72 results about "Collaborative computing" patented technology

This invention provides a cross-institutional collaborative data computation method based on secure multi-party computation, belonging to the field of data security and privacy protection technology. The method includes: each participating institution registering its identity and data metadata; the task initiator publishing a computation task description; performing operator-level decomposition on the objective function to obtain efficiency, overhead, and security strength indicators for each candidate protocol; using the security strength indicators to correct the efficiency and overhead indicators, weighted summing to obtain a comprehensive score, selecting the optimal protocol accordingly, and generating a hybrid protocol graph and execution plan; performing collaborative computation in stages, with protocol conversion at transition points; finally, the share is sent to the settlement node, aggregated and reconstructed to obtain plaintext results, and generating verifiable proofs. This invention achieves dynamic adaptation of protocols and tasks, optimization of security and performance balance, verifiable results, and dynamic adaptive scheduling.

This invention discloses an edge-cloud collaborative computing and dynamic scheduling system and method for omnidirectional robots, relating to the field of edge-cloud collaborative technology. The system includes: during industrial operation of the omnidirectional robot, acquiring torque, pose, and control commands at the edge and aligning them in time to form a sequence of key operational states; generating predictive features through sliding time window differential and time-weighted aggregation; comparing the predicted features with historical normal features to obtain predicted trigger values; initiating high-frequency uploading of key data when the trigger value exceeds the local risk reference band of the corresponding operational stage; the local risk reference band is constructed in stages by historical similar devices; edge nodes cache data according to operational stages and issue upload and retention strategies to achieve adaptive data scheduling under edge-cloud collaboration. This solves the problems of delayed risk information transmission and difficulty in timely capturing the early risk evolution process of industrial operations caused by existing abnormal trigger-based uploads.

The application discloses a kind of anonymous routing methods of double-layer satellite network for delay-sensitive service, it is applied to VLEO / LEO double-layer satellite network, this network is based on software-defined network SDN architecture implementation, including data plane, control plane and application plane;Data plane includes VLEO layer, VLEO / LEO cross layer and LEO layer;Application plane is deployed with the routing method of the application upper;The method comprises: constructing layered decoupling routing calculation framework, to divide routing decision into including LEO layer inner routing, VLEO / LEO cross layer routing and VLEO layer inner routing multilayer path selection;Using the collaborative computing capability of SDN controller and satellite node, multilayer path selection is reconfigured and weight disturbance in real time, generates optimal routing path.The application can effectively reduce end-to-end delay, balance network load, improve the real-time performance, robustness and security anonymity of double-layer satellite network.

This invention discloses a heterogeneous government data collection and analysis system based on data governance and privacy protection, belonging to the field of intelligent analysis technology. The system includes: a data collection and cleaning module, which intelligently collects and cleans heterogeneous government data using adaptive algorithms; a privacy protection and dynamic desensitization module, which achieves comprehensive data privacy protection through dynamic desensitization technology and secure multi-party computation technology; an intelligent analysis and collaborative computing module, which, by introducing federated learning technology, enables multi-party collaborative model training without sharing original data, and shares the learned model parameters to achieve cross-departmental and cross-regional joint data analysis; and a data sharing and management module, which establishes a cross-departmental data sharing and verification mechanism through blockchain technology to ensure the transparency and traceability of data exchange. This invention can achieve efficient and intelligent government data collection, privacy protection, and analysis with low resource consumption.

The application relates to a cloud-edge collaborative computing framework and processing method for multi-modal data stream fusion processing, which comprises the following steps: S1: based on the original data stream collected by the edge computing node, an improved Wiener filtering algorithm is used for noise suppression, signal denoising is realized through adaptive threshold wavelet transform, and a time stamp alignment technology is used to solve the time delay difference problem of multi-modal data, and a space-time aligned and purified data stream is generated. The application has the effects that through the combination of the improved Wiener filtering algorithm and the adaptive threshold wavelet transform, the noise suppression efficiency of the original data stream is significantly improved, the time stamp alignment technology effectively solves the time delay difference of multi-modal data, the generation of the space-time aligned and purified data stream ensures that the subsequent processing has a unified time sequence reference, and the space-time attention fusion network adopts the collaborative architecture of a bidirectional gate recurrent unit and a lightweight 3D convolution network.

This invention provides a method and system for constructing a smart elevator edge digital twin based on cloud-edge collaboration. The method includes: acquiring environmental information of the vertical elevator's operating scenario; generating a meta-model file based on the environmental information parameters, the meta-model file including an elevator shaft model, an elevator subsystem model, interface files, and a canvas file; acquiring current subsystem data of the vertical elevator; uploading and writing data to an edge module near the subsystem based on dynamic parameter data and static dimension data collected from control cabinets near the subsystem, thus obtaining an edge data center for the elevator subsystem; matching the model-driven data and state decision data processed by the edge computing module with the corresponding model component names under the general file package to form an edge digital twin model, and sending the processed data form to a cloud server; pulling the digital twin data and general file package of the vertical elevator from the cloud server to the digital twin front end, and displaying the elevator's operating status and early warning results in the digital twin model; realizing the coordinated implementation of vertical elevator equipment under cloud-edge collaborative computing and digital twin technology, improving the effective information extraction capability and real-time monitoring level of the monitored equipment, and better preventing potential safety threats to elevator equipment.

This invention relates to the field of cloud computing technology, and particularly to a cross-domain data collaborative computing method based on secure multi-party computation and differential privacy. The invention proposes the following scheme: the task initiator abstracts the collaborative task into an intent description object and compiles it into an intermediate representation with gating logic, generating a dense-state function load for joint evaluation by multiple parties; the task assisting end generates real local contributions and counterfactual comparison contributions locally based on private data, and forms a detection score according to the request trajectory, adaptively determining the mixing coefficients. The two types of contributions are then subjected to restricted mixing and direction correction processing before participating in the dense-state computation. This application, without changing the appearance of the evaluation protocol, balances task intent protection and sensitive subgroup existence protection, improving security and engineering usability in cross-domain collaborative scenarios.

The present application relates to a CPU / GPU collaborative computing power scheduling method based on task granularity division, belonging to the technical field of computer architecture and heterogeneous computing scheduling, which solves the problems of coarse task division granularity, lack of interconnection state dynamic sensing ability, rigid computing power resource allocation and large cross-device communication overhead in the prior art when CPU / GPU cooperates to execute. The method comprises: performing task granularity division on a heterogeneous computing task to obtain a plurality of independently scheduled subtasks and data dependency relationships between the subtasks; determining the comprehensive scheduling cost of each subtask on each candidate execution device based on the real-time running state information of the system, the subtasks and the data dependency relationships between the subtasks; determining the optimal execution device of the current subtask according to the comprehensive scheduling cost of each subtask on each candidate execution device, and allocating each subtask to the corresponding optimal execution device; wherein the execution device in the system is a CPU or a GPU.

The application discloses an edge-cloud collaborative computing power scheduling architecture and a low-power consumption management method, and relates to the technical field of edge computing.The application collects multi-dimensional resource indexes of distributed edge computing nodes, calculates resource availability scores through standardized processing and weighted comprehensive score calculation; extracts static code features and dynamic running features of tasks, adopts a gradient boosting decision tree algorithm to predict execution time, CPU occupancy, memory requirement and network transmission data volume of the tasks, and performs node performance correction; adopts an improved non-dominated sorting genetic algorithm to solve a multi-objective optimization problem, and generates a task offloading decision; the application solves problems such as difficulty in resource quantification under an edge computing environment, lack of accurate prediction in task scheduling, poor single-objective optimization effect, and unintelligent power consumption control, realizes efficient task scheduling and low-power consumption operation, and has good scalability and adaptability.

The application relates to the technical field of electronic communication, and particularly provides an audio indoor positioning base station system based on multi-parameter fusion. The system comprises an audio signal collection, preprocessing and feature extraction module, a local lightweight fusion module, a cloud collaborative computing engine and a positioning result output module. Through dynamic unloading of a computing load to the cloud and adoption of a lightweight neural network model for local preliminary positioning, intelligent switching of a cloud collaborative mode and a local autonomous mode is realized in combination with a dynamic load decision unit, so that the positioning precision and system robustness are guaranteed while the base station computing complexity and power consumption are reduced.

This invention discloses a self-attention trajectory learning and dynamic simulation system for urban digital twins, involving the fields of urban digital twins, artificial intelligence, and computer simulation technology. It includes a perception layer, a data preprocessing layer, a self-attention trajectory learning layer, a dynamic simulation engine layer, a decision and optimization layer, and an interface and service layer. Based on a hierarchical spatiotemporal sparse attention network, this system combines graph attention networks with a local-global hybrid sparse attention mechanism to efficiently capture long-range spatiotemporal dependencies of urban mobile entities and reduce computational complexity to near-linear levels. Combined with an edge-cloud collaborative computing architecture and a parallel simulation engine, it achieves high-precision prediction and low-latency dynamic simulation of large-scale urban mobile entity trajectories. This effectively solves the problems of insufficient spatiotemporal dependency modeling, low computational efficiency, and poor adaptability to concept drift in existing technologies, providing strong technical support for applications such as smart city traffic management and emergency response.

This invention discloses a method and system for dynamic task allocation in edge-cloud collaborative computing for the entire CNC machining process. The system includes a process analysis module for real-time identification of process boundaries and technological features; a machining task perception module for automatically generating a list of various computing tasks required for the current process based on process features (including tool wear monitoring, spindle vibration monitoring, thermal error compensation, machining parameter optimization, workpiece quality prediction, contour error compensation, etc.); a process-driven dynamic decision engine for independently deciding whether to allocate each computing task to the edge or the cloud; and a CNC instruction collaboration module for converting the decision results into compensation instructions and writing them into the CNC system. This invention combines process flow features with various computing task types throughout the CNC machining process, achieving refined dynamic allocation at the process and task levels. It also ensures task continuity through the migration of intermediate computing results during process switching, making it suitable for various intelligent application scenarios throughout the entire CNC machining process.

The application belongs to the field of Internet of Things collaborative computing, and discloses an analysis method and system for accurate cooperation between Internet of Things terminals, which is suitable for Internet of Things scenes such as intelligent agriculture. The method builds an Internet of Things cluster containing high-power and low-power machines, and runs a bidirectional PIM multicast protocol between routers. The system realizes device state synchronization and assisted path pre-construction through an improved IGMP / PIM protocol. When a low-power machine initiates a task request, it can be accurately scheduled to a high-power machine according to the pre-built path. A state-triggered update mechanism is adopted, which only triggers global state revocation when all subordinate devices are busy, greatly reducing network overhead. The system realizes efficient power cooperation between devices, significantly improving task response speed and resource utilization.

The application discloses an automatic server operating system installation and configuration system and method, which is provided with an image management module and a configuration template library, can store various operating system images, provides predefined templates and supports user customization, faces complex scenes, and can be flexibly selected by the user to adapt to images and templates, quickly adapt to new business scenes, and solve the problem that the prior art is difficult to meet complex scenes; meanwhile, with the aid of an automatic collaborative computing model, data interaction and collaboration of each module are realized, an automatic installation engine calls an API to automatically install and configure, efficiency is improved, manual errors and delays are reduced, and business is ensured to be timely and stably online during large-scale deployment; secondly, parameters are accurately analyzed by the automatic installation engine, servers are accurately configured according to rules and requirements, various configurations are accurately set according to templates for different types of servers, the optimal configuration of servers is ensured to adapt to business requirements, and a customized configuration effect is accurately achieved.

The present application relates to the technical field of big data, and particularly to an Internet of Things edge node big data collaborative processing system, which comprises: collecting real-time running state data of each edge node in an Internet of Things edge computing system, wherein the real-time running state data comprises a task queue backlog length, a node residual energy parameter and communication channel state information, and a multi-dimensional queuing system model is constructed based on the real-time running state data.In the present application, a time interval sliding window is used to capture data distribution characteristics, which can identify network jitter and real node failure precursors.A node failure dynamic risk assessment index is calculated based on the data distribution characteristics, which quantifies the node health condition, and task migration is triggered immediately when the risk index is abnormal, a predictive takeover mechanism is used to ensure the continuity and integrity of big data processing tasks, eliminate the risk of data loss caused by sudden downtime of edge nodes, and improve the robustness of the collaborative computing architecture.

The application discloses a kind of construction safety log intelligent filling system, it is related to building information technology field, the system includes multidimensional field perception layer, multi-agent collaborative computing layer, semantic modeling and conflict resolution layer, safety risk reasoning layer and log automation generation layer.Multidimensional field perception layer real-time acquisition original data stream and carry out time and space marking;Multi-agent collaborative computing layer extracts feature and local decision by multiple heterogeneous function agents in parallel;Semantic modeling and conflict resolution layer exports consistent semantic event by construction safety ontology library and collaborative filtering mechanism;Safety risk reasoning layer executes logical reasoning to identify irregular events in combination with knowledge graph;Log automation generation layer encapsulates and translates into standardized text.The application effectively solves the problem of manual filling omission, realizes the active inference and automatic generation of log, and ensures the consistency of data processing logic.

The application discloses a multi-screen visualization collaboration method and system based on agents, and related devices, relates to the technical field of human-computer interaction, and the method is applied to a portable computing device and comprises the following steps: receiving and analyzing a task instruction input by a user to generate a standardized task request; constructing a plurality of agents in a local mode to form an agent cluster, generating a task directed acyclic graph based on the task request; binding the plurality of agents to different display terminals one by one, and when detecting that the load of a first agent is too high, migrating part of a task of the first agent to a second agent with lower load, synchronously updating the display content of the corresponding display terminals; triggering human-computer interaction when a preset key decision node is reached; and after the task is completed, auditing and finally processing and storing the results. In this way, the application realizes the visualization presentation of the multi-agent collaborative computing process, load balancing and human-computer interaction intervention at key links, and improves the reliability and efficiency of the agent in processing complex tasks.

This application relates to the field of drawing management technology and discloses a method and system for estimating man-hours for batch drawing upgrades. It includes: a data access layer for receiving multi-source data associated with the drawing upgrade task; an intelligent parsing module for constructing a structured model of the drawing pages; a drawing change cascading impact analysis module for identifying affected drawing pages and operation units; a case retrieval module for retrieving historical similar cases of the current drawing and obtaining reference man-hour data; a multi-agent collaborative computing module for calculating the preliminary man-hour prediction result of the current drawing task; and an uncertainty quantification output module for calculating the confidence interval of the preliminary man-hour prediction result using a preset uncertainty quantification model and outputting a man-hour estimation report. This application significantly shortens the work cycle for estimating man-hours for batch drawing upgrades and greatly reduces labor costs and human error.

The application relates to the technical field of power equipment detection, in particular to a power transmission line insulator zero value detection system and method based on a UAV, which comprises a pre-judgment and multi-source detection module, an edge node control and device storage module, a central control and remote control module and a system disaster recovery and auxiliary module; the application breaks through the traditional single inspection mode, carries out risk early warning through a fixed monitoring unit, guides the UAV to realize "on-demand precision inspection", and innovatively adopts an "edge-cloud" collaborative computing architecture, carries out real-time preprocessing and feature purification on multi-source data at the UAV end, effectively compresses the data volume, improves the response speed, finally fuses multi-source heterogeneous data in the cloud for deep intelligent diagnosis, and the whole-chain optimization and collaboration are realized from task generation, data acquisition to analysis and diagnosis, so that the overall efficiency, accuracy and real-time performance of the inspection operation are significantly improved.

This invention belongs to the field of edge computing technology and discloses a lightweight AI model adaptive deployment system for edge computing. It constructs a dependency strength matrix by accurately quantifying data dependencies between model layers, monitors network stability in real time and identifies abnormal periods, calculates communication penalty factors and bandwidth attenuation factors based on latency peak distribution characteristics, and then constructs a communication overhead prediction model for collaborative computing between edge devices. The system innovatively generates a multi-granularity model segmentation candidate scheme set and identifies segmentation boundaries that trigger resource contention by monitoring computational load fluctuations and memory usage changes. It dynamically adjusts the model partitioning granularity and mapping strategy based on the gradient transmission volume and activation value size at the segmentation boundaries. This invention reduces cross-device communication overhead, improves the balance of computational resource utilization, and ensures real-time response capabilities in latency-sensitive scenarios.

The present application relates to the technical field of wind farm monitoring and management, and specifically discloses a wind farm profile virtual-real fusion real-time reconstruction method and system. It includes five stages: first, the task is adaptively distributed through the heterogeneous edge-cloud collaborative computing node cluster; second, the wind farm is divided into semantic paragraphs and marked with value based on the characteristics of terrain, wake and power gradient; third, the semantic fracture index is calculated to judge the profile stability, and the reconstruction instruction is generated when needed; fourth, the differentiated visual reconstruction is performed according to the value label, and the double-path robustness check is carried out in parallel; finally, the system parameters are optimized in a closed loop combined with the interactive feedback and the check results. This method realizes the whole process from data acquisition, semantic understanding, intelligent reconstruction to continuous optimization, and improves the real-time performance, accuracy and adaptive ability of the wind farm visualization analysis.

This invention relates to the field of spatiotemporal data processing technology, and discloses a method, system, terminal, and storage medium for predicting multi-dimensional urban indicators based on semi-supervised learning with small samples. The method includes: extracting visual, textual, and structural features of urban space using a three-branch encoder, and generating a unified urban region representation vector using a multimodal fusion mechanism; subsequently, constructing a semi-supervised collaborative computing framework that integrates a heterogeneous neural network model and an ensemble tree model as a regressor; and, under conditions of a small number of labeled samples, using an alternating pseudo-label generation and four-fold pseudo-label selection mechanism, performing semi-supervised augmentation training on the model with massive amounts of unlabeled data to improve the accuracy of spatiotemporal data labeling. This invention significantly reduces the dependence on labeled data and achieves high-precision labeling prediction across multiple dimensions of urban indicator calculation.

A mobile edge task offloading method based on a sandcat-based collaborative computing strategy is proposed. This method proposes a two-step offloading computing strategy combining user collaboration and UAV assistance: First, considering the user communication coverage, an improved K-Means clustering strategy is used to cluster maritime users, achieving collaborative offloading and task integration among users. Second, a mobile edge computing architecture assisted by a single UAV is constructed. An optimization model is established with task processing latency as a constraint, aiming to minimize the weighted total energy consumption of the UAV and users. Based on this, a joint trajectory optimization and task offloading algorithm are used to solve the model. This invention can effectively alleviate maritime communication network congestion, significantly reduce system weighted energy consumption while meeting latency requirements, and achieve full utilization of communication and computing resources.

The application belongs to the technical field of electric power data security and data sharing, and discloses an electric power data security sharing method based on privacy calculation and related equipment. The hybrid storage system solves the problem of limited storage capacity of the blockchain. The on-chain storage metadata guarantees traceability, and the off-chain storage encrypted data improves storage efficiency. The improved BFT consensus algorithm guarantees the consistency of the distributed nodes and solves the trust problem of cross-organization data sharing. The collaborative computing protocol of secret sharing and homomorphic encryption takes into account the privacy protection strength and the computing and communication efficiency. Compared with the traditional single privacy calculation scheme, the computing efficiency is improved, the communication cost is reduced, the proxy re-encryption technology realizes controllable sharing of data, and the compliance requirements are met. The application can effectively support sensitive data sharing scenes such as electric power load prediction and cross-department collaborative analysis, and can guarantee that sensitive information such as power grid topology and user power consumption behavior is not leaked.

This invention relates to the field of smart grid inspection and edge computing technology, specifically to a cloud-edge collaborative computing resource balancing scheduling system for unmanned vehicles used in converter station inspections. It includes modules for collaborative management and control, risk mapping, demand prediction, resource scheduling, and feedback. The system identifies computing power sensitive areas and passage zones through physical entropy value correlation analysis. Its core is to combine scenario risk coefficients with visual semantic pre-simulation to determine instantaneous computing power peaks and demand types. When strong real-time, heavy computation, or low-risk signals are generated, edge exclusive locking, cloud high-priority slice offloading, or energy-saving degradation strategies are executed respectively. This invention changes the current scheduling situation that relies solely on CPU load, ensuring that resources are preferentially tilted towards high-risk areas, and realizing dynamic reconfiguration of computing power resources.

The application discloses a cross-center collaborative task dynamic planning method based on a pipeline mechanism, and steps of the method comprise the following steps: 1) according to a collaborative requirement, arranging a data source and a collaborative model algorithm, and configuring a cross-center collaborative computing task; 2) dividing the cross-center collaborative computing task into subtasks according to whether data migration behavior will be generated, and forming a DAG (Directed Acyclic Graph); 3) according to the DAG, dividing different data sources into different subtasks; 4) when a subtask T is allocated with a data source D, the subtask T is sent to a data center where the data source D is located to be executed; for a subtask T' which is not allocated with a data source, according to execution conditions of an upstream subtask of the subtask T' and resource conditions of each data center, a data center for executing the subtask T' is determined, and then the subtask T' is sent to the corresponding data center to be executed. The application realizes efficient arrangement and execution of cross-data-center collaborative tasks for complex analysis scenarios.

The application belongs to the field of computing power network and information security, and relates to a C4I computing power network trusted access and computing method, device and computer equipment.The method comprises the following steps: fusing a C4I command link and a trusted computing power network, constructing a task scene driven hierarchical access and collaborative scheduling system, and outputting a task priority quantitative value; using a trusted execution environment, a national secret algorithm and a quantum key distribution full-stack trusted mode to construct a hardware level security base; constructing a dynamic trust evaluation model, calculating a real-time trust value and realizing adaptive iteration of a security strategy; realizing safe and elastic scheduling of computing power resources; using a Double DQN reinforcement learning algorithm for global collaborative optimization, dynamically adjusting a security strategy, a scheduling strategy and an elastic scheduling strategy; according to the strategy execution effect, constructing a multi-scene quantitative adaptation model to realize adaptation of different infrastructure scenes and cross-scene collaboration. The application realizes trusted access and collaborative computing of a high-security, high-real-time and high-collaborative computing power network.

The application provides a regional new energy hierarchical diagnosis system based on big data cloud edge-end collaborative calculation, relates to the technical field of power diagnosis, and comprises the following: a data preprocessing unit for preprocessing multi-source state data; a preliminary diagnosis unit for determining a preliminary abnormal device; a model construction and solving unit for determining key abnormal devices and root causes affecting regional performance indexes based on adjoint theory; a strategy determination unit for determining an optimal operation strategy of the device; and a strategy evaluation unit for generating a strategy effect evaluation report and encrypting and uploading the report to the cloud to realize continuous optimization of cloud edge-end collaboration. The application realizes accurate positioning of equipment-level hidden faults of regional new energy field groups to active early warning of system-level operation risks, finally generates and executes a globally optimal collaborative control strategy, forms a closed loop of diagnosis, decision-making and optimization, and significantly improves the comprehensive operation efficiency of regional new energy assets.

This invention discloses a cloud-edge collaborative algorithm scheduling method and apparatus, relating to the field of cloud-edge collaborative computing technology. The method includes: initializing a resource monitoring module on the edge when the intelligent camera algorithm starts, sampling and updating multiple runtime resource occupancy indicators at a preset period; generating a comprehensive evaluation index characterizing resource sufficiency based on preset weights using a state calculation module and caching it; and encapsulating and uploading the comprehensive index and original index to the cloud side using a dual-mode approach of periodic reporting and event-triggered reporting. After the cloud-side receiving module parses the cache, the state evaluation module determines the computing power status based on dual thresholds, and the algorithm scheduling module adaptively executes scheduling, realizing dynamic algorithm scheduling for edge-cloud collaboration, effectively improving resource utilization and service stability. This invention achieves efficient collaboration through edge-cloud adaptive scheduling and resource contention avoidance, solving problems such as evaluation distortion and scheduling lag in traditional solutions.