21 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 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.

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 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.

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.

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.

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 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.

The application belongs to the technical field of cloud computing organization management, and discloses an organization management system based on a cloud computing architecture, which adjusts the cloud server specification in advance by combining historical data with an organization business load prediction model to avoid system collapse during peak periods; the master-standby-disaster three available area architecture and multi-specification micro cloud instance meet the high concurrency and high stability requirements of provincial organizations, are suitable for low-cost and low-concurrency scenarios of township-level organizations, and avoid the plight of small organizations pulling large carts; compared with traditional systems, the resource utilization rate is significantly improved, and the stable operation of subsequent business modules is supported, thereby reducing operation and maintenance costs and business interruption risks caused by improper resource adaptation; through inter-module coordination and linkage, the module data of organization management, learning, organization fees and the like are automatically synchronized, so that when the organization relationship is transferred, learning permission can be opened and a payment base can be generated without manual repeated input, thereby greatly reducing the operation burden of grassroots organizations.

The application relates to a cloud computing network stress testing method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: analyzing the planning data of a tested item by a target analysis tool to obtain multiple network planes. In the multiple cloud computing architectures contained in an architecture perception model, the planning data and the network planes are matched to obtain an on-site cloud computing architecture of the tested item. The required data for stress testing is generated based on the planning data and the on-site cloud computing architecture. The link test result is obtained by testing based on the required data for stress testing. By using the method, the automatic and accurate perception of the cloud computing architecture of the tested item can be realized, the number of links required for testing the cloud computing structure of the tested item and the specific address are identified, the automatic testing of all the links required for testing in the cloud computing architecture is realized, and the stress testing efficiency in the cloud network scene is improved.

The invention discloses a cloud service control system based on a cloud computing architecture, and relates to the technical field of cloud services. According to the cloud service control system based on the cloud computing architecture, the resource analysis module, the judgment module, the resource scheduling module, the metering module and the resource optimization module are arranged, the resource utilization efficiency and the matching degree in a future time period are analyzed through a predictive scheduling algorithm, and the resource demand and the utilization efficiency are analyzed in advance; quick response can be ensured and the utilization efficiency can be maximized when the resource demand changes; by analyzing the link state of the service node and carrying out resource allocation according to the load balancing characteristic and the geographic position of the user, the use of the service node is optimized, the response speed and the load balancing capability of the system are improved, and node overload and resource idleness are avoided. A resource scheduling strategy is adjusted through a distributed architecture and load balancing, the loads of the central server and the key manager are dispersed, the stability and reliability of the system are improved, and the risk of a single point of failure is reduced.

The application discloses an airport special vehicle real-time scheduling method based on a digital twin model, which comprises the following steps: an edge server receives special vehicle real-time information, calculates special vehicle priority according to the special vehicle real-time information, and sends the obtained special vehicle priority to a cloud center; the cloud center constructs a digital twin model based on an edge-cloud computing architecture, calculates flight priority, and determines a special vehicle scheduling scheme according to the flight priority and the special vehicle priority; the cloud center performs online simulation on the special vehicle scheduling scheme, issues real-time scheduling instructions to the special vehicle according to the simulation result, and sends the real-time scheduling instructions to the edge server. The method adopts the edge-cloud computing architecture to construct the digital twin model, considers real-time information of the special vehicle priority and the flight priority on the basis of the digital twin model, adjusts the scheduling plan of the special vehicle according to the flight priority, realizes real-time adjustment and accurate control of a flight guarantee process, and improves flight guarantee efficiency.