38 results about "Cloud computing architecture" patented technology

The invention provides a building industry finance and tax compliance digital evidence chain integrated platform which is deployed based on a cloud computing architecture and comprises a distributed server cluster, a network communication module and a database storage system. By constructing the integrated digital platform, fundamental efficiency improvement and risk management and control reinforcement of finance and taxation management in the building industry are realized, the platform enables a data acquisition process to be highly automatic, manual repeated entry work is greatly eliminated, financial personnel are liberated from tedious account checking, and the financial personnel are greatly improved. The compliance check module based on a rule engine and artificial intelligence can perform real-time and comprehensive automatic analysis on all transaction data, significantly improves the identification accuracy and timeliness of potential compliance risks, realizes instant alarm, converts passive risk response into active risk response, and improves the risk handling efficiency. Compared with a traditional manual mode, the data processing speed is improved in order of magnitude, the monthly checkout and report generation time is greatly shortened, the enterprise decision cycle is remarkably accelerated, and the overall operation efficiency is improved.

The invention provides an intelligent customer interaction method and system based on cloud computing and Al, and the method comprises the steps: carrying out the elastic shunting of user request data in a high-concurrency scene in a cloud computing architecture, and obtaining a classified request data set; performing AI model-driven semantic analysis and emotion feature extraction on a user input text in the request data set to obtain a user core intention tag and an emotion tendency coefficient; performing fusion analysis on the user core intention label, the emotional tendency coefficient and the user historical interaction behavior data to obtain a user personalized demand portrait; and based on the personalized demand portrait of the user, performing adaptive optimization on the preset UI interaction scheme and the service response template to obtain a scenarized optimal interaction scheme. According to the method and the device, the service stability is improved, the core demand in the text input by the user can be deeply understood, and the defect that the personalized interaction demand of different users is difficult to meet at present is overcome.

The invention discloses an assembly type maritime work component management system based on BIM. The system comprises a BIM model management module, a component production management module, a component transportation management module and a component installation management module. The BIM model management module is used for creating, importing, updating and visualizing a BIM model of the assembly type maritime work component; the invention relates to the technical field of maritime engineering management. According to the BIM-based fabricated maritime work component management system, through a BIM model management module, full-process management is performed on fabricated maritime work components from model creation and introduction in a design stage to model updating and visual display in a construction process. All the modules interact through data interfaces, and a cloud computing architecture and a B / S mode are combined, so that all participants of a project can share information such as the size, the material and the progress of components in real time, an information island is broken, the problem of unsmooth information circulation in a traditional management mode is solved, and the information transmission efficiency and accuracy are greatly improved.

The invention discloses a CAE (Computer Aided Engineering) workflow control subsystem and method based on cloud computing, and belongs to the technical field of crossing of computer simulation and cloud computing, and the subsystem comprises a workflow configuration analysis module which is used for converting a CAE workflow configuration file possibly containing cyclic dependence into a directed acyclic graph state machine through strong connectivity component analysis; the workflow state machine operation module is used for dynamically deriving task groups which can be executed in parallel in the next stage in the state machine according to the current state; and the workflow scheduling module is used for submitting the tasks to a cloud solving platform in parallel and realizing fault recovery through state persistence. Through deep fusion of a graph theory method and a cloud computing architecture, automatic scheduling, efficient parallel execution and reliable fault tolerance of the CAE workflow are realized, and the computing resource utilization rate and the system reliability are remarkably improved.

Cloud computing architecture is described for implementing test modules in a communication-controlled cloud environment with access to private data. The test modules perform synchronous tests on the private data and export test results to an analytic environment subject to data export policies. An analytic application is used to asynchronously analyze the test results in the analytic environment. The cloud computing architecture alternatively or additionally includes an interface for deploying investigation-bound cloud environments in restricted subnets. A collection of software is instantiated in the investigation-bound cloud environment, and the investigation-bound cloud environment may be accessed with remote access credentials using a remote access protocol for testing the collection of software. Information about the investigation-bound cloud environment displayed in the analytic application, and the analytic application and the restricted subnet are forcibly deleted when the investigation is complete.

The invention discloses a civil engineering structure monitoring data fusion processing and visual analysis method based on a cloud computing architecture. In the invention, through standardized integration and dynamic cleaning of multi-source heterogeneous data, in a data access stage, an edge node carries out real-time preprocessing on vibration strain displacement and other multi-type sensor data, and in combination with a moving average filtering and abnormal value elimination mechanism, the stability of original data is ensured; after entering the cloud, heterogeneous data output by different devices are integrated into a standard column storage structure through unified format conversion and unit calibration, and meanwhile, missing values and sensor drift errors are processed by adopting linear interpolation and drift correction technologies, so that the data quality is remarkably improved. The hierarchical processing mode not only retains the real-time advantage of the edge end, but also realizes deep cleaning of large-scale data by means of cloud computing power, so that a more reliable data basis is obtained for subsequent fusion analysis, and structural state misjudgment caused by data exception is avoided as much as possible.

The invention relates to an intelligent employment data processing method and system based on cloud computing. The method comprises the following steps: integrating multi-source heterogeneous data through a cloud computing architecture and constructing a standardized data set; generating a dynamic feature set with a time dimension and an industry label by using a distributed feature extraction technology; parallel portrait construction and dynamic matching degree matrix calculation are combined to realize accurate mapping of professional ability and post requirements, and then a matching model is optimized by fusing real-time industry trend features; and finally, detecting industry demand changes, and according to the dynamic matching model, generating structured data supporting personalized post recommendation and skill improvement suggestions. The problems that a traditional method is poor in real-time performance, single in dimension and lagged in response are solved, the dynamic adaptive capacity and analysis precision of employment data processing are remarkably improved, and an employment service platform is helped to provide timely and accurate personalized post recommendation and skill improvement suggestions for job seekers.

A secure cloud computing architecture including: a first data management and / or computer program execution space (A) in which the data management or program execution is controlled by a user; and a second data management and / or computer program execution space (B) in which the data management or program execution is controlled by a third-party operator, first security policies (PSA) applied to the data or execution of programs in the first execution space (A); second security policies (PSB) applied to the data or execution of programs in the second execution space (B); a security property (P) expected by the user, compliance with the first and second security policies guaranteeing a data management and / or computer program execution in accordance with this property (P); and a trusted computing base (TCB) guaranteeing, in the absence of a violation, the application of the second security policies (PSB) in the management of the data and / or execution of the programs in the second execution space (B).

The invention discloses a resource charging method based on multi-cloud service, and particularly relates to the technical field of resource management and cost optimization. The method comprises the following steps of: constructing standardized multi-cloud service interaction path data by acquiring service flow billing related data configured by a user in a multi-cloud computing architecture; the method comprises the following steps: constructing a cross-cloud traffic loopback identification matrix by extracting data forwarding directions, node switching frequencies and loopback transmission modes among different cloud service providers; analyzing repeated charging and charging asymmetry characteristics of the cross-cloud traffic loopback, and generating an accumulated premium risk assessment index; the forwarding cost difference and the reverse charging threshold value between the cloud service nodes are analyzed, and path reconstruction priority scoring data are generated; the method comprises the following steps: constructing a cross-cloud path optimization objective function for minimizing accumulated charging cost, outputting optimized suggested path data, reconstructing a cross-cloud transmission path of business service, and adjusting cross-cloud node configuration; the cross-cloud resource utilization efficiency is improved, and the charging cost is effectively reduced.

The invention discloses a medical and health management system, and the system comprises a user management module which is used for carrying out the identity recognition, authority distribution and information management of various users of the system, including but not limited to medical personnel, patients and management personnel; the medical information storage module is used for storing medical related data including medical record information, diagnosis records, inspection reports, image data, medicine information, medical instrument information and the like of patients; the system adopts cloud computing architecture deployment, the expansibility is high, resources can be dynamically adjusted according to the business volume of medical institutions, the operation cost is reduced, multiple medical institutions can realize medical resource sharing through the system, such as sharing medical equipment reservation information and expert seating information, and the resource utilization rate is improved. For example, multiple hospitals in the area share high-end medical equipment through the system, the equipment vacancy rate is reduced, and resource waste caused by repeated purchase of equipment is avoided.

The invention discloses an edge network cloud system architecture system supporting multi-source heterogeneous data fusion, which relates to the field of cloud computing architecture, and comprises a cloud platform center layer, an edge computing node layer, a network transmission module and a sensing and executing layer. The data quality and the utilization efficiency are improved; relying on a cloud edge collaborative manager and a model performance evaluation model, ensuring that the new model is safely issued to an edge node on the premise of improving the performance; the data stored in the cloud data lake module is analyzed and processed by the deep fusion and modeling module to generate a global optimization strategy and model; the edge computing node layer is connected with a local actuator, a local decision instruction generated by the edge data fusion and decision module can be directly sent to the local actuator, and local closed-loop control under the condition of network disconnection or high network delay is achieved.

A system and method for split rendering immersive media using proxy edge cloud computing architecture. The system and method may include dynamically determining a task-split or splitting a task for rendering a scene in an immersive media stream into two or more computational tasks based on one or more processing delays, wherein the task-splitting splits the scene into first part including one or more first tasks performed by an edge computing system and second part including one or more second tasks performed by a cloud computing system. The system and method may include transmitting the first parts of the scene, and transmitting the second parts of the scene.

This application relates to a cloud-based intelligent employment data processing method and system. The method integrates multi-source heterogeneous data through a cloud computing architecture and constructs a standardized dataset; it utilizes distributed feature extraction technology to generate a dynamic feature set with time dimensions and industry labels; it combines parallel profile construction and dynamic matching degree matrix calculation to achieve accurate mapping between professional skills and job requirements; and it further optimizes the matching model by incorporating real-time industry trend features; finally, by detecting changes in industry demand, it generates structured data that supports personalized job recommendations and skills enhancement suggestions based on the dynamic matching model. This addresses the problems of poor real-time performance, limited dimensions, and delayed response in traditional methods, significantly improving the dynamic adaptability and analytical accuracy of employment data processing, and helping employment service platforms provide timely and accurate personalized job recommendations and skills enhancement suggestions to job seekers.

The invention relates to the technical field of cloud computing and data processing, in particular to an internet data parallel processing system based on a cloud computing architecture. Comprising a data acquisition and preprocessing module, a data distribution and scheduling module, a parallel computing module, a communication and coordination module, a result storage and feedback module, a dynamic resource elastic expansion module and an intelligent task decomposition module based on deep learning. According to the internet data parallel processing system based on the cloud computing architecture, accurate distribution of data and reasonable scheduling of tasks are realized through an intelligent algorithm of the data distribution and scheduling module in combination with load conditions and task type characteristics of each computing node, the problem of unbalanced node loads is avoided, and the system is high in practicability. The parallel computing module adopts a distributed computing framework, and data is segmented and then processed by a plurality of nodes at the same time, so that the processing time of large-scale data is shortened. Compared with a traditional data processing system, the processing speed is increased, and the real-time requirement of a user for data processing can be quickly responded.

This application provides a cloud computing-based integrated service big data processing terminal and method, applicable to the field of electronic digital data processing. The method includes: collecting information data sent by users to the terminal and storing it in a database; classifying integrated services into local services and cross-domain services based on the user-provided information data; after classifying the information data into integrated services, processing the information data in the database using a clustering algorithm; setting a threshold for cluster center selection on the information data, and sequentially selecting the remaining information data nodes as initial cluster centers with the threshold as the maximum interval before merging them; comparing the processed information data with the initial data stored in the database. Compared with traditional distributed and centralized cloud computing, the edge cloud computing architecture effectively combines the advantages of both traditional network structures, solving problems such as dispersed service resources and difficulties in resource coordination.

The invention belongs to the technical field of water conservancy projects, and provides a dam mass data distributed processing method based on a cloud computing architecture, which comprises the following steps: S1, performing unified access and standardized description on multi-source heterogeneous dam monitoring data, and generating a structured data object with a spatio-temporal index; s2, performing adaptive data fragmentation and distributed storage optimization based on space-time attributes and access features of the data objects; s3, according to the characteristics and resource requirements of the computing tasks, constructing a dynamic extensible parallel computing workflow; s4, in the process of executing the parallel computing workflow, implementing resource flexible scheduling and task migration based on load feedback; and S5, performing multi-dimensional aggregation on the processing result, and feeding back to the data storage layer.

The application discloses an edge computing method based on artificial intelligence, and particularly relates to the technical field of edge computing and artificial intelligence. The method comprises the following steps: deploying a light neural network model on an edge device to realize local data processing; dynamically optimizing the allocation strategy of a computing task between an edge node and a cloud end through a deep reinforcement learning algorithm; and realizing cross-device collaborative training by using an improved federated learning framework to guarantee data privacy. Through real-time data processing on the edge side, the average system response delay is reduced by more than 40%; the task offloading algorithm based on Q-learning can reduce network traffic by more than 30%; and the innovative differential privacy federated learning mechanism reduces the data leakage risk by 60% on the premise of ensuring model accuracy. Through multi-level technical cooperation, the application effectively solves the problems of high response delay, large network bandwidth occupation and insufficient data privacy protection in the traditional cloud computing architecture.

The invention relates to a method for allocating a computer unit, referred to as a computer unit to be allocated, to a computer unit cluster from a set of computer unit clusters comprising at least a first computer unit cluster (LP_K8S) and a second computer unit cluster (HP_K8SJ), the computer unit to be allocated comprising at least a first and a second virtual machine (VM LP, VM HPJ) previously associated with the first cluster and with the second cluster respectively, and at most one of the virtual machines, referred to as the active virtual machine, being active at a given time, the allocation method comprising: - stopping (156, 256) the first active virtual machine of the computer unit to be allocated, which active virtual machine is associated with the first cluster; and - activating (160) the second virtual machine, associated with the second cluster, so as to ensure dynamic allocation of said computer unit to the second cluster.

Cloud computing architecture is described for implementing test modules in a communication-controlled cloud environment with access to private data. The test modules perform synchronous tests on the private data and export test results to an analytic environment subject to data export policies. An analytic application is used to asynchronously analyze the test results in the analytic environment. The cloud computing architecture alternatively or additionally includes an interface for deploying investigation-bound cloud environments in restricted subnets. A collection of software is instantiated in the investigation-bound cloud environment, and the investigation-bound cloud environment may be accessed with remote access credentials using a remote access protocol for testing the collection of software. Information about the investigation-bound cloud environment displayed in the analytic application, and the analytic application and the restricted subnet are forcibly deleted when the investigation is complete.

Cloud computing architecture is described for implementing test modules in a communication-controlled cloud environment with access to private data. The test modules perform synchronous tests on the private data and export test results to an analytic environment subject to data export policies. An analytic application is used to asynchronously analyze the test results in the analytic environment. The cloud computing architecture alternatively or additionally includes an interface for deploying investigation-bound cloud environments in restricted subnets. A collection of software is instantiated in the investigation-bound cloud environment, and the investigation-bound cloud environment may be accessed with remote access credentials using a remote access protocol for testing the collection of software. Information about the investigation-bound cloud environment displayed in the analytic application, and the analytic application and the restricted subnet are forcibly deleted when the investigation is complete.

The invention discloses a confidential cloud computing architecture with supervision and expansion, a data interaction method, a terminal and a medium, and relates to the field of cloud computing, and the confidential cloud computing architecture comprises a hardware layer, a platform layer, a supervision layer and an adaptation layer. The hardware layer integrates the independent trusted execution environment into a trusted root of a preset public key infrastructure; the platform layer provides an extensible confidential calculation scheduling system; the supervision layer provides a full-life-cycle supervision system; the adaptation layer provides a compatibility and trust verification mechanism. According to the method, a unified trust system is established, the user trust verification cost is reduced, and full-process supervision of data circulation, computing power use and algorithm execution is realized by constructing a supervisible confidential cloud computing architecture. In addition, the invention also provides a high-expansibility cluster solution, supports large-scale data processing and elastic capacity expansion, adapts to different scene requirements, realizes plug-and-play deployment and use by being compatible with the existing cloud computing normal form, and reduces user migration and application cost.

This invention relates to the fields of intelligent transportation and information processing technology, specifically a multimodal travel route recommendation system for passengers based on knowledge graphs and machine learning. Deployed on a distributed cloud computing architecture, the system comprises a multi-source heterogeneous data acquisition and preprocessing layer, a travel knowledge graph construction and management layer, a dynamic spatiotemporal semantic perception layer, a knowledge-guided deep recommendation decision layer, and an interactive route presentation and feedback layer. These layers are interconnected through a high-speed communication protocol to achieve closed-loop linkage. The system integrates static traffic, real-time operations, passenger attributes, geographical environment, and unstructured text data to construct a semantically rich travel knowledge graph, and combines graph convolution and temporal models to perceive the travel context. The recommendation engine, based on a multi-task neural network, completes route generation, personalized ranking, and interpretive reasoning. The system supports interactive feedback and dynamic optimization, making it suitable for intelligent transportation and personalized travel services.

Systems, methods, techniques, and apparatus for power network state estimation are disclosed. An exemplary embodiment is a method for state estimation in a power network, the method comprising: receiving a set of Supervisory Control and Data Acquisition (SCADA) information including a power network topology; generating a SCADA state estimate using the set of SCADA information; receiving a set of PMU phasors using a cloud computing architecture; aligning the timestamps of the SCADA estimate with the timestamps of the set of PMU phasors using the cloud computing architecture; updating the power network topology using the set of PMU phasors using the cloud computing architecture; generating a hybrid state estimate using the updated power network topology, the set of PMU phasors, and the SCADA state estimate using the cloud computing architecture; and transmitting the hybrid state estimate to a local control system.

This disclosure provides a decentralized cloud computing architecture, method, and apparatus, relating to the field of cloud computing technology. The architecture includes: a decentralized hardware layer comprising multiple hardware components, including a general-purpose computing processor and at least one domain-specific accelerator (DSA), with any two hardware components interconnected via a switch; and an operating system layer comprising: an operating system (OS) mounted on the general-purpose computing processor, and at least one OS agent, each corresponding to one of the at least one DSA. Through the collaboration of the decentralized hardware layer and the operating system layer at both the hardware and software levels, a centralized cloud computing architecture is constructed. Thus, neither the general-purpose computing processor nor the DSA needs to communicate via a central hub.

The invention belongs to the technical field of cloud computing organization management, and discloses an organization management system based on a cloud computing architecture, which is characterized in that the specification of a cloud server is adjusted in advance through an organization service load prediction model in combination with historical data, and the system crash during peak hours is avoided; the main-standby-disaster three-available-area architecture and the multi-specification micro-cloud instance not only meet the high-concurrency and high-stability requirements of provincial organizations, but also adapt to township-level low-cost and low-concurrency scenes, and avoid the dilemma that a large vehicle is pulled by a small horse at a basic level; compared with a traditional system, the resource utilization rate is remarkably increased, meanwhile, bottom support is provided for stable operation of a follow-up service module, and the operation and maintenance cost and the service interruption risk caused by improper resource adaptation are reduced; through cooperative linkage among the modules, automatic synchronization of module data such as organization management, learning and organization fee is realized, for example, when an organization relationship is transferred, learning authority can be opened and a fee-paying cardinal number can be generated without manual repeated input, and the operation burden of a primary organization is greatly reduced.

The invention provides a virtualized Linux cloud host creation method and device, and relates to the technical field of cloud computing architecture. When the method is executed, firstly, an available IP address is obtained, then, a network configuration file is generated according to the available IP address, then, the network configuration file is injected into a virtual machine mirror image so as to automatically configure network parameters, then, the virtual machine mirror image is subjected to system initialization, and the virtual machine mirror image after system initialization is obtained. The method comprises the steps of initializing a system of a virtual machine, performing disk management operation on the virtual machine image after the system is initialized to obtain a virtual machine image after the disk management operation, and finally performing monitoring integration operation on the virtual machine image after the disk management operation to obtain a virtualized Linux cloud host. In this way, the creation effect of the virtualized Linux cloud host can be improved.

Cloud computing architecture is described for implementing test modules in a communication-controlled cloud environment with access to private data. The test modules perform synchronous tests on the private data and export test results to an analytic environment subject to data export policies. An analytic application is used to asynchronously analyze the test results in the analytic environment. The cloud computing architecture alternatively or additionally includes an interface for deploying investigation-bound cloud environments in restricted subnets. A collection of software is instantiated in the investigation-bound cloud environment, and the investigation-bound cloud environment may be accessed with remote access credentials using a remote access protocol for testing the collection of software. Information about the investigation-bound cloud environment displayed in the analytic application, and the analytic application and the restricted subnet are forcibly deleted when the investigation is complete.

The invention relates to the technical field of internet cloud sharing design service platforms, and discloses a clinical, design and production three-terminal data interconnection cloud sharing design service platform, which comprises a platform architecture, a function module and a core module, the platform architecture is based on a cloud computing architecture and comprises a clinical port, a design port, a production port, a big data processing center, a research and development center, a machine room server, a big database and a cloud service center. The function module comprises a data interaction and processing module, an order management module, a communication cooperation module, a production docking module, a qualification auditing and credit management module and a data security and tracing module. The core module comprises an AI intelligent module, a distributed supply chain management module and a patient interaction module. The cloud sharing design service platform can realize full-process digital closed loop from in-port scanning, data acquisition and CAD design to 3D printing and numerical control cutting, and the production efficiency and precision are improved, so that the defect of process splitting in the prior art is overcome.

The application discloses a resource charging method based on multi-cloud services and particularly relates to the technical field of resource management and cost optimization; the method comprises the following steps: acquiring service traffic charging related data configured by a user in a multi-cloud computing architecture, and constructing standardized multi-cloud service interaction path data; extracting data forwarding directions, node switching frequencies and loop transmission modes among different cloud service providers, and constructing a cross-cloud traffic loop identification matrix; analyzing repeated charging and asymmetric charging characteristics of cross-cloud traffic loops, and generating cumulative premium risk assessment indexes; analyzing forwarding cost differences and reverse charging thresholds among cloud service nodes, and generating path reconstruction priority score data; constructing a cross-cloud path optimization objective function for minimizing cumulative charging costs, outputting optimized recommended path data, and reconstructing cross-cloud transmission paths of business services and adjusting cross-cloud node configurations; and the method improves cross-cloud resource utilization efficiency and effectively reduces charging costs.