68 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 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.

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 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 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.

This invention proposes a satellite-ground collaborative mission management method for Earth observation space cloud services, belonging to the field of satellite application and management technology. This method first identifies three main roles in satellite-ground collaborative mission management; secondly, it selects a management mode based on the urgency and complexity of the needs; then, it generates mission plans and instructions for the remote sensing satellite constellation and telemetry and control station network, and executes the missions; finally, after mission execution, data is received through the space edge cloud service center and the ground station network, thereby achieving management under a cloud architecture. This method overcomes the shortcomings of existing methods, such as difficulty in coordinating satellite-ground missions, difficulty in coordinating multiple types of users, and slow data transmission speeds. It improves the collaborative management performance of remote sensing satellite missions under a satellite-ground integrated system and can meet the application requirements of rapid, accurate, and comprehensive remote sensing satellite mission planning and management.

The application relates to the technical field of elevator Internet of Things data processing, and discloses an elevator Internet of Things data management and backup method and system under a hybrid cloud architecture, which comprises the following steps: collecting elevator terminal equipment original data streams on the edge side and processing the original data streams to generate pretreatment data; identifying data sensitivity according to a security rule library, encrypting high and medium sensitive data, and keeping low sensitive data in plaintext; dividing the data into three backup priority sets according to business criticality, and respectively distributing the data to an edge side local backup unit, a private cloud and a public cloud backup cluster after clustering based on access frequency and storage occupation; monitoring storage load, link state and backup progress, and scheduling resources when an exception occurs; and regularly recovering and practicing the backup data. Through data hierarchical processing, differential storage, dynamic monitoring and scheduling and regular practice, the application supports intelligent management of large-scale elevator cluster data.

The invention discloses a computing method of a single cloud architecture threshold multi-party privacy set intersection depending on trusted hardware and homomorphic encryption. The method relates to a task requester, a cloud server supporting the Intel SGX technology and a plurality of task participants. The cloud server is logically divided into a rich execution environment (REE) and a trusted execution environment (TEE) isolated by CPU hardware. The method comprises the steps that a task requester securely deploys private key fragments to a TEE through remote authentication; establishing secure connection between each participant and the server, and directly uploading the encrypted Bloom filter to the REE; performing homomorphic aggregation on the ciphertext by the REE, and calling an internal function of the TEE through a secure interface (ECALL) to jointly execute security comparison; and the TEE completes decryption and threshold comparison by using the private key fragmentation in the isolation region, and encrypts and returns a result. According to the method, the TEE is used for replacing a cooperation server in a traditional architecture, network communication is converted into memory interaction, the collusion risk of the server is eliminated, meanwhile, the communication overhead is remarkably reduced, and the calculation efficiency and privacy security are improved.

The invention discloses a digital resource secure storage and authorized access method based on a distributed cloud architecture, and relates to the field of digital resource secure storage, and the encryption method comprises the steps: generating a resource key based on a user identifier of an uploader of a resource file and random salt derivation; encrypting the header data by using the resource key to obtain header encrypted data; storing the main body data to an object storage server, and obtaining an access address of the main body data; generating resource index data of the resource file, and storing the resource index data into a main server; when the third party is allowed to temporarily access, generating an authorization record and signing and issuing an access bill; when the third party accesses temporarily, the access bill and the authorization record are verified, and an audit log is recorded; when the third-party temporary access needs to be revoked, updating a revocation state in the authorization record; the storage pressure of a main server is shared through distributed storage, and refined access control of encrypted resources is realized through authorization records, access bills and audit log mechanisms.

This invention discloses a hybrid cloud database integration and AI enterprise agent method for SMEs, including unified storage and event collection, cross-system data exchange and standardization, version and conflict handling, lifecycle and compliance archiving, industry-specific RAG and report generation, model training and validation deployment, and providing industry-specific intelligent applications in the engineering / manufacturing field such as semantic retrieval, intelligent question answering, and business prediction based on a finely tuned AIGC large model. This invention also discloses a hybrid cloud database integration and AI enterprise agent system for SMEs, including a foundation layer, a data flow layer, and an intelligence layer. This invention adopts a hybrid cloud architecture, combining local NAS and external MaaS platforms to achieve secure local storage of core data and elastic computing power support, solving the problems faced by SMEs in the informatization construction of engineering / manufacturing scenarios, such as chaotic data management, difficult system integration, high compliance risks, difficulty in implementing AI applications, and high cloud dependency costs.

The invention provides an intelligent driving simulation scene management platform and method and electronic equipment. The intelligent driving simulation scene management platform comprises a front-end application layer and a cloud end, the front-end application layer is used for responding to the operation of a user on the function module and sending a corresponding operation request to the cloud; wherein the function module comprises a classification management module and a scene management module, and the classification management module is used for constructing a classification scene database and a scene label library; the scene management module is used for labeling and classifying new scenes and performing scene retrieval; the cloud comprises a back-end service layer and a data storage layer; the back-end service layer is used for receiving the operation request sent by the front-end application layer and driving the corresponding function module to execute the corresponding operation based on the operation request; the data storage layer adopts a hierarchical storage architecture and a private cloud architecture to manage scene data; wherein the format of the scene data adopts a preset standard format. According to the invention, problems existing in existing automatic driving scene management are improved.

The invention relates to a comprehensive path optimization method for a wide-area cloud architecture network in the field of network cloud fusion. The method is particularly applicable to load balancing scheduling of a large number of continuous service flows (elephant flows) in a cloud architecture network. Comprising the following steps: monitoring a network state and identifying elephant flow, collecting information of each link, and detecting and identifying the elephant flow; topological path pre-screening and candidate path library construction improve the calculation and optimization efficiency. Initializing ant colony parameters, and formulating a multi-dimensional heuristic function; performing path exploration and probability selection, and generating an ant colony exploration calculation formula according to a target; dynamically updating pheromones, and searching an optimal path according to local pheromone updating and global pheromone updating; issuing an optimal path and updating a flow table; and continuous monitoring and dynamic adjustment are carried out. According to the invention, a dual control plane combining a routing protocol and an SDN protocol is designed, the improved ant colony algorithm based on network topology is adopted to optimize elephant flow path selection under a cloud network architecture, and the network global service transmission efficiency is improved.

The application discloses a kind of robot tactile perception and force error compensation systems for circuit breaker operation and maintenance, it is related to power equipment operation and maintenance automation technical field, including tactile perception module, using piezoresistive effect flexible sensor array, support 16-way data synchronous acquisition;Force error compensation module, integrated impedance control algorithm and multi-source error compensation model;System control module, using industrial grade embedded controller, support CAN bus and Ethernet communication;Environment adaptation module, deployment temperature and humidity and vibration sensor, dynamic adjustment parameter;Execution drive module, contain servo driver and adaptive clamping mechanism;Data storage and analysis module, using edge-cloud architecture;Safety protection module, with over force protection and insulation monitoring function.The application improves tactile perception precision and force error compensation effect, enhances complex environment adaptation capability;Through self-calibration and data optimization, keep long-term stable operation, adapt to different specifications circuit breaker operation and maintenance demand.

A distributed flow steering method in a cloud-based architecture includes a first server assigning a connection ID to each of several data flows owned by the first server. The connection ID uniquely identifies the first server and includes a server ID contained within the data packet header of each of the several data flows owned by the first server. The method further includes the first server identifying a data flow characterized by a data packet header containing a connection ID that uniquely identifies a second server as the owner of the data flow, and redirecting that data flow to the second server.

The invention discloses a multi-agent collaborative large model data distillation method and system under an edge cloud architecture. According to the method, firstly, training and inference are executed locally through an edge student Agent, and local knowledge information is extracted to obtain generator model parameters; secondly, the edge student Agent uploads generator model parameters to the cloud teacher Agent; then the cloud teacher Agent aggregates knowledge of each edge student Agent and updates teacher model parameters; then the cloud teacher Agent issues global knowledge to the edge student Agents to help distillation of cloud large model knowledge; and finally, the performance of each edge student Agent is improved through collaborative reasoning and collaborative strategies. According to the method, a teacher-student relationship network of a cloud large model and an edge small model is established, and knowledge is shared through a soft label and a feature alignment distillation mechanism; by using uncertainty weighting and distillation temperature self-adaption methods, the model precision and the training stability are ensured.

The invention discloses a two-wheeled electric vehicle charging pile fault prediction method and system. The method comprises the following steps: S1, constructing three-level data processing nodes; s2, collecting historical operation data of the charging pile through the charging pile client, and preprocessing the historical operation data; s3, performing equalization processing, training the fault prediction model, and sending the trained fault prediction model to a big data management platform for aggregation; s4, constructing a deep reinforcement learning agent for the fault prediction model and training the deep reinforcement learning agent; and S5, collecting real-time operation data through the charging pile client, performing real-time inference through the fault prediction model, and determining a dynamic fault prediction threshold. According to the method, three-level nodes of the charging pile client, the charging station data platform and the big data management platform are constructed, a data island of a single station is broken through by an end-edge-cloud architecture, and the value of multi-source data is fully excavated. The problems of poor generalization ability and high false report and missing report rate in the prior art are solved.

The invention provides a data grading sharing interface device and method in a vehicle and road cloud three-level cloud architecture, and belongs to the technical field of data security, and the device comprises an authority control module which is used for receiving a data access request, and carrying out the authentication of the request based on a preset three-level authority matrix, the three-level permission matrix is used for defining data access ranges of the edge cloud, the region cloud and the center cloud; the format conversion module is used for performing format conversion on the authenticated data and dynamically adjusting the data compression ratio based on the real-time network bandwidth; and the protocol interaction module is used for transmitting the data subjected to format conversion and compression through a request-response-acknowledgement mechanism based on an MQTT-SN protocol. According to the invention, the security and reliability of data access are improved, the data transmission efficiency is optimized, and the high requirements of vehicle-road cooperation, intelligent traffic and other application scenes on data security, real-time performance and reliability are met.

Techniques for generating, simulating, and optimizing one or more provider-specific cloud-based architectures from a provider-independent architecture definition are disclosed. An architecture generator maps provider-independent service definitions to provider-specific service components for one or more specific cloud service providers. An architecture simulator simulates execution of a set of operations on the provider-specific cloud-based architectures to determine one or more performance and cost metrics. An architecture optimizer varies one or more design choices or parameters of a provider-specific service component to suggest which variant is optimal with respect to an optimization objective.

The invention discloses an incontinence care closed-loop system and method based on perception to service mapping. The incontinence care closed-loop system comprises a sensing acquisition module, a data analysis module, a service mapping module, a service execution module, a care rule base, a database, an AI model, a feedback loop and a nurse intelligent terminal. The system directly converts sensing data into a nursing service instruction by sensing service mapping, and automatic judgment and intelligent scheduling of excrement monitoring, body position monitoring, paper diaper replacement and turning-over nursing are achieved; multi-task linkage execution, task priority, responsibility nursing personnel priority, merging scheduling and AI optimization are supported; an edge-cloud architecture is adopted, and the system is adaptive to multiple scenes of families, old-age care institutions and door-to-door care. According to the invention, automatic closed loop of the whole incontinence nursing process is realized, the nursing timeliness, efficiency and safety are improved, and the labor cost and the disturbance to the nursed person are reduced.

This invention discloses a cloud-based data processing system for book data acquisition, belonging to the field of data processing. The system includes: a data acquisition module, a cloud storage module, a data processing engine, an intelligent analysis module, and a security protection module. The data acquisition module supports automatic acquisition and preprocessing of multi-source heterogeneous book data; the cloud storage module adopts a distributed storage architecture to achieve highly reliable data storage and elastic expansion; the data processing engine, based on the MapReduce framework, performs parallel processing and cleaning of the acquired book data; the intelligent analysis module utilizes natural language processing and machine learning algorithms to deeply mine and classify book content; and the security protection module implements data encryption, access control, and privacy protection. This invention achieves efficient acquisition, processing, and analysis of book data through a cloud architecture, solving the problems of low efficiency and poor scalability in traditional book data processing, and has advantages such as strong processing capabilities, low storage costs, and high intelligence.

The invention discloses an agent development and safe operation method and system based on a private cloud. The method comprises the following steps: firstly, constructing a multi-level security virtual private cloud architecture; secondly, deploying a containerized agent platform in each security domain, creating an application through a micro-service architecture, binding an exclusive knowledge base, and establishing an application-knowledge base one-to-one security channel based on digital signature; thirdly, implementing a fine-grained permission management and control mechanism, dynamically verifying a data security level label by adopting an access control model, and guaranteeing the security of sensitive information in combination with a privacy technology; then integrating a cue word optimization engine to improve the interaction performance of an agent, and applying a dynamic load balancing algorithm to realize efficient scheduling of a large model cluster; and finally, constructing an end-to-end security protection chain through an AK / SK authentication system. According to the method, the secret-related intelligent agent is managed and controlled from multiple angles, safe development and efficient operation of the intelligent agent in the secret-related environment are achieved, and it is guaranteed that secret-related knowledge information is safe and controllable.

The invention discloses a task processing method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring a to-be-reasoned task and corresponding environment task joint perception information; under the condition that the environment task joint perception information meets a preset condition, analyzing the to-be-reasoned task by using a target small member model in a pre-trained family model to obtain an intermediate handover packet output by a target key intermediate layer of the target small member model; and analyzing the intermediate handover packet by using a target large member model in the family model to obtain a first answer output by a task output header of the target large member model, and outputting the first answer as a target answer of the to-be-reasoned task. According to the method and the device, the technical problems of low analysis efficiency and serious waste of computing resources when an end-side reasoning task is analyzed by adopting an end-side cloud architecture in related technologies are solved.

This invention belongs to the field of power system security protection technology, and relates to a centralized digital management and control method for substation anti-misoperation devices based on cloud services. It establishes a centralized monitoring platform with a layered cloud architecture, acquires data from anti-misoperation devices in real time through intelligent data acquisition devices, processes the data through edge computing devices, and uploads it to the cloud platform. Utilizing five-prevention rules and the cloud platform to form a dual verification mechanism, it establishes a full lifecycle security protection system covering dual verification of device access, encrypted data transmission, and dual-person joint review of operation permissions. This solution effectively solves the technical problems of traditional anti-misoperation devices, such as scattered data management, low efficiency of manual verification, high risk of unauthorized access, and difficulty in operation traceability. It achieves centralized monitoring, intelligent verification, and dynamic security management of substation anti-misoperation devices, significantly improving the operation and maintenance efficiency and operational reliability of power systems, and providing reliable technical support for the construction of new power systems.