1642 results about "Edge node" patented technology

The invention belongs to the technical field of artificial intelligence and computer vision, particularly relates to a visual processing edge adaptive decision-making method for resource and task perception, and aims to solve the problem of scheduling mismatch caused by resource dynamic change and task demand diversity in visual task processing in an edge computing environment. The method comprises the following steps: collecting multi-dimensional resource state data of edge nodes in real time to form a resource state vector with high time resolution; analyzing the visual task request, and constructing a quantifiable task feature vector; and establishing a resource-task association mapping model based on a dynamic weight distribution mechanism. The method also supports cross-edge domain collaborative decision, and processes a pipeline dynamic reconstruction and security isolation mechanism. According to the technical scheme, the fluctuation of the resource utilization rate is reduced to 15% or below, the average task processing delay is reduced to 60%, the scheduling satisfaction degree is improved by 40% or above, and the self-adaptability and the service quality guarantee capability of the edge vision system are remarkably enhanced.

The invention discloses an edge computing collaborative distributed computing power scheduling method and device, and relates to the technical field of distributed computing and edge computing. The method comprises the following steps: collecting real-time operation state data of each edge node in a distributed edge node group; processing the real-time operation state data through a preset time sequence analysis operation, and predicting a predicted user load of each edge node in a preset future time period; combining the real-time operation state data with the predicted user load, and constructing a joint state vector; inputting the joint state vector into a preset reinforcement learning algorithm, and outputting a GPU resource dynamic allocation strategy; and when an AI reasoning request input by a user is received, determining a target edge node for the AI reasoning request from the distributed edge node group according to the GPU resource dynamic allocation strategy, and assigning the AI reasoning request to the target edge node. By implementing the technical scheme provided by the invention, the real-time performance and the stability of the distributed computing power system in a high-concurrency scene are improved.

The embodiment of the invention discloses a resource scheduling method and device, equipment and a medium, and relates to the technical field of resource scheduling. The method comprises the following steps: acquiring a service quality constraint index of a data processing task, and resource states of a cloud center and an edge node; constructing a three-dimensional dynamic resource feature space according to the resource state fluctuation information of the edge node, the spatio-temporal information of the cloud computing and the edge node and the historical occurrence probability of the service quality constraint index; and performing particle swarm optimization based on the three-dimensional dynamic resource feature space to obtain an initial strategy set, scheduling computing resources in the cloud center and the edge node based on the initial strategy set, and processing the data processing task based on the scheduled computing resources. According to the technical scheme, scheduling is flexibly carried out according to the condition of the data processing task and the resource condition of the cloud center and the edge node, and the actual requirement of the data processing task is accurately met.

The invention discloses a BIM-based hydropower station full life cycle design, construction, operation and maintenance integrated control system, and the system comprises an intelligent sensing layer which integrates 5G + Beidou positioning, a LoRa gateway and a sensor, collects multi-source heterogeneous data in real time, and transmits the multi-source heterogeneous data to a digital twinborn layer after the multi-source heterogeneous data is filtered by an edge node; a digital twinborn layer: constructing a parameterized model library through laser point cloud and BIM automatic registration, integrating a geological parameter dynamic correction algorithm, mapping a construction period stress field in real time, and updating model parameters based on sensing data self-evolution; the intelligent decision-making layer performs equipment fault prediction by using an LSTM neural network, optimizes multi-machine load distribution in combination with an improved PSO algorithm, and automatically adjusts a start-stop strategy when the load fluctuates; and the security execution layer is used for triggering equipment operation after virtual twinborn deduction verification through a block chain evidence storage instruction, realizing virtual-real dual verification in combination with an industrial firewall, and finally feeding back a running state to the sensing layer to calibrate and update a model, and supporting intelligent decision.

The invention relates to the technical field of industrial internet-of-things data security, in particular to an edge calculation differential privacy industrial internet-of-things data desensitization verification system and method.According to the system and the method, edge nodes are divided in a three-dimensional mode according to resource capacity, function positioning and privacy requirements, differential differential privacy parameters are formulated in combination with data attributes and leakage influences, and the safety of the industrial internet-of-things data is improved. And a dynamic adaptive grid is matched to realize hierarchical alignment, scene binding and elastic reconstruction, so that a cross-hierarchical privacy risk is avoided, and verification logic is simplified. Noise intensity is dynamically corrected by integrating multi-dimensional factors based on grids, lightweight, medium and deep hierarchical desensitization is designed for three types of nodes, and privacy protection and data availability are balanced. A privacy security and data availability two-dimension, terminal-gateway-area three-level verification chain is constructed, the desensitization effect is comprehensively evaluated, closed-loop iteration is achieved through hierarchical judgment and accurate adjustment, and industrial scene requirements are efficiently met.

The invention relates to the technical field of system monitoring, and discloses an information system full-link monitoring method based on high-frequency index acquisition optimization, which comprises the following steps: monitoring the running state of an information management system in real time, dynamically adjusting the sampling frequency by means of a customized service key identification component and a comprehensive load prediction model, and performing real-time monitoring on the sampling frequency. Transmitting the target data to the edge computing node; a lightweight monitoring agent is deployed at an edge node, and a wavelet signal decomposition algorithm is adopted to extract features and distinguish data types; constructing an information management business knowledge graph and an entity-relation-business rule base, associating abnormal features, and generating an abnormal root cause report in combination with a time sequence prediction model and a knowledge constraint large language model; based on report and information service priorities, monitoring resources are dynamically allocated in the edge-cloud collaborative architecture, and related model parameters, service association rules and constraint weights are optimized according to operation and maintenance feedback. According to the invention, targeted monitoring requirements in the business peak period and efficient utilization of system resources can be met at the same time.

An AI-based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure and coordination with distributed systems to support important use cases for a range of enterprises. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may use AI, IoT, and technologies that filter, process, and move data more effectively across communication networks. Embodiments of the platform may leverage energy market connection, communication, and transaction enablement platforms. Embodiments may employ intelligent provisioning, data aggregation, and analytics.

The invention provides a low-voltage power distribution network load change trend intelligent prediction and management method, belongs to the field of low-voltage power distribution network load prediction, and is used for solving the problems of large load fluctuation, insufficient prediction precision and high cloud deployment delay of a hybrid industry transformer area in related technologies. The method is deployed at an edge node of a transformer area, high-quality data is output through multi-modal data anomaly detection and scene completion, a four-dimensional dynamic load portrait is constructed based on the high-quality data, model super-parameter self-adaptive parameter adjustment is realized by combining transfer learning and Bayesian optimization, and accurate load data is output through three-dimensional linkage resource scheduling and dynamic fusion residual error correction. The method improves the load prediction precision and efficiency, reduces the response delay, and can effectively support the real-time scheduling of the power distribution network.

The invention relates to the technical field of power system monitoring, and particularly discloses a cable current-carrying capacity dynamic evaluation and early warning system based on distributed sensing. The system comprises a distributed sensor network, edge computing nodes, a block chain privacy computing platform and a dynamic evaluation and early warning center. The sensing network collects multi-source physical parameters of the cable in real time; carrying out local preprocessing and feature extraction on the edge nodes; the privacy computing middle station cooperatively constructs and solves an electricity-heat-aging joint model and outputs a cable thermal state and an aging coefficient on the premise of not leaking special models and data of all parties through federated learning and a safe multi-party computing technology; and the early warning center calculates the dynamic current-carrying capacity, evaluates the health state and implements graded early warning. According to the invention, accurate and safe dynamic evaluation and intelligent early warning of the current-carrying capacity of the cable are realized.

The invention relates to the field of emergency management, in particular to an emergency cooperative scheduling strategy generation method based on swarm intelligence. The method comprises the following steps: acquiring multi-source data of each agent, preprocessing the multi-source data, and feeding back the preprocessed multi-source data to the corresponding agent; the intelligent agent generates a preliminary scheduling strategy according to the received multi-source data, and updates and maintains a strategy distribution snapshot of an edge node in the preliminary scheduling strategy; and obtaining an evolution path of the secondary disaster, inputting the evolution path of the secondary disaster, the preprocessed multi-source data and the strategy distribution snapshot of the edge node into the federal depth Q network model, and generating a collaborative scheduling strategy. In this way, the technical problems that structural obstacles exist in data integration and sharing facing emergency scenes, allocation and scheduling of computing resources are difficult to meet dynamic and high-timeliness requirements of emergency responses, and the overall toughness and cooperative capacity of a system are insufficient are solved.

The invention discloses a mobile robot accurate docking method based on edge calculation, and aims to solve the problems that the dynamic docking precision is reduced and the collision risk is increased due to micro-motion or drifting of a target station. According to the method, unified time reference alignment is carried out on data of a camera, a laser radar, an inertial measurement unit, an ultra-wideband range finder, a station encoder and a programmable logic controller at an edge node, and a three-dimensional special Euclidean group equivariant multi-source fusion network is used for outputting relative pose estimation and covariance; the estimation in the time window is further used as a condition to be input into a conditional diffusion short-time prediction model to obtain a time-varying mean value and a time-varying covariance, an anisotropic probability tube is constructed, and prediction-measurement joint correction is carried out based on a score function; scenarized opportunity constraints are constructed under correction probability tube constraints, a tubular nonlinear model is adopted to predict, control and solve a reference trajectory, an actuator command is generated in combination with depth visual servo and compliance control, and the technical effects of high precision, robustness and safe docking under the station dynamic disturbance condition are achieved.

Mechanisms are provided for dynamically implementing reactive actions in edge nodes of a network in response to user equipment (UE) behaviors. Data of UE events are collected to infer UE movements and UE behavior within the network. A machine learning computer model is executed on the collected data of UE events to predict UE movements and UE behavior and their impact on edge node conditions within the network with regard to quality of service (QoS) metrics. An accuracy of the precited impacts of the predicted UE movements and UE behavior is evaluated and, based on the accuracy, reactive action(s) to execute to reduce the predicted impact of inaccurate predictions on edge node conditions with regard to the QoS metrics are determined and recommended to a control plane of the network for implementation of at least one of the one or more reactive actions on edge node(s) of the network.

The invention relates to a semi-supervised learning data anomaly intelligent identification and treatment system, which is applied to a hydrogen energy commercial vehicle, and comprises a data acquisition and preprocessing module, which is arranged on a vehicle-mounted terminal and is used for acquiring a hydrogen storage system signal, a hydrogen supply system signal, a fuel cell system signal and a whole vehicle system signal in real time, processing the acquired signal data; the semi-supervised anomaly recognition module is arranged on a cloud platform, is connected with the data acquisition and preprocessing module, and is used for fusing the processed signal data into a rule engine and semi-supervised learning, constructing a semi-supervised training data set, and performing deep auto-encoder model training through the data set so as to realize accurate anomaly recognition of few sample scene writing; and the exception treatment and feedback module is arranged at an edge node, is connected with the semi-supervised exception recognition module, carries out exception recognition through a trained model, carries out graded treatment according to the exception severity, and establishes a model evolution mechanism to realize continuous optimization of the system.

The invention discloses a real-time cleaning and aligning method for multi-source heterogeneous data, and particularly relates to the technical field of source heterogeneous data, a self-adaptive interface adapter is compatible with multiple types of data, and a semantic index atlas is generated through four-dimensional classification labeling; constructing a cloud-edge collaborative streaming processing framework, preprocessing edge nodes, and performing accurate cloud alignment; a dynamic rule cleaning and semantic-dimension-entity three-layer progressive alignment mechanism is adopted, and a four-dimensional quality evaluation system is combined for real-time monitoring; and through closed-loop optimization, homomorphic encryption, a block chain and other security mechanisms, data security and traceability are ensured. According to the real-time cleaning and aligning method for the multi-source heterogeneous data, the real-time performance and accuracy of data processing are effectively improved, the method is adaptive to multiple service scenes, and high-quality data support is provided for data value mining.

The invention discloses a collaborative data processing method and system based on source-network-load-storage integration. The collaborative data processing method and system are used for data acquisition, processing and optimal scheduling of multi-source heterogeneous equipment. The method comprises the following steps: registering a power generation side device, an energy storage side device, a power distribution network device and a load side device, obtaining self-description information, automatically identifying a communication protocol and loading a corresponding drive; a standardized data stream is generated through protocol conversion and semantic mapping, edge nodes execute real-time feature extraction and preliminary calculation, and a cloud platform performs global state estimation, prediction analysis and optimization scheduling; establishing a time delay monitoring and task priority mechanism to realize edge and cloud dynamic collaboration; performing quality evaluation and anomaly repair on the transmission data to generate a high-quality data set; a probability distribution model is established based on data, an uncertainty weight is calculated, feature fusion and weighted dimension reduction are performed through a collaborative data processing engine, a weighted confidence matrix is formed and input into optimal scheduling, system robustness is enhanced, and the method is suitable for a power system with a high new energy proportion and large load fluctuation.

The invention relates to the technical field of big data processing and analysis, in particular to a commodity traceability management method and system based on big data. The method comprises the steps of determining a data range and association logic of each type of events by constructing a semantic event system of a commodity life cycle, performing data acquisition, preprocessing and credibility evaluation at edge nodes and generating an evidence header, and further converting data into multilayer verifiable commodity fingerprints of an L1 edge abstract, L2 event level fusion and L3 full link fusion, the method comprises the following steps: realizing feature fusion and dynamic incremental updating, then performing distributed notarization and multi-party verification on fingerprints through a federated anchor point mechanism to ensure that a full link is traceable and cannot be tampered, and finally supporting full link traceability query and anomaly tracking based on a unique commodity identifier. And closed-loop traceable management is realized through visual display and multi-role authority management. According to the invention, the reliability, interpretability and cross-organization trust of commodity traceability are effectively improved.

The invention relates to the technical field of edge computing and artificial intelligence model updating, and provides a cloud edge collaborative edge end equipment operator hot updating method, system and device and a medium. The method comprises the steps that a cloud detects a new version of an operator and generates an incremental update package between the new version and the old version; the edge node pulls the incremental update package and performs multiple security verification on the incremental update package; a double-instance inference engine is deployed in the edge node, operators passing verification are preloaded to a standby engine, and hot switching from an operation engine to the standby engine is achieved through a state synchronization mechanism; monitoring the running state of the new engine after switching, if the running state is abnormal, triggering a rollback mechanism, and switching back to the original engine; and optimizing edge node resources, cleaning old version operators, and reporting an update state and a resource use condition to the cloud. Core mechanisms such as differential updating, double-instance engine hot switching, multiple safety verification and dynamic resource scheduling are fused, and therefore efficient, safe and non-perceptual operator updating is achieved.

The invention relates to the technical field of edge computing, in particular to an edge node collaborative optimization method and system of a computing power network. Comprising the steps of collecting resource state information and historical behavior data of a plurality of edge nodes to form a resource state data set; constructing a computing power network topological graph based on the resource state data set, and extracting node feature representation of each node through a graph neural network; calculating the comprehensive credibility of each edge node based on the historical behavior data, and integrating the comprehensive credibility into the node feature representation; collecting energy consumption state data of each edge node, and calculating an energy consumption efficiency index of each edge node; and a coupling linkage mechanism of credibility and energy consumption perception is established. Through a coupling linkage mechanism of credibility and energy consumption perception, high resource utilization rate, low task delay, strong system robustness and green energy conservation of computing power network edge node scheduling are realized.

The invention discloses a power distribution network short-circuit parameter real-time monitoring method, system and equipment based on edge calculation and a medium, and belongs to the technical field of power system monitoring and protection, and the method comprises the steps: collecting data information in real time at each monitoring node of a power distribution network, generating a state vector reflecting a node operation state, and inputting the state vector to an edge calculation unit, processing through an internal first calculation model to obtain a power grid fault evaluation result of a corresponding node, and compressing and uploading the power grid fault evaluation result to a cloud; based on the uploaded data and the system operation state, a system performance evaluation result is generated in real time, and an adaptive optimization algorithm is used for dynamically adjusting internal model parameters of the edge calculation unit. According to the method, the composite algorithm model fusing feature extraction and parameter calculation is deployed at the edge node, the problems that a traditional centralized processing mode is large in data transmission delay and central node calculation pressure is concentrated are solved, and the technical effect of reducing bandwidth occupation of a backbone communication network is achieved.

The invention discloses an edge computing node collaborative task unloading method for guaranteeing a low-delay service, and relates to the field of edge computing, each edge computing node generates a collaborative view comprising the edge computing node and a neighbor edge computing node through a local LSTM prediction model and federated learning, and the collaborative view comprises a predicted resource state and a predicted network state; according to the invention, the local LSTM prediction model and federated learning are combined to generate the collaborative view, so that accurate prediction and global information sharing of edge node resources and network states are realized, and the accuracy of decision making is improved; the tasks are analyzed into a dependency graph with key path marks, so that priority scheduling of the key tasks is ensured, and the overall task time delay is reduced; based on a weighted voting consensus mechanism of node credibility and resource adequacy, the efficiency and reliability of decision consensus among nodes are improved; the task unloading efficiency and the service quality of the low-delay service are effectively improved, and the stability and the reliability of the edge computing system are enhanced.

The invention discloses a distributed urban and rural planning decision support method and system, and relates to the technical field of data analysis, and the method comprises the following steps: S100, drawing a planar graph of a to-be-planned region, dividing the planar graph of the region according to a Morse decomposition method, and constructing a region matrix, S200, calculating the coverage rate of the region matrix, and S300, calculating the coverage rate of the region matrix. And planning the surveying and mapping path. According to the method, planning units and surveying and mapping grids are unified into the same quadrilateral matrix through Morse complex, repeated zoning and edge matching errors are effectively avoided, the edge weight is inversely proportional to the building coverage rate, an unmanned aerial vehicle and a surveying and mapping vehicle automatically and preferentially encrypt a high built-up area, field working hours are saved, the data precision of a core construction area is guaranteed, and the construction efficiency is improved. The regional matrix naturally corresponds to edge nodes, and each node only communicates with an adjacent unit, so that the MST and the local route can be locally solved, the communication complexity is reduced, and the method is suitable for large-scale regional surveying and mapping.

The invention discloses an RDMA asynchronous communication optimization method and system based on a lock-free queue, a medium and a processor, and relates to the technical field of remote communication. The method comprises the following steps: constructing a lock-free annular buffer area at an edge node, registering the lock-free annular buffer area as an RDMA memory area, and configuring a queue and a thread pool; the collection thread concurrently writes data, and the RDMA submission thread asynchronously completes remote transmission based on a buffer area state; and the queue processing thread performs closed-loop linkage transmission and buffer area states through'batch polling-result analysis-state synchronization-exception processing ', and dynamically adjusts the buffer area and queue parameter optimization performance at the same time. The problems that an existing RDMA scheme is poor in thread cooperation, weak in resource adaptation, not timely in state linkage and the like are solved, transmission delay and CPU overhead are greatly reduced, communication stability and the resource utilization rate are improved, and the method is suitable for edge computing and other high-concurrency data interaction scenes.

A node-edge symbolic consent kernel (NESCK) provides a computing architecture in which every instruction is gated by a verifiable human-intent signal and an ethical-predicate chain prior to execution. The system integrates a biometric-sensing front-end (EEG / GSR / facial micro-affect), a symbolic arbitration engine that transforms bio-intent data into consent tokens, and a cryptographically bonded node-edge ledger that records execution lineage, revocation, and audit proofs. Each node represents an executable state bound to a human consent fingerprint, while each edge encodes the ethical transition rules authorizing propagation through the network. At runtime, the kernel evaluates symbolic predicates, verifies zero-knowledge proofs of consent, and allows or halts instruction dispatch. The framework operates across devices, edge nodes, and cloud layers, enabling real-time lawful AI behavior, revocable autonomy, and tamper-proof moral audit trails. Embodiments span neuroadaptive wearables, autonomous vehicles, robotics controllers, and sovereign AI systems requiring continuous consent and transparent accountability.

The invention relates to the technical field of computer task scheduling, in particular to a server integration system based on edge computing, which comprises a topological path delay analysis module, a multi-dimensional priority mapping scheduling module, a cross-architecture performance conversion module, a dynamic priority rearrangement module and a task migration optimization module. According to the method, more dynamic and flexible task scheduling is realized by accurately analyzing communication delay and network topology characteristics between edge nodes and based on comprehensive evaluation of node performance and delay weight, the intelligent level of task allocation is improved, and the limitation of static load detection is effectively avoided; the adaptability of the system to inter-node communication delay and execution time offset is enhanced, a task migration strategy is optimized, reasonable allocation and efficient execution of tasks among heterogeneous nodes are ensured, performance waste caused by improper resource scheduling is reduced, the utilization rate of resources and the overall performance of the system are improved, and the service life of the system is prolonged. And the dynamic response capability of task scheduling in the edge computing environment is enhanced.