445 results about "Edge node" patented technology

The application provides an industrial Internet of Things method and system based on edge computing, and relates to the technical field of industrial Internet of Things.The method comprises the following steps: through a historical welding data request of a welding robot edge node, including vibration, temperature and current parameter acquisition frequency, time period dynamic acquisition and statistical distribution rule analysis are carried out, and the number distribution of requests of welding quality analysis services and equipment state monitoring services in the next period is predicted.The application realizes real-time response of welding quality analysis through multi-dimensional data prediction and dynamic resource scheduling, generates a service deployment scheme of adaptive load change under the guarantee of millisecond delay and energy efficiency constraint by combining horizontal cooperation unloading and multi-objective optimization algorithm, and improves the edge computing reliability and service continuity of the industrial welding scene.

The embodiment of the application discloses a kind of traffic network scheduling control method, system based on space-time big data, electronic equipment, belong to the technical field of electronic data processing, method steps include: the following steps are executed in edge side: receiving and analyzing terminal raw data, filtering and trajectory generation using the rules issued by cloud end;Call fusion engine to perform space-time alignment and matching on multi-source trajectory, generate fusion trajectory;Based on fusion trajectory, traffic flow, average speed are calculated synchronously, and input to event risk assessment model to output risk probability value;After comparing risk probability value with threshold, event level is generated and corresponding alarm is triggered.In cloud side, server converges multi-dimensional information uploaded by each node for macro analysis and model iteration optimization, and periodically issues updated strategy and configuration to edge node to dynamically adjust its processing logic, so as to realize intelligent traffic control from cloud end global optimization to edge real-time execution.

This invention relates to the field of multi-source data processing technology, specifically disclosing a data governance method and system for multi-source heterogeneous data fusion. The method includes receiving unstructured data fed back by a data acquisition agent, performing virtual mapping on the unstructured data, recursively adjusting the access parameters corresponding to the data source node, and generating simulation data based on the virtual mapping when a data source node experiences a data upload anomaly. It also includes receiving data increment density and data hotspot distribution fed back by the data acquisition agent, planning the scheduling path of the unstructured data parsing end based on the data hotspot distribution, and receiving the parsing results fed back by the parsing end. This invention acquires unstructured data by deploying edge nodes, identifies the unstructured data, constructs a data domain model, synchronously determines the data state of the unstructured data, marks hotspot areas in the data domain model, and then determines the parsing order, enabling important data to be parsed faster.

This invention relates to the fields of machine learning and edge computing technology, specifically to a blockchain-based trusted distributed hybrid expert model edge computing system. The system includes a task publisher for publishing learning tasks, which may be training or inference tasks; a blockchain network for receiving learning tasks and evaluating expert weights to obtain sparse activation expert index results; a blockchain network for receiving learning task processing results from the edge layer and achieving consensus; a gating network deployed within the blockchain network; an edge layer for downloading the corresponding expert model from the storage layer based on the sparse activation expert index results to process the learning tasks and uploading the processing results to the blockchain network; and a storage layer for storing all expert networks, each with a CID. This invention significantly improves the defense capability of the distributed MoE network against malicious edge node data tampering attacks, thereby ensuring the learning performance of the MoE system.

This application discloses a system login method, an edge node device, and a computer-readable storage medium for performing system login while improving system login security. The method includes: generating a random password; setting the login password of the target system as the random password; logging into the target system using the random password; and changing the login password of the target system so that no device can obtain the changed login password.

A task distribution rationality judgment method and device, equipment, storage medium and program product are disclosed, a target task area is divided into at least two sub-areas, the target task area is the overall task range of large model training under a cloud edge collaboration architecture; the area parameters of each sub-area are obtained respectively, the area parameters include at least two types of feature data; for the area parameters of each sub-area, the area parameters are subjected to quantitative operation respectively, and at least two types of feature evaluation values corresponding to each sub-area are obtained; based on the feature evaluation values corresponding to all sub-areas, the collaborative distribution rationality value of the target task area is calculated; when the collaborative distribution rationality value is greater than a preset reasonable distribution threshold value, it is determined that the task distribution of the target task area is in an abnormal state. By adopting the embodiment of the present application, the task multi-element characteristics and the all-around state of the cloud edge node can be comprehensively evaluated, so that the rationality of the large model training task distribution can be accurately judged, and the large model training efficiency is improved.

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.

This invention discloses a cloud-edge-device intelligent collaborative system and method for secure cross-network transmission, comprising terminal devices, edge nodes, an edge cloud platform, and a central cloud platform: The terminal devices collect production data from oil and gas field operations and upload the data to the edge nodes; the edge nodes upload the production data to the edge cloud platform and perform AI inference and execution based on received container images or model files; the edge cloud platform preprocesses the production data based on preset cloud-edge collaborative rules and transmits the processed production data to the central cloud platform via a northbound security gateway; and deploys the received container images or model files to the corresponding edge nodes; the central cloud platform trains AI algorithms based on the production data to obtain container images or model files, which are then transmitted to the edge cloud platform via a southbound security gateway. This system enables secure sharing and intelligent collaborative scheduling of oil and gas production data.

The application provides a ship identification model training method, a cloud server and a ship identification system. The method comprises the following steps: inputting sample images of ships of multiple models as a first training set into an initial ship identification model, and determining a first loss value of the initial ship identification model under initial model parameters; inputting the first loss value into the initial ship identification model in reverse, adjusting the initial model parameters and iteratively executing until the first loss value output by the initial ship identification model meets a preset condition, and obtaining a target ship identification model; inputting multiple new sample images of a new ship as a second training set, performing incremental learning and updating of the target ship identification model according to the second training set and a pre-training language model, obtaining an updated ship identification model, and sending the updated ship identification model to each edge node device, so that each edge node device performs ship identification based on the updated ship identification model, and the ship identification precision is improved.

This invention discloses a time-constrained, secure, and controllable data flow method in a cloud-edge environment, belonging to the field of mobile edge cloud computing. Specifically, it involves: First, establishing a communication scenario including an authorizing agency for both the sender and receiver, the sender, edge nodes, a cloud server, and the receiver. Given security parameters, the authorized agency for the receiver runs a global initialization algorithm, outputting public parameters and domain public / private keys. Then, the authorized agency for the sender inputs the public parameters and domain public key, outputting its own domain public / private key. For each sender, an access structure and a valid time interval are assigned, and an encryption key is generated. For each receiver, an attribute set is assigned, and a decryption key is generated. Finally, the sender runs the encryption algorithm and outputs the original ciphertext; the edge node verifies the sender's sending authority and outputs the cleaned ciphertext; and the legitimate receiver decrypts and outputs the plaintext. This invention meets the requirements for secure and controllable data flow in a cloud-edge environment.

This invention belongs to the field of electronic digital data processing technology, specifically relating to a low-altitude communication data anomaly detection system and method based on edge intelligence. The system parses protocol fields from the raw bitstream of the low-altitude communication link layer, extracts type identifiers and payload lengths to construct a protocol state machine transition matrix, and utilizes a Long Short-Term Memory (LSTM) network to extract state transition probability sequence features. Geographically adjacent edge nodes are set as micro-federated learning groups. Each node, after training using local features, only uploads network gating weight parameters to its neighboring nodes for weighted aggregation to update its local model. An alarm is triggered when the real-time state transition probability sequence deviates from the normal transition matrix and exceeds a preset topology threshold. This invention can identify protocol state machine logic errors and transition anomalies, reducing the amount of communication data required for collaborative model updates between edge nodes.

This invention belongs to the field of smart logistics technology and discloses a method for cost and carbon emission collaborative optimization for hubs and regional warehouse networks. It constructs a three-dimensional global collaborative optimization model of cost, carbon emission, and timeliness, and adjusts target weights in real time through a dynamic weight adaptive mechanism. It integrates cost and carbon emission elements across the entire supply chain to establish a three-flow linkage, coupled with a multi-dimensional constraint system. This breaks down optimization barriers in warehousing and transportation, and dynamically adapts to regional carbon emission quotas and cargo urgency. While ensuring timely order fulfillment, it achieves the collaborative goal of minimizing overall cost and keeping carbon emissions within quota limits, balancing economic benefits and environmental responsibility, and enhancing the overall competitiveness of the supply chain. Through edge nodes, it achieves heterogeneous data cleaning and standardization, dynamically calibrates carbon emission factors and quantifies them into unified units, and completes distributed storage, access control, and full-chain traceability through a consortium blockchain, ultimately constructing a digital twin data foundation.

The application discloses a high-density stereoscopic warehouse dynamic storage space allocation method for WMS, and relates to the technical field of artificial intelligence. Stereoscopic warehouses are arranged along the direction of the roadway, and stackers, shuttles and hoists are arranged in the stereoscopic warehouses. Field programmable gate array acceleration cores are arranged in the edge nodes of the stereoscopic warehouses. The acceleration cores are interconnected with the WMS through a time-sensitive network. The method comprises the following steps: step 1, the acceleration core constructs a beat dependency graph based on the current carrying instruction issued by the WMS, wherein the carrying motion is taken as an event node, and the action connection relationship is taken as a directed edge; step 2, the acceleration core constructs a colored Petri net; and step 3, the acceleration core constructs a graph attention reinforcement learning decision module and takes a heterogeneous graph as input, and the WMS drives the corresponding carrying equipment to perform the actions of warehousing, moving and replenishment. The application can significantly improve the system throughput, reduce the equipment idle rate and beat jitter under the sub-millisecond end-to-end decision delay.

This invention claims protection for an edge node selection scheme that considers content placement, belonging to the fields of mobile communication technology and computer network technology. It addresses the problem of effectively utilizing edge caching to shorten transmission paths in a vehicle-to-everything (V2X) edge caching environment while ensuring stable transmission. First, the network control center collects vehicle and network data at the edge node side, composed of base stations and edge servers. Second, based on the collected data, a caching strategy matrix is ​​calculated to determine the caching scheme and perform pre-caching. Third, a comprehensive edge node selection scheme is constructed by combining adaptability, received signal strength, and load rate. Next, the network control center selects the edge node with the highest score for the vehicle terminal. Finally, the vehicle terminal selects the edge node with the highest score for access.

This invention discloses a ship cloud computing load balancing method based on a hybrid intelligent algorithm, relating to the field of ship intelligent computing and cloud computing integration technology. The method includes the following steps: task classification modeling, edge-cloud resource status monitoring, multi-objective optimization decision-making, and dynamic load adjustment. This invention reduces the load standard deviation of ship edge-cloud nodes through a multi-objective optimization algorithm and a dynamic adjustment mechanism. It optimizes task processing efficiency, reducing the average latency of real-time tasks and shortening the total processing time of non-real-time tasks. It reduces system energy consumption by optimizing the energy consumption coefficient, thereby reducing the total energy consumption of edge nodes and cloud servers. It adapts to ship scenarios, improving the robustness and adaptability of scheduling strategies to address the heterogeneity of ship tasks and network dynamics.

This invention discloses a method and system for real-time data synchronization of multiple target libraries for power metering, belonging to the field of power metering data processing technology. Edge node devices collect and preprocess real-time data from power metering terminals, generating an energy consumption and resource status matrix before uploading it to the cloud. The cloud automatically detects the target library type and determines the adaptation priority through dynamic format conversion and hierarchical analysis. Based on the status matrix and priorities, a five-dimensional objective function is constructed, including business priority, cross-library adaptation success rate, system energy consumption, network load balancing, and data time-series consistency. An optimal scheduling scheme is generated using the DDPG algorithm. Transmission is completed by combining LSTM prediction, sliding window differential, and lightweight compression. After SHA-256 segmented verification, the hash value and device ID are written into the power industry consortium blockchain. This invention achieves multi-library collaborative adaptation, low bandwidth consumption, low energy consumption, and high time-series consistency, ensuring real-time, accurate, and secure synchronization of metering data.

A communication method is provided. In this scheme, a first edge node receives a data packet from a source node, and then determines a first transmission path and a second transmission path from the first edge node to a second edge node connected to the receiving node or multicast group of the data packet. The first transmission path includes a first intermediate node connected to the first edge node and a first forwarding path from the first intermediate node to the second edge node. The second transmission path includes a second intermediate node connected to the first edge node and a second forwarding path from the second intermediate node to the second edge node. The first edge node sends a first message and a second message to the first intermediate node and the second intermediate node, respectively. Both the first message and the second message include path information and data from the data packet. The path information in the first message represents the first forwarding path, and the path information in the second message represents the second forwarding path.

The application provides an emotional smart home central control method based on multi-chip cooperation and MCP protocol, and relates to the technical field of smart home control. The method comprises the following steps: collecting and preprocessing user multi-modal data through a multi-heterogeneous perception edge node, encapsulating the data into a standardized data frame containing semantics and space-time attributes through an MCP protocol, and transmitting the data frame to a decision hub node. The decision hub node fuses data to construct a global dynamic emotion topology map, triggers a multi-chip distributed cooperative reasoning mechanism, dynamically schedules resources in combination with system load to generate an emotional control strategy. After the strategy is distributed to an execution device, the edge node continuously monitors feedback data, the decision hub node calculates a deviation value and corrects reasoning parameters through reinforcement learning, and a closed-loop optimization loop is formed. The application improves emotion recognition accuracy and strategy adaptability, protects user privacy, and enhances the humanization and intelligence level of smart home interaction.

This invention discloses a method and system for regenerative braking energy storage scheduling based on dynamic prediction. The method includes: collecting data on vehicle operation, line conditions, environment, energy storage status, grid load, and real-time carbon price; generating predicted values ​​for train braking energy, multi-vehicle conflict probability, and energy storage health status through a prediction model, and calculating expected carbon emission reduction benefits; inputting the above results into a decision model, which uses trains, energy storage systems, the grid, and carbon trading nodes as game players, and performs collaborative optimization through multi-agent reinforcement learning fusion game strategies, outputting charging and discharging scheduling commands; issuing execution commands through a cloud-edge-device architecture, with edge nodes autonomously responding to emergencies in case of communication interruption. The system includes corresponding modules. This invention achieves proactive collaborative optimization from passive storage to "prediction-game theory-maintenance-carbon value-added," improving energy utilization and economic efficiency.

The application relates to the technical field of edge computing, and particularly discloses an infrared image processing method for excavator operation early warning based on edge computing, which comprises the following steps: dividing an infrared image into regions according to the temperature values of pixel points in the infrared image to obtain an active region and a background region; distributing the active region to edge nodes for preprocessing according to a network condition score F of the edge nodes; after all the active regions received by an edge node j are preprocessed, the active regions are redistributed to the edge node for preprocessing; after preprocessing of all the active regions is completed, the active regions are combined with the background regions which have not been preprocessed into a target image, and the target image is used for excavator operation early warning; and the real-time degree of the excavator operation early warning is improved.

The present application relates to the technical field of building energy consumption management and control, and particularly relates to a building full life cycle energy consumption real-time management and control method based on edge computing, which constructs a benchmark energy consumption library and a dynamic management and control framework, collects data in real time through an edge node, compares the data with benchmark values, forms a deviation matrix, quantifies the influence of external factors, identifies the root cause of the deviation through multi-level attribution, and finally generates and executes a precise control strategy on the edge side based on the analysis results, forms an intelligent closed loop of "comparison- attribution-regulation", realizes a paradigm shift from traditional energy consumption monitoring and control to precise attribution control based on multi-level root cause diagnosis, and thus achieves real-time energy efficiency optimization in a self-adaptive and root cause treatment manner in the full life cycle of a building.

The application claims a service-aware resource collaborative allocation method, belonging to the field of mobile edge computing resource allocation, which comprises: analyzing task demand characteristics, obtaining task abstract categories combined with task demand characteristics, and mapping them into corresponding demand types. Based on the task demand type, candidate collaborative node clusters are selected through destruction operators and repair operators, then the task and resource matching problem is modeled as a combinatorial optimization problem, the collaborative benefits of candidate node combinations are calculated, and the edge node combination with the highest collaborative benefit is selected. The application mainly considers the problems of task demand fuzziness and task and resource matching imbalance in the computing power network, designs a task demand type identification algorithm and a resource collaborative allocation strategy, selects a reasonable collaborative node cluster, and reduces the time delay while improving the resource utilization.

This invention discloses a method and system for integrated end-to-end data management of complex equipment. First, the server acquires structured and unstructured data from the design, manufacturing, and operation / maintenance stages of the system via a data acquisition module. Second, edge nodes perform data preprocessing, unifying data formats and time bases. Subsequently, the system constructs entity attribute and time-series models through a unified data modeling module, and achieves multimodal information alignment and representation through a data fusion module. The system also incorporates a quality risk early warning model, combining historical data and real-time status to achieve risk scoring and fault prediction. Finally, the application service module supports business functions such as intelligent scheduling, quality traceability, predictive maintenance, and performance analysis. This invention integrates the entire data flow across design, manufacturing, and operation / maintenance, solving problems such as data silos, low system collaboration efficiency, and difficulty in quality traceability, thereby improving the intelligence level and decision-making capabilities of complex equipment manufacturing.

Embodiments of the present application provide an edge resource expansion method, an edge cloud system, a device and a storage medium. In the embodiments of the present application, a center service node in a center cloud can request a server producer to produce a target server based on an edge resource expansion request of a target user; and install an operating system on the target server and deploy a cloud service; then, a logistics transportation party can be instructed to transport the target server to a target edge cluster corresponding to the target user, so as to deploy the target server in the target edge cluster and expand an edge node in the target edge cluster. In the embodiments, a whole-link expansion process integrating the user's edge resource expansion request, server production, transportation and edge cluster deployment is implemented, and the edge node expansion is integrated and end-to-end edge node expansion is implemented.

The application discloses a kind of distributed image denoising methods based on edge computing, it is related to edge computing technical field;Effectively improve image denoising;The application is by setting edge node model, based on edge node model acquisition target image target pre-processing image;And target pre-processing image is classified to obtain distributed morphological image by image form;Distributed morphological image is extracted to obtain the denoising intensity coefficient of distributed morphological image;Further, according to the denoising intensity coefficient, distributed morphological image is denoised to obtain distributed denoising image;Further, the image feature set of distributed denoising image is obtained, and distributed denoising image is spliced to generate target restoration image according to image feature set;Obtain the denoising coefficient of target restoration image;Judge the eligibility of denoising coefficient, if not qualified, then target restoration image is denoised to obtain qualified target restoration image and corresponding denoising times.