133 results about "Cloud architecture" patented technology

The invention relates to a multi-modal data-driven rail transit construction potential safety hazard identification method and system, and solves the problem that the accuracy and real-time performance of potential hazard identification are insufficient due to the fact that multi-source data cannot realize deep collaboration, and the method comprises the following steps: obtaining multi-modal data containing a BIM + GIS model, an unmanned aerial vehicle image stream and Internet of Things data; according to an edge end-cloud architecture, by taking BIM + GIS coordinates as a reference, the edge end associates and binds data and cleans and transmits the data to the cloud, and the cloud corrects and generates a standardized data set; and the comprehensive risk index is calculated by using an entropy weight method, and a report is generated and pushed to a construction management terminal. The method has the following effects: the multi-modal data barrier is broken, the integrated accurate identification of the hidden hidden danger of the rail transit construction is realized, and the accuracy and efficiency of the construction safety management and control are fundamentally improved.

The invention provides an environment supervision method and system based on an edge cloud architecture. The method comprises an edge end device and a cloud platform, wherein the edge end device is arranged on an environment supervision site; the execution steps of the side end device comprise: collecting a pollution source multi-modal data stream; according to the pollution source multi-mode data flow, triggering an alarm through a grading abnormity identification model; in response to the alarm, generating an evidence chain with a timestamp; extracting a monitoring deviation feature D, a behavior anomaly feature B and a semantic feature S according to the evidence chain; compressing the monitoring deviation feature D, the behavior anomaly feature B and the semantic feature S to generate a feature packet Z; and uploading the feature packet Z to a cloud platform. The cloud data matching performance can be effectively improved, and the false alarm rate is reduced.

The invention provides a chip design and verification system and method.The system comprises a tool chain integration and automation module, the tool chain integration and automation module is provided with a tool containerization unit and a multi-physics field collaborative optimization unit, and the tool containerization unit is used for containerizing and packaging an EDA tool into an independent functional unit; each EDA tool container comprises a complete runtime environment, a dependency library and a license; the multi-physics field collaborative optimization unit is used for acquiring design parameters of the chip and evaluating the performance of the chip design scheme; the system further comprises a chip particle heterogeneous integration module which is used for carrying out system modeling and layout planning on the chip according to the received chip design file and carrying out multi-physics field simulation on a chip model. The system further comprises a security and credibility verification module which is used for carrying out vulnerability detection and security analysis on the chip design file in a credible environment in the chip design process. The invention further provides a working method of the system. According to the invention, the problem of isolation of a chip design tool chain can be avoided.

The invention discloses a collaborative secret state task matching method based on edge calculation in mobile crowdsourcing. The method comprises the steps of system initialization and key generation, requester-cloud platform registration authentication, worker-edge server registration authentication, login authentication and attribute submission, requester task issuing, cloud platform task delegation, secret state matching, worker task content decryption, worker answer submission, answer forwarding and decryption. According to the invention, cooperative authentication of workers and requesters and precise task matching based on attributes are realized under an edge-cloud architecture. A fuzzy extractor and an authentication encryption method with associated data are introduced, so that low-cost user local authentication and registration and authentication of a joining system are realized, the security is improved, and the calculation cost is reduced. Through function hidden inner product encryption and a Paillier cryptographic algorithm, task-worker matching and task answer submission are realized in a ciphertext state, and the requirements of a mobile crowdsourcing application scene with high safety, high privacy and dispersed cost are met.

The invention provides a remote sensing data processing task thermal replacement method based on a cloud architecture. The method comprises the following steps: constructing a comprehensive state model of nodes; carrying out structure standardization processing on the received original remote sensing data so as to carry out unified management on data sources; sensing the comprehensive state of each node in the data source and the node cluster in real time; extracting a graph structure of a current to-be-scheduled task, automatically selecting a most suitable scheduling algorithm according to the graph structure, and generating a task execution topological graph; performing task distribution and execution according to the task execution topological graph; sensing the comprehensive state of each node in real time in the execution process of the task, and determining a resource elastic scaling strategy of the node; fault detection is conducted on the nodes and the task execution environment, after it is judged that faults occur, the operator operation state is rebuilt in other available nodes based on operator-level local snapshots, tasks are recovered through hot loading, and persistent storage and output of result data are completed.

The invention relates to the technical field of computers, and discloses a document collaboration platform based on cloud computing. Real-time semantic complementation, automatic typesetting optimization and content abstract generation are provided through the intelligent editing assistant module, the manual editing burden is remarkably reduced, and the efficiency is improved; a rule engine of an automatic operation module is combined to realize automatic management of document versions, intelligent resolution of conflicts and standardized review, and the problems of communication cost and conflicts in multi-person cooperation are thoroughly solved; the problem of information overload is solved by relying on the deep semantic processing capability, and accurate positioning of core content is realized; and meanwhile, based on the elastic expansion capability of the distributed cloud architecture, the stability and high availability of multi-terminal real-time cooperation are ensured. Finally, a document processing closed loop integrating intelligent assistance, an automatic process and efficient cooperation is formed, and the bottlenecks of a traditional platform in the aspects of editing efficiency, cooperation fluency, information processing and the automation level are comprehensively broken through.

The invention relates to the field of Internet of Vehicles information security, and provides a multi-source heterogeneous detection model based on federated learning for potential safety hazards of vehicle and road cloud architecture information. A federated learning mechanism is utilized, different communities are fused, and high-precision and low-delay diversified attack detection is realized aiming at detection models of different attack characteristics. According to the technical scheme, the method comprises the following steps: dynamically dividing a local model by using multiple graph clustering algorithms, and performing community classification according to feature similarity; selecting a community initial for guiding optimization in the same community by means of a dual-objective function model; a weighted average or gradient optimization aggregation community model is adopted to improve the accuracy and convergence speed of the model; heterogeneous knowledge fusion among models is realized through mapping and transfer learning technologies, and a reward and punishment mechanism is designed to dynamically adjust the contribution degree of the models. The invention aims to construct a complex attack detection model with higher detection speed, higher precision and less resource consumption through dynamic community division and multi-objective optimization to form an integrated safety guarantee system of a distributed architecture of the Internet of Vehicles.

The invention relates to a mountain photovoltaic automatic laying method and system based on BIM and GIS, and the method comprises the steps: obtaining the engineering parameters of a to-be-laid photovoltaic project based on the GIS, and inputting the engineering parameters into a built-in photovoltaic module database of a preset cloud architecture system to obtain a photovoltaic module matched with the to-be-laid photovoltaic project and the corresponding photovoltaic module parameters; calculating the capacity of photovoltaic panels in the photovoltaic modules and the inverter based on the parameters of the photovoltaic modules, and taking the capacity as the boundary of the photovoltaic layout area; calculating layout parameters of the boundary of the photovoltaic layout area by utilizing a GIS (Geographic Information System); based on the layout parameter of each grid unit and a preset screening condition, screening out an area capable of being laid on the GIS, and establishing a BIM model of the area capable of being laid; based on the layout parameters of the grid units in the layout area, a shadow analysis mathematical model is adopted to calculate the spacing between the photovoltaic panels before and after layout; generating a photovoltaic panel layout array based on the front-back spacing of the photovoltaic panels; and inputting the photovoltaic panel layout array into the BIM model of the layout area to complete automatic layout of the photovoltaic panels.

The invention provides a method for constructing a container cloud agent based on a MiniMax-Text-01 large model, a medium and equipment, belongs to the technical field of automatic operation and maintenance, and can perform fault detection, flow prediction and resource elastic expansion and contraction according to real monitoring data. The intelligent agent constructed by the invention can automatically pre-judge and give an optimal scheme for capacity expansion or migration when finding that the load of the server is abnormal, so that the frequency and the duration of manual intervention are reduced. In a multi-cloud and hybrid cloud architecture, an intelligent agent can cooperatively manage calculation, storage and network resources on a heterogeneous platform, and cross-cloud load balancing and cost optimization are realized. In addition, the intelligent agent can help a data analysis and machine learning team to better utilize a cloud GPU / TPU cluster and automatically select a proper computing power and storage scheme, so that the training and prediction efficiency of mass data is remarkably improved. In the scene of Internet of Things and edge computing, the intelligent agent can link the cloud and the edge nodes, intelligently dispatch the computing power and bandwidth of each node, and find and correct abnormal data streams in time.

The collaborative scheduling system of the end-side cloud architecture comprises a cloud subsystem, a plurality of side subsystems and a plurality of terminal systems, the side subsystems are associated lower-level systems of the cloud subsystem, and the terminal systems are associated lower-level systems of the side subsystems; the cloud subsystem is used for responding to a task request, determining a task scheduling process, generating task scheduling information and sending the task scheduling information to the side subsystems or the terminal subsystems through an encrypted transmission channel for re-scheduling or execution, and an initiator of the task request is any one of the cloud subsystem, the plurality of side subsystems and the terminal systems; the side subsystem is used for receiving a task request and generating task scheduling information, or reporting the task request to the cloud subsystem so as to receive the task scheduling information, and then sending the task scheduling information to the side subsystem through an encryption transmission channel for execution, so that dynamic optimization and efficient utilization of resources of the whole network are realized; and a more efficient and safer collaborative system is constructed.

The invention relates to the technical field of key distribution, in particular to a multi-cloud architecture-oriented dynamic key fragmentation storage management method and system, which comprises the following steps of: dividing a manufacturing process of the manufacturing industry into a plurality of time sequence stages corresponding to different data access authority levels and security requirements, and constructing corresponding quantum state evolution operators; driving the key state to evolve among different time sequence stages through a quantum state evolution operator according to the progress of the manufacturing process, and adjusting the access authority of the key; quantum entanglement connection is established between supply chain nodes, the trust degree between the nodes is measured and quantized through the association of the quantum entanglement state, and the distribution weight of the key fragments is adjusted based on the change of the trust degree; collecting constraint parameters of production takt, equipment state, logistics delay and quality index in the manufacturing process, and converting the constraint parameters into quantum state constraint conditions; and when it is detected that the constraint condition changes, recalculating the key fragment distribution scheme based on the quantum state constraint condition and the credibility, and synchronously distributing the key fragment distribution scheme to each supply chain node.

A computing services environment may include application gateways receiving application-layer request messages from various sources. The computing services environment may also include an autonomous agent platform configured to instantiate and execute an autonomous agent to evaluate network traffic associated with a portion of the computing services environment. The computing services environment may also include an orchestration engine configured to determine one or more mitigation policies corresponding with one or more of the application gateways based on identification of the application-layer distributed denial of service attack by the autonomous agent. The computing services environment may also include application-layer web application firewalls corresponding to the plurality of application gateways and implementing the one or more mitigation policies to prevent a subset of subsequent application-layer request messages from the subset of the sources from reaching one or more components of the computing services environment.

The invention discloses a full-link monitoring and operation and maintenance management method for a point mall supply chain platform under a multi-cloud architecture, which can monitor full-link nodes of the supply chain platform in real time and provide a basis for optimizing resource configuration and improving system performance. The monitoring data is analyzed and processed through the star large model, and when the monitoring data of the system is found to be abnormal, an alarm notice can be sent out in time to remind operation and maintenance personnel to carry out fault processing, so that problems during use of a user are avoided. When it is monitored that some resources are low in long-term utilization rate and zombie applications may occur, an alarm notice is sent to remind operation and maintenance personnel to optimize resource allocation, and cost is saved. Stable operation of the point shopping mall supply chain platform is guaranteed, and user experience is improved. The stable platform operation and the timely fault processing can improve the system performance, optimize the operation cost, enhance the credibility and loyalty of the user to the platform, and promote the improvement of the activeness and retention rate of the user. According to the full-link monitoring and fault processing method under the multi-cloud architecture, unified management and monitoring of a plurality of cloud platforms can be realized, so that the management process is simplified, the management cost is reduced, and the flexibility and expandability of the whole system are improved.

The invention discloses an early cancer screening model based on a cloud architecture and a construction method, and particularly relates to the technical field of medical artificial intelligence. The method comprises the following steps: firstly, collecting and normalizing heterogeneous medical image and biomarker data of a plurality of geographic areas, and constructing a unified data set; then extracting image features and marker features by adopting multi-task learning, fusing equipment and region attributes, establishing a joint representation vector, and training a preliminary screening model; after the model is deployed to a cloud end, target area data is adapted through online transfer learning, and an area model is generated; further extracting an activation heat map, identifying a weak signal high response area in combination with sensitivity analysis, comparing with manual annotation, constructing a local weight adjustment strategy based on errors, optimizing the model, and outputting a final model; the method improves the recognition capability of early lesions under the condition of low signal-to-noise ratio, is suitable for remote and basic screening scenes, and has a high practical value.

The invention discloses an ultra-low delay smart home cooperation system and method based on an edge computing gateway, the system comprises smart home equipment, the edge computing gateway and a cloud server, and the edge computing gateway comprises a local storage module, a data processing module, a communication module and an equipment cooperation control module. Four core units including a local storage module, a data processing module, a communication module and an equipment cooperative control module are integrated through an edge computing gateway to form an acquisition-processing-control localized closed loop, and real-time data processing, equipment state monitoring and cooperative decision are sunk to an edge layer. The method breaks through the hundred millisecond delay bottleneck of the traditional cloud architecture, realizes millisecond response speed, and in addition, only uploads desensitized abstract data to the cloud by only performing necessary processing on the original data of the equipment at the edge gateway, thereby avoiding the risk of user privacy disclosure.

An object access service is implemented on a computer system for configuring and enforcing multi-dimensional data security using security assignments beyond role-based access controls. The computer system accesses a request submitted on behalf of a user for access to database structure(s). The computer system requests predicate(s) mapped to role(s) assigned to the user and stored in association with the database structure(s). Predicate(s) submitted to the database may be dynamically filled in by the database pursuant to retrieving data from the database structure(s) to reference security assignment field(s) and security assignment value(s) of the security assignment. The predicate(s) are used to retrieve a security assignment that restricts access to the database structure(s) beyond the role(s) assigned to the user. The security assignment is used to restrict data accessible from the database structure(s) to generate a result set, which is transmitted to a client consumer system for consumption via a consumer interface.

The application belongs to the field of cloud computing, and is a management method of a computing power cloud architecture combining a container cloud and a virtual machine cloud, a cloud management platform, a host resource management layer and a resource access layer, and comprises the following steps: S1: a host resource management module registers with the cloud management platform; S2: a registration and scheduling module sends a container creation instruction, and a host creates and schedules a container; S3: the host resource management layer reports idle resource information to the cloud management platform, and the cloud management platform triggers the binding and deployment of the container and the host; S4: a virtual machine access module sends a virtual machine creation instruction, and deploys a virtual machine to a specified host; S5: a resource access layer respectively collects and reports the running states of the container and the virtual machine; S6: a container state monitoring module sends a container unbinding and destruction instruction, and a host resource management module executes the operation; S7: a virtual machine host module sends an unbinding instruction, executes virtual machine unbinding, and a resource access layer releases resources.

The invention provides a railway BIM modeling method based on a cloud architecture. The railway BIM modeling method comprises the following steps: collecting design data of related equipment related to a railway; making a railway BIM model based on three-dimensional design software; creating a three-dimensional scene of the railway engineering, performing cache slicing through a cloud end, and publishing the three-dimensional scene as a three-dimensional data service; building a cloud architecture GIS environment; analyzing the BIM models in batches in the cloud architecture GIS environment, and storing the BIM models in a distributed system; a railway BIM model management platform based on the cloud GIS is built, railway BIM model data service is released, and visual display and automatic integration of the BIM model are achieved. And the cloud architecture and BIM modeling are combined, so that the modeling efficiency is effectively improved.

The present application relates to the technical field of computer, and especially relates to a hybrid cloud architecture graph construction method and system, electronic equipment and storage medium. The method comprises the following steps: deploying a collection agent on at least one cloud platform of a hybrid cloud, or burying a point in a container image of an application system of the cloud platform, so as to obtain application container information when the application container is started; performing container self-discovery based on the container name in the application container information to obtain new container information; performing container asset life cycle management and asset data integration based on the new container information to obtain a container asset information table; and constructing an architecture graph of the application system based on the container asset information table and a dependency relationship in a configuration management database. The method can effectively improve the efficiency and accuracy of container self-discovery in the hybrid cloud, and can update the architecture graph in a timely manner, has high accuracy, and has strong application adaptive ability.

The invention discloses a whole-process data perception and comprehensive evaluation method and system for intelligent homework accompanying, and belongs to the technical field of intelligent education and human-computer interaction. The method comprises the following steps: before homework, generating a dynamic homework plan based on a homework list, student historical data and a real-time state; during operation, academic, behavior and emotion data are collected through a multi-modal sensor and are processed under an end-cloud architecture; in the writing process, handwriting recognition is combined with a knowledge graph to realize real-time discovery and attribution of wrong questions; after operation, a comprehensive evaluation report is generated based on the multi-dimensional evaluation model, feedback is output through a natural language generation module, and the report is stored after being subjected to credible evidence storage processing. The system realizes objective monitoring and personalized evaluation of the whole process of student homework through cooperation of dynamic adaptive planning, real-time error attribution, multi-modal perception fusion and block chain evidence storage, and has the advantages of improving accompanying quality, enhancing report credibility and facilitating long-term tracking.

Cloud architecture information descriptive of a proposed cloud architecture is obtained, including user response information including information indicative of cloud architecture requirements for the proposed cloud architecture to fulfill and component selection information indicative of cloud components that meet cloud architecture requirements. The cloud architecture information is processed with agentic orchestration models to obtain a role output including security control information indicative of security controls selected for the proposed cloud architecture. Each of the agentic orchestration models includes a machine-learned model prompted to fulfill a corresponding cloud architecting role. The role output is associated with a cloud security role. Based on the security control information, information indicative of the security controls selected for the proposed cloud architecture is provided to a user device.

The invention discloses a large model distributed training method and system based on all-in-one machine hybrid cloud, and relates to the technical field of resource management. A hybrid cloud training architecture and secure communication are established between a local all-in-one machine cluster and a public cloud elastic cluster; dividing training data according to sensitivity, and realizing local storage of core sensitive data and cloud side storage of non-sensitive data; dividing the training task into a core task and an extension task, and scheduling the core task and the extension task to a local training node and a cloud side training node for execution; monitoring local resource load and task progress, and elastically applying and releasing cloud side training nodes according to training stages; and limiting cross-domain transmission as necessary information, updating model parameters through gradient aggregation, and synchronously entering the next iteration. Through the technical scheme of the invention, on the premise of ensuring that sensitive data is not out of a domain, on-demand expansion and recovery of cloud side computing power are realized, cross-domain communication overhead is reduced, distributed training throughput and stability are improved, a training period is shortened, and comprehensive cost is reduced.

The invention provides a hybrid cloud architecture-based power business load balancing scheduling method and system, and the method comprises the steps: classifying power business operation data collected in real time based on the importance of power businesses, and obtaining a target power business category; determining a current load state of the target power business category by combining resource state information of the private cloud resource pool and the public cloud resource pool based on the target power business category and the corresponding power business operation data; and determining a load scheduling strategy based on the current load state for the target power business category. According to the method, the problems that in an existing method, the resource capacity of a local physical server cluster is fixed, sudden peak loads are difficult to deal with, and service response delay and data processing interruption are likely to be caused are effectively solved, and meanwhile stable and efficient operation of a power system is guaranteed.

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.

Proliferation of edge devices has significantly advanced technologies in sectors such as autonomous driving and surveillance. However, deploying machine learning models on these resource-constrained devices presents challenges including scalability and managing unpredictable workloads thereby affecting real-time performance in edge-only environments. The present disclosure discloses a method and system for workload deployment in a content guided and service level agreement (SLA) aware edge-cloud architecture. In the present disclosure, a camera feed content-guided load balancing technique is provided that dynamically manages workloads between edge and cloud. Features are extracted from an incoming camera feed to perform load-balancing process efficiently. The load balancer determines a maximum number of concurrent feeds for processing at the edge, with the remaining feeds handled by the cloud based on content of the incoming camera feed. Additionally, a cost model to estimate expenses of deploying the edge-cloud architecture in real-world scenarios with dynamic workloads is provided.

The invention discloses an intelligent garden data management method and system based on cloud computing, and relates to the crossing field of cloud computing technologies, and the method comprises the steps: collecting and preprocessing multi-dimensional garden data through a distributed terminal, and adding classification and space-time labels; using a hybrid cloud architecture to realize data security storage and collaborative management, and establishing a classification index; a deep learning model is trained based on historical data, an analysis result is output, a personalized maintenance scheme is generated, and dynamic optimization is performed through real-time feedback; secure data intercommunication between the platform and various terminals is realized by means of a multi-terminal collaborative interaction module, and data security is guaranteed by adopting encrypted transmission and authority control; monitoring and encryption protection are implemented in the whole process, a traceable log is generated and filed regularly, and continuous iterative upgrading is performed in combination with a model effect. The method has the advantages that based on the hybrid cloud architecture and distributed data processing, garden management is promoted to be transformed to data intelligent driving through full-process closed-loop optimization, and the management efficiency and the refinement level are greatly improved.

The invention discloses a real-time classroom behavior monitoring and feedback method based on an end-edge-cloud architecture and multi-modal perception, belongs to the technical field of intelligent education, and is implemented based on a three-layer architecture of a terminal layer, an edge layer and a cloud layer. The terminal layer comprises student terminals and classroom cameras, the student terminals read seat labels through RFID, complete identity verification in combination with fingerprint identification, generate identity-seat binding certificates and send the identity-seat binding certificates to the edge layer through Bluetooth, and the cameras collect video streams and transmit the video streams to the edge layer; the edge layer target detection model identifies the behavior of an original video stream and outputs a result, the space-time association matching and rule engine maps a behavior area to a physical seat according to a seat-image coordinate relationship, the behavior area is matched with a voucher to generate an association behavior event record, a feedback control instruction is generated after event accumulation judgment, and the feedback control instruction is sent to the video stream. The information is sent to a student terminal through Bluetooth to execute reminding; and the cloud server receives the data uploaded by the edge layer, stores and calculates the data, and responds to a query and management instruction.

The invention provides a highway toll collection system data processing method and system based on a cloud architecture, and the method comprises the steps: collecting toll collection data streams uploaded by edge nodes under the cloud architecture, and extracting features to generate exclusive data behavior fingerprints and fingerprint baselines; then constructing a cross-domain cooperative defense network comprising edge nodes, a cloud center and a terminal defense module, wherein the modules are associated through a defense linkage rule; the fingerprint deviation degree is monitored in real time, and when the fingerprint deviation degree exceeds a preset range, multi-module linkage defense is triggered and a processing result is generated; performing intelligent self-healing adjustment on a defense linkage rule and a fingerprint baseline according to a processing result, generating a scheme and synchronizing the scheme to all modules; and finally, distributing the data subjected to defense processing to corresponding terminals. According to the invention, the safety and operation efficiency of the charging system are improved.

The invention relates to the technical field of big data processing and information security, and discloses a cloud architecture-oriented multi-dimensional credit data processing system. Comprising a multi-source data acquisition and standardization module, a multi-dimensional space-time topology mapping engine, a query intention analysis and topology preheating module, a cold and hot layered encryption storage scheduler, a path integrity verification and cache management module and an event driving and incremental calculation module. According to the method, a multi-dimensional spatio-temporal topological graph containing entities and field domain nodes is constructed, and the influence of spatial attributes on credit is quantified; performing cold and hot layered encryption storage by utilizing query mode recognition; constructing a cache fingerprint based on the encrypted object to verify the path integrity; and in response to data change, performing dynamic incremental calculation by using a credit potential energy conduction model in combination with a consistency verification result. According to the method, on the premise of ensuring data privacy and consistency, the calculation efficiency and evaluation accuracy of the large-scale credit graph are improved.

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.