resource (DER) Management System (DERMS) that manages operation of DERs connected to a power system. The cloud system implements a DERMS testing platform on virtualized cloud computing resources of the system. The platform tests the DERMS and is modularized. In some embodiments, the DERMS is modularized into at least a simulation module and a communication module, where the simulation module is configured to simulate DER(s) of power system and the communication module communicatively interfaces DERMS with DER(s) via communication network(s). The simulation module also simulates the power system under test conditions as the DERMS manages operation of DER(s) via the communication module. The cloud computing system exposes the platform as a service for testing the DERMS under test conditions. The cloud system scales the resources allocated to the platform with the number of DER(s) simulated under different test conditions.

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.

The invention discloses a simulation-based vehicle cloud function test system, which comprises an evaluation layer, a service layer, a communication layer and a test layer, and comprehensively covers test requirements, scene design, service simulation and communication actual measurement from the evaluation layer to the test layer by constructing a multi-layer simulation test environment. The comprehensiveness and efficiency of the vehicle cloud function test are greatly improved, the environment can accurately simulate the complex real world condition, and the effectiveness of the test result is ensured. The specific cloud platform service simulation module design enables the technical scheme of the application to deeply test various service functions of the cloud platform, such as data processing and decision issuing, so that the blank of lack of cloud platform simulation in the traditional test is filled, and the verification level of the overall performance of the vehicle cloud system is improved.

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 present disclosure relates to a method, an apparatus, a system, and a computer program for function notification of a function accelerator card in a cloud system and, more particularly, to a method performed by a control server of a cloud system, an apparatus, a system, and a computer program, wherein the method includes: receiving, from a function accelerator card included in at least one unit task server of the cloud system, a list of acceleration functions that are capable of being performed by the function accelerator card; and assigning a first task to the function accelerator card in consideration of the acceleration function list. According to the present disclosure, a list of acceleration functions of a functional acceleration card included in a unit task server of a cloud system may be notified to a control server, thereby enabling efficient distribution of the workload and improving the overall performance of the cloud system.

The embodiment of the invention provides a proprietary cloud system, a data packet sending method, equipment, a product and a storage medium, the proprietary cloud system comprises a virtual proprietary network, a service instance and a common instance run in the virtual proprietary network, and a service cluster is pre-configured with a cluster virtual IP address; an application process runs in the common instance, and the application process is used for generating a data packet; the method is applied to a configuration process operated in a common instance, and comprises the following steps: obtaining configuration information which comprises a cluster virtual IP address and a real IP address of a service instance in a service cluster; based on the configuration information, determining a local real IP address of a local service instance located in the same area as the process; if the local real IP address can be connected, a local service conversion rule is generated and provided for the operating system of the common instance, and the local service conversion rule is used for indicating the operating system to convert the destination address in the data packet into the local real IP address.