2275 results about "Edge server" patented technology

The invention discloses an equipment intelligent safeguard system based on an offline large model, and relates to the field of equipment management and safeguard, and the system comprises a collection module which collects an equipment surface defect image and a time sequence signal through an unmanned plane, collects a text log, processes the defect image, the time sequence signal and the text log, and generates a multi-modal data set; the feature fusion module is used for performing feature extraction on the multi-modal data set and fusing the three features to generate a multi-modal feature; the updating module is used for extracting a fault triple from the semantic vector, constructing a Bayesian causal graph according to the fault triple, dynamically updating node confidence and generating a knowledge graph; the maintenance strategy module is used for matching the multi-modal features with a knowledge graph and positioning a fault root cause; and the lightweight model deployment module is used for pre-training a lightweight model in an edge server, accelerating parameter aggregation by using a quantum annealing algorithm, adjusting and optimizing a global large model according to the aggregated parameters, and generating a strategy in combination with reinforcement learning.

The invention belongs to the technical field of Internet of Vehicles, and particularly provides a federated learning optimization system and method for an intelligent network connection automobile scene, which improves an asynchronous federated learning framework, divides a plurality of vehicles into different clusters due to the isomerism of computing power, and adopts a cloud-edge-end three-layer federated learning framework, so as to realize the optimization of the federated learning of the vehicles. An edge server is deployed on a roadside unit RSU, received model parameters are subjected to pseudo-asynchronous aggregation in a self-adaptive dynamic time window, the received model parameters are subjected to real-time aggregation at a cloud end, the system model training efficiency and quality are improved, meanwhile, the data security is ensured, the user privacy is protected, and the user experience is improved. And the efficiency of the federated learning optimization system and the quality of the global model in the intelligent network connection automobile scene are ensured.

According to the 5G portable WiFi signal enhancement method and system provided by the invention, surrounding environment data are collected through an environment sensing sensor, and a device placement scheme is optimized in combination with a deep learning algorithm, so that the signal coverage range is expanded; the strategy of dynamically adjusting the transmitting power, the signal frequency and the modulation mode can optimize the signal strength according to the environmental change; interference signals are analyzed through quantum calculation, null is formed through the beam forming technology, interference signal receiving is effectively restrained, and the signal strength in the target direction is enhanced. In combination with the Internet of Things technology, uploading equipment state data to an edge server in real time for big data analysis, and pushing a personalized signal enhancement scheme according to different user use environments; through precise placement guidance and a dynamic frequency band switching strategy, a user can enjoy smoother network connection in a use process, and good signal quality is ensured to be kept in a complex environment. According to the invention, not only can the signal quality of 5G portable WiFi be improved, but also more intelligent and personalized network experience can be provided for users.

The invention discloses a power plant specific area personnel behavior identification and early warning system, and relates to the technical field of industrial safety intelligent monitoring, the power plant specific area personnel behavior identification and early warning system comprises a data acquisition module, an edge calculation module and a central processing platform, the edge computing module carries out lightweight processing and localized decision making on original data, the load of the central processing platform is reduced, and the central processing platform realizes full-process safety management and control of a high-risk area through multi-modal data fusion, dynamic authority management and trajectory analysis. According to the invention, lightweight AI models are embedded in various cameras of a work card authentication terminal and a visual perception unit, preliminary reasoning is directly completed at the work card authentication terminal and the cameras, so that the work card authentication terminal and the cameras have edge computing capability, and independent edge servers or industrial personal computers are deployed near data sources of various regions of a power plant. And serving as a regional edge computing node.

The invention relates to a secure communication method between an edge server and terminal equipment. The secure communication method comprises the following steps: establishing a physical layer channel state information database, generating a multi-dimensional feature vector and encoding the multi-dimensional feature vector into a terminal equipment identifier, generating an identity authentication request by the terminal equipment, and performing hash operation to obtain a first hash value; the identity authentication request and the first hash value are sent to the edge server, the edge server verifies the identity authentication request and queries a corresponding device key, performs hash operation to obtain a second hash value, generates an authentication response message after successfully comparing the second hash value with the first hash value, encrypts the authentication response message by using the device key, and sends the encrypted authentication response message to the edge server; and sending the encrypted authentication response message to the terminal equipment. And the terminal equipment decrypts by using the equipment key to obtain the session key, and communicates with the edge server based on the session key. By using the physical layer channel information and the equipment hardware characteristics, the security communication method fusing dynamic identity authentication and efficient key management improves the security, reliability and anti-attack ability of communication between the edge server and the terminal equipment.

The invention relates to a digital twinning dynamic deployment and migration optimization method based on vehicle-infrastructure collaborative awareness, and belongs to the technical field of Internet of Vehicles and digital twinning. The method comprises the following steps: constructing a digital twin network oriented to the Internet of Vehicles; a three-party utility model driven by heterogeneous sensing resources is constructed, and sensing quality, task completion time and node energy consumption are comprehensively considered; the sensing area overlapping degree of the vehicle and the road side unit is dynamically quantified, and the data acquisition redundancy rate is reduced in combination with a preset threshold value; a parallel pre-migration mechanism is designed according to the computing load, the storage capacity and the communication resource state of the edge server, and independent fragmentation transmission of the digital twin model is achieved through a multi-path concurrent transmission strategy; establishing a joint optimization model with maximized comprehensive utility; a multi-agent near-end strategy optimization algorithm is adopted to realize collaborative optimization of a digital twin deployment strategy, migration path selection and perception task distribution. According to the invention, the real-time performance and reliability of the digital twinning service in the Internet of Vehicles scene are improved.

The embodiment of the invention provides a dynamic allocation method and device for AI reasoning tasks, and the method comprises the steps: monitoring the workload, memory availability, network delay, bandwidth and energy consumption of each computing node in a cloud edge cooperation system in real time, the computing node comprising a local edge device, an edge server and a cloud platform; according to the delay requirement, the calculation requirement and the data privacy requirement of the task and the real-time state of each calculation node, dynamically distributing the task to different calculation nodes; adjusting the complexity of an AI model used for executing the task according to the resource use condition of the computing node; and according to the historical performance data and the real-time network condition in the task execution process, optimizing a task allocation strategy.

The invention relates to an air-ground integrated bridge disease monitoring method and an application system thereof, and the method comprises the steps: controlling an unmanned plane to take off from a nest, and transmitting a bridge initial inspection task to the unmanned plane; sending the bridge image data to a vehicle-mounted high-performance edge server for primary three-dimensional modeling; carrying out monomer splitting on the general three-dimensional model of the bridge, and carrying out close photogrammetry on the bridge by an unmanned aerial vehicle based on a close inspection route to obtain a monomer high-resolution image of each part of the bridge; the unmanned aerial vehicle automatic inspection and data processing platform executes disease target detection and disease semantic segmentation on the single high-resolution images; and associating and binding disease target detection and disease semantic segmentation results with the bridge refined three-dimensional model to generate a bridge disease detection report. A worker only needs to draw an inspection area, and the unmanned aerial vehicle automatic inspection and data processing platform automatically generates an unmanned aerial vehicle inspection route according to the drawn inspection area, so that the unmanned aerial vehicle autonomously and safely inspects a bridge.

The invention discloses an event early warning efficient management method and system based on park management, and relates to the technical field of safety management, and the method comprises the steps: constructing a hexagonal cellular unit layout, generating an equipment registry and a network topology structure, and forming a structured data set through data alignment and sliding window verification; the edge server loads an initial weight, calculates a dynamic weight matrix based on the LSTM network, and generates a high-order feature vector set in combination with the graph neural network; inputting the high-order feature vector set into a space-time prediction model to generate an environment evolution trend prediction value, fusing network topology and BIM model parameters to calculate a dynamic risk index, and triggering graded early warning; the command center analyzes the early warning signal to generate a fusion visual interface, historical cases are matched through federal learning, a resource allocation scheme is optimized, and an optimal disposal strategy is output through Monte Carlo simulation. By dynamically adjusting the weight of the sensor, the weight of the infrared sensor in the high-temperature-sensitive area is increased, and the abnormality capturing efficiency is improved.

The invention discloses a storage security protection and fire-fighting early warning method based on an edge AI mode, and belongs to the technical field of safety monitoring, and the early warning method employs an edge AI artificial intelligence video data processing analysis method to carry out intelligent security protection behavior identification early warning and intelligent fire-fighting behavior identification early warning; the edge AI artificial intelligence video data processing analysis method comprises the steps that a data center pushes a trained model to an edge AI server, and the edge AI server analyzes streaming data collected by edge Internet of Things equipment in real time and carries out AI reasoning; the edge AI server uploads the received new condition related data to the cloud to train the model, and the trained model is pushed to the edge AI server again; according to the invention, security and protection and fire-fighting applications are deeply fused, an information island is thoroughly broken, data sharing and interconnection are realized, unified output of early warning signals is ensured, and the emergency response speed and accuracy are improved.

The invention discloses a computing network resource scheduling optimization method based on multi-agent deep reinforcement learning, and the method comprises the steps: building a distributed service caching, task scheduling and computing network resource distribution model through comprehensively considering the multi-party computing power of a cloud side end, the system time delay, the system energy consumption and the load balance of heterogeneous nodes of an edge layer in a computing network scene; taking an edge server as an intelligent agent, considering the time dependence of a task request and the influence of the task request on service cache, and using an Attention-LSTM algorithm to make a service cache decision; based on a multi-agent depth deterministic policy gradient algorithm, constructing an MA-LDDPG algorithm to perform task scheduling and resource allocation decision; in consideration of the influence of service caching on task scheduling and resource allocation, a service caching strategy obtained by an intelligent agent is used as an additional observation state of task scheduling and resource allocation decision to participate in training, so that joint optimization of system time delay and edge layer heterogeneous node load balancing is achieved.

The invention provides a regional electromagnetic patrol method and system based on vehicle-mounted electromagnetic detection equipment, and the method comprises the following steps: recognizing a target monitoring node based on historical data and an electromagnetic propagation model, and constructing an electromagnetic environment diagram network; a network is divided into sub-graphs through multi-scale clustering, a vehicle-mounted electromagnetic inspection vehicle and an edge server are deployed in each sub-graph area, and a central server is deployed in a central area. And the vehicle-mounted equipment performs electromagnetic detection according to the optimized path and acquires data. And the edge server updates the sub-graph by using edge calculation and federated learning technologies, and performs local anomaly analysis. And the central server aggregates all sub-graph update information to complete global update and anomaly analysis. And finally, local and global analysis results are fused to form a comprehensive electromagnetic anomaly analysis result, and the comprehensive electromagnetic anomaly analysis result is visually displayed on a regional map. According to the invention, real-time and accurate large-range electromagnetic environment monitoring and analysis are realized, and the electromagnetic management capability is effectively improved.

A dynamic micro-service migration and route redirection method based on topology awareness comprises the following steps: S1, acquiring cloud computing data center and edge server resource information, an application request stream set and an initialized mirror image deployment matrix in a dynamic scene, and dividing time slot-based input request stream features; s2, in each time slot, a multi-mirror-image preliminary deployment scheme of the micro-service is obtained in a marginal gain optimal mode, and a node routing scheme is redirected according to a mirror image deployment matrix of a physical server node; and S3, according to the resource cost, the multi-mirror-image preliminary deployment scheme and the node routing scheme, screening out topology dependence pairs with abnormal bandwidths in a request stream, and based on topology perception, executing migration operation for micro-service mirror images on server nodes of links with abnormal bandwidths to obtain an optimal joint decision of micro-service deployment and request routing. According to the method, topology-aware micro-service mirror image migration and online redirection probability routing are realized in a dynamic cloud computing data center scene.

The invention discloses an edge cloud collaborative federated digital twin model construction method based on a dynamic hierarchical game, and aims to construct an edge cloud collaborative federated model generation framework based on distributed sensing data to solve the problems of data islands, high time and energy overhead, poor model performance and the like existing in central dynamic digital twin model construction. And an online decision optimization problem is modeled by taking maximization of long-term model quality and reduction of long-term resource overhead as targets, and dynamic decision optimization is realized. In a federated digital twinborn model generation framework, a bottom sensor collects feature data of a physical entity and transmits the feature data to an edge server to construct a local digital twinborn model, and a cloud server collects all the local digital twinborn models and integrates the local digital twinborn models into a global digital twinborn model. According to the method, the technical problems of model heterogeneous dynamic evolution, sensor resource overlapping and sharing and computing communication resource joint optimization in federated digital twinborn construction are effectively solved, the quality of the digital twinborn model is remarkably improved, and the system energy consumption and configuration cost are reduced.

The invention relates to an edge anomaly detection method based on global-personalized collaborative federated learning, and the method comprises the steps: enabling an edge server to generate a global model, and then releasing the global model; the client initializes a local model according to the global model; converting the traffic features into global and personalized feature vectors by using a condition calculation component at a client; the client introduces comparison and magnitude loss into a global category embedding module, guides the learning of global feature vectors according to the embedding of sample categories, and guides the learning of personalized feature vectors by using cross entropy loss; the client side judges whether loss replacement is executed or not by calculating the cosine similarity of the cross entropy loss and the total loss updating gradient, and then model training is accelerated; the client uploads the update of the model to the edge server after completing local training; performing global aggregation by the edge server, and updating a global model; and repeating the steps until the anomaly detection model converges. The method is beneficial for improving the edge anomaly detection precision and efficiency.

The invention discloses an electrical equipment state monitoring system and method based on the Internet of Things, and belongs to the field of the Internet of Things, and the method comprises the following steps: installing an intelligent sensor node with a built-in edge calculation and self-diagnosis function, and carrying out the self-adaptive adjustment of frequency collection data according to the working state and historical data of electrical equipment; an edge server is added between the gateway device and the cloud server, and hierarchical data analysis is carried out; training the historical data of the electrical equipment by using a machine learning algorithm, constructing an equipment health state model, and performing fault prediction and health assessment; an early warning condition value is set according to the prediction result, and the intelligent alarm system carries out fault processing according to the early warning condition value. The maintenance cost can be reduced, and the efficiency and accuracy of maintenance work can be improved.

The invention provides an assembly line parallel distributed training method, device and system for a deep neural network, and the method comprises the steps: sequentially transmitting each group of small-batch training data forming the current-batch training data to an edge server through an optical network, enabling the edge server to cooperate with the cloud server to perform asynchronous parallel cooperative training on different sub-task models forming the deep neural network in a communication and training decoupling mode, and outputting gradients corresponding to each group of small-batch training data in sequence by the edge server; and sequentially receiving the gradient of each group of small-batch training data. According to the invention, correct transmission of data in a model training process can be ensured, communication overhead in the training process can be reduced, load balance between devices can be realized, model training efficiency and effectiveness can be improved, and the utilization rate of resources of the devices participating in training can be increased.

Disclosed are various embodiments for dynamic availability zones in radio-based networks. In one embodiment, excess resource capacity on a radio access network (RAN)-enabled edge server in a cloud provider network is determined. The RAN-enabled edge server is located at a cell site and is configured to perform distributed unit (DU) and / or centralized unit (CU) functions for a RAN. The excess resource capacity is offered as part of a cellular capacity zone that is generally available to customers of the cloud provider network.

The invention discloses a storage digital twinning system integrated with a UWB-RFID technology. The storage digital twinning system comprises an entity storage, an edge server and a storage digital twinning system. The entity storage comprises a fixed UWB base station, a UWB tag, a fixed RFID base station, a passive tag and a movable RFID base station. And the edge server collects all base station equipment data of the entity warehouse, processes and calculates cargo information according to a built-in algorithm, and performs data interaction with an upper-layer warehouse digital twin system at the same time, so that the operation condition of the entity warehouse is remotely monitored. According to the system, through the ultra-wideband technology, the radio frequency identification technology and the digital twinning technology, the storage position of stored goods can be determined, entity storage data can be visualized, and the problems of low positioning precision and low informatization degree in the existing storage management technology are solved.

The invention discloses a segmentation federal learning model training method based on a heterogeneous system. The system comprises a plurality of clients, a central server and an edge server. The global model segments the client local model and the server local model based on each client device condition and deploys to corresponding clients and central servers, respectively. And in each round of training, the client performs parallel training, forwards propagates local data, outputs crushed data, transmits the crushed data to the server local model of the central server, continuously forwards propagates to obtain a loss value, and backwards propagates a gradient to update parameters of the server local model and the client local model. And each client transmits the updated model to the edge server, and the edge server and the central server complete parameter exchange of a public layer and then perform model gathering to generate a global client model and a global server model. The global client model distributes client local models to the clients according to each client break point. The method can adapt to equipment conditions of heterogeneous equipment, and the model training efficiency is improved.

The invention is suitable for the technical field of artificial intelligence and cloud computing crossing, and provides a cloud mobile phone control system based on a large language model, comprising an interaction agent used for receiving a cloud mobile phone control instruction input by a user by using a natural language, and sending the cloud mobile phone control instruction to a large language model agent; the large language model agent is used for analyzing and constructing the instruction and sending a tool calling instruction to the MCP client; the MCP client is used for initiating a remote procedure call request to the MCP server; the MCP server side is used for sending a task instruction to the cloud mobile phone control platform; the cloud mobile phone control platform is used for sending the task instruction and the equipment information to the edge server; and the edge server is used for transmitting the task instruction and the equipment information to the cloud mobile phone management service node, so that the cloud mobile phone management service node executes corresponding operation on the target cloud mobile phone. According to the invention, intelligent control of the cloud mobile phone can be realized, and the management efficiency of the cloud mobile phone is improved.

The invention discloses a multi-mode intelligent inspection system and device for an oil field gathering and transportation station yard, and belongs to the technical field of oil field gathering and transportation station yard inspection. The multi-mode intelligent inspection system comprises a data acquisition module, an abnormity judgment module, a station yard model simulation analysis module and a decision analysis and execution module; comprising a vibration camera sensor, a temperature camera sensor, a pressure camera sensor, a flow camera sensor, a liquid level camera sensor, a gas camera sensor, an ultraviolet flame camera sensor, an infrared thermal imaging camera sensor, a visible light camera sensor and a depth camera sensor and is used for collecting process equipment, environment states and personnel operation behavior data of a current oil field gathering and transportation station and recording the data collected by the sensors in a near-field edge server and a cloud server; the time difference of data acquisition is eliminated through a space-time alignment algorithm, and a time sequence health vector in a unified format is generated. According to the multi-mode intelligent inspection system and device for the oil field gathering and transportation station yard, multi-mode inspection can be carried out on the oil field gathering and transportation station yard based on the type of the oil field gathering and transportation station yard, and typical anomalies are recognized and processed.

The invention belongs to the field of Internet of Vehicles network communication, and discloses an Internet of Vehicles federated learning dynamic sparse training method based on cluster expansion. Constructing a hierarchical federated learning training architecture under the scene of the Internet of Vehicles, wherein the hierarchical federated learning training architecture under the scene of the Internet of Vehicles is of a client-server two-layer structure and comprises an edge server and a road system; performing federated dynamic sparse training based on a hierarchical federated learning training architecture in an Internet of Vehicles scene: training a sparse network structure based on local data by a training node under coordination and management of an edge server, and periodically updating the sparse network structure according to a predefined reconstruction updating standard; and finally, aggregating and reconstructing a global network structure meeting the target sparseness at the edge server.

The invention relates to the technical field of electric digital data processing, and particularly discloses a hierarchical federated learning method for adaptive compression coding, which comprises the steps of hierarchical federated learning architecture construction, edge server matching, model generation performance judgment, compression ratio matching and hierarchical federated learning architecture judgment, and specifically comprises the following steps: firstly, constructing a hierarchical federated learning architecture; each layer can make full use of computing resources thereof, edge servers corresponding to the terminal devices are obtained through matching, the terminal devices are used for generating environment data training models, corresponding compression rates are obtained according to network transmission performance of the edge servers, and the compression rates are used for data compression. The edge server receives the environment data training model of each terminal device and carries out adaptive compression coding and edge aggregation in sequence according to a corresponding compression ratio, and the cloud server receives an edge aggregation result and carries out global aggregation; and finally, judging whether the cloud server to which the hierarchical federated learning architecture belongs uploads a global aggregation corresponding result to an environment resource scheduling cloud platform or not.

Disclosed are various embodiments for automated deployment and management of distributed units (DUs) in a radio-based network. In one embodiment, it is determined to deploy a radio-based network at least partially using infrastructure of a cloud provider network. A radio access network (RAN)-enabled edge server of the cloud provider network is deployed to a physical location of the radio-based network. The RAN-enabled edge server is configured to perform a set of distributed unit (DU) functions for the radio-based network.

The invention relates to a digital twinborn factory data communication and privacy protection method, which comprises the following steps: establishing an edge-cloud digital twinborn model which comprises an edge layer, a digital twinborn layer and a cloud layer; the edge layer comprises an edge server and IIOT equipment of a factory; the digital twin layer comprises a plurality of digital twin servers; the cloud layer comprises a cloud server; dividing the neural network model for classifying tasks into a shallow model and a deep model; and the cloud server receives real-time model update provided by the digital twin server, grouping and training the IIOT equipment based on the digital twin model updated in real time, weighting and aggregating local shallow model parameters encrypted by each IIOT equipment in a group by adopting a secure multi-party computing method, and performing parallel computing by the IIOT equipment by using the aggregated part. According to the method, data sharing among multiple pieces of workshop equipment can be realized while data privacy is protected, the production process is optimized, and the product quality is improved.

The invention provides a 5G portable WiFi fault diagnosis and recovery method and system. The method comprises the following steps: collecting network connection data, system information and operation logs from a communication terminal connected to 5G portable WiFi equipment; analyzing the data, and generating a fault diagnosis report; selecting and executing corresponding recovery operation from a recovery scheme library according to the report; monitoring equipment state and network connection in real time, and judging whether recovery is successful or not; if the fault is not solved, an advanced solution is provided, and equipment information, program attributes, scheme library information, network connection data, system information and operation logs are sent to an edge server; the edge server obtains fault data and diagnosis reports of a plurality of similar devices as references, potential fault laws are mined through big data analysis, and reference analysis data are generated; and optimizing an intelligent diagnosis program and a recovery scheme library according to the reference analysis data and other information. The fault type can be accurately identified, the fault recovery time is remarkably shortened, and the user experience is improved.

The invention relates to an Internet of Vehicles task unloading method based on federated learning, and belongs to the technical field of information. Aiming at the problem of how to make an optimal unloading decision in a dynamic environment of the Internet of Vehicles, a framework based on an edge intelligent system is constructed, a communication and calculation model between the Internet of Vehicles and an edge server is established, a calculation unloading strategy based on federated learning is designed, the Internet of Vehicles and the federated learning are fused, and the optimal unloading decision is made. The processing time delay of the vehicle calculation task is minimized, the calculation task can be unloaded to an edge server to be processed based on the Internet of Vehicles, simulation experiments prove that the method reduces the time delay to a certain extent, and the vehicle task unloading efficiency is improved.

The invention provides an abnormal network traffic identification method based on small sample data enhancement, and the method comprises the steps: S1, converting one-dimensional traffic data into a two-dimensional gray-scale map, and employing a twin network combined with a dynamic attention mechanism to analyze the differences between various types of traffic data pair by pair; parameters of an attention mechanism in the twinborn attention network are analyzed, and inter-class difference features are identified; s2, selecting a training domain by using the inter-class feature difference matrix, introducing a comparative learning method to judge the difference between real data and generated data, and generating an enhanced data set for each class of traffic; s3, after the enhanced data set issued by the central server is received, each edge server uses the enhanced data set and local sample data to train the twinborn attention network in the step S1; due to different local sample data sets, each edge server is trained to obtain a personalized abnormal traffic identification and classification model suitable for local data distribution, and abnormal traffic is effectively classified.

The invention discloses a multi-edge device collaborative reasoning method and system for a hybrid expert large model, and the method comprises the steps: collecting and analyzing system data, and adjusting the expert layout according to the system data; in the first stage, the number of experts required by each layer of a server is dynamically determined by balancing activation diversity and memory resource limitation; in the second stage, according to the expert number and the activation mode of each layer obtained in the first stage, low-time-delay reasoning is achieved by minimizing the calling times of remote experts, and the experts are distributed to all the servers. The system includes a global scheduler and an edge server participating in the system. By using the method, edge multi-machine joint reasoning is achieved, the deployment method is optimized, online adjustment deployment can be performed according to data changes, and the reasoning speed is increased compared with other deployment technologies. The method can be widely applied to the technical field of distributed machine learning.