5156 results about "Edge node" patented technology

The invention relates to the technical field of privacy data protection, and discloses a privacy data protection method and system based on homomorphic encryption and federated learning, and the method comprises the steps: processing original health data collected by Internet of Things equipment of a plurality of pension institutions, and obtaining a preprocessing data set and a privacy risk assessment result; encrypting to obtain a double encrypted data set; constructing a decentralized federated learning framework, and calculating global training parameters; performing noise injection processing and model iteration training to obtain a privacy protection model; performing trusted execution environment deployment on the privacy protection model, and performing identity verification through role-based multi-level access control and zero-knowledge proof to obtain a security access strategy; edge node distributed deployment is conducted on the privacy protection model, cross-mechanism health data analysis is conducted through a secure multi-party computing protocol, a collaborative analysis result is obtained, various privacy protection technologies are adopted, and efficient utilization of data is achieved while data privacy is protected.

The invention discloses a transformer area intelligent fusion terminal data processing system based on edge calculation, relates to the field of edge intelligence, and solves the problems of poor real-time performance and poor data exception processing accuracy of an existing intelligent fusion terminal data processing system. According to the method, communication between micro-services is efficiently managed through the container arrangement engine and the service grid, the message transmission delay is reduced, and resource allocation is further optimized through a resource scheduling algorithm based on reinforcement learning, so that system resources are used most reasonably; according to the method, anomaly detection is rapidly and accurately carried out through a space-time heterogeneous fusion network model, hierarchical processing of data and on-cloud training of the model are realized through a cloud-edge collaborative architecture, edge nodes participate in model training through a federated learning mechanism, the model performance is optimized, the transmission time of the data in the network is shortened, and the problem positioning speed is increased; the real-time performance and the reliability of an existing system are greatly improved.

The invention discloses a cloud edge cooperative control method, which comprises the following steps: establishing a cloud edge cooperative architecture, collecting data by edge nodes, and selecting a corresponding data compression strategy to compress the data; generating an edge-cloud collaborative uploading data stream; the cooperative control layer executes adaptive load balancing based on a cloud-side node state, task characteristics and a network condition which are monitored in real time so as to realize task allocation and scheduling between the cloud and the side; the cloud receives the data stream transmitted by the edge node, decompresses and processes the compressed data according to the metadata information, completes the task and feeds back an execution result; a node performance prediction model is constructed by using system state monitoring information, a task allocation strategy and a resource allocation and data compression strategy are adjusted in real time according to a prediction result, and cloud-side collaborative optimization is realized. Efficient data transmission, flexible task scheduling and self-adaptive resource management are realized, and the performance of the system in a complex and changeable environment is comprehensively improved.

The invention discloses a data center intelligent operation and maintenance method and system based on an industrial internet of things, belongs to the field of image processing calculation, and aims to solve the problems of privacy protection deficiency, serious manual dependence and weak energy efficiency and safety. Full-link dynamic optimization is realized through an edge calculation layer and an adaptive clustering network, edge nodes are integrated with a wavelet transform noise filtering technology, dynamic denoising is performed on multi-band sensor signals of temperature, vibration, current and the like, heterogeneous device time sequence data are aligned in combination with a dynamic time warping algorithm, sampling frequency differences of multiple devices are eliminated, and the multi-band sensor signals are obtained. A weight formula is put forward, a cluster head is dynamically elected based on node residual energy, data similarity and a load coefficient, a transmission path of a high-delay area is optimized in combination with a multi-hop routing protocol, the packet loss rate and the signal strength are evaluated in real time, redundant coding is started for a high-packet-loss link or the high-packet-loss link is switched to a low-power-consumption mode. And synchronizing the dynamic topology state with the digital twin platform through an open platform communication unified architecture protocol.

The invention discloses an edge computing resource allocation optimization method and system based on reinforcement learning, and relates to the technical field of edge computing resource management.The method comprises the steps that computing load data and network state data are collected, a historical load sequence is formed, a state space matrix is constructed, the historical load sequence is mapped into a discrete state value, and the discrete state value is obtained; establishing a deep neural network model; inputting the state space matrix into a deep neural network model, and generating a resource allocation strategy set according to the priority and time delay requirements of the calculation tasks; iteratively training the resource allocation strategy set by adopting a double Q learning algorithm, setting a reward function based on task completion time and a resource utilization rate, and selecting an optimal resource allocation strategy; and adjusting computing resource allocation of the edge nodes according to the optimal resource allocation strategy, and meanwhile, feeding back an execution result to the deep neural network model for online learning updating. According to the method, intelligent optimization of edge computing resource allocation is realized by introducing a deep reinforcement learning method.

The invention provides a private domain live broadcast data storage and visitor authentication method and system based on a block chain, and relates to the technical field of data storage and access control, and the method comprises the steps: collecting multi-mode biological characteristic data of a visitor, carrying out the feature extraction and fusion through a deep neural network of a space-time attention mechanism, and carrying out the authentication of the visitor; authenticity verification is carried out in combination with an adversarial generative network, a dynamic identity token is generated, and a hash value of the dynamic identity token is written into the block chain. The method comprises the following steps: performing semantic analysis on live broadcast data flow through a self-adaptive content perception model, constructing a double-layer access control matrix according to a data sensitivity level and a credibility score, performing self-adaptive fragmentation and hierarchical encryption based on federated learning, and then storing. After an access request is subjected to identity verification, an optimal edge node is selected according to access authority and network performance parameters to construct multi-path transmission, and a transmission strategy is generated based on a service quality score and a data sensitivity level. And when the service quality is lower than a threshold value, triggering the smart contract to perform fault migration.

The invention provides an intelligent Internet of Things public security management and control system and method based on multi-source data fusion, and belongs to the technical field of Internet of Things security. According to the system, unified collection and standardized processing of multi-source data are achieved by recognizing a system operation scene and loading corresponding model parameters and response strategies, and a unified semantic representation structure is constructed. And the system executes anomaly detection at an edge node, completes risk scoring and grading alarm judgment in combination with a semantic structure and anomaly feature information, and generates a control response instruction based on strategy matching. Meanwhile, dynamic optimization of the scoring model and identity authentication, behavior auditing and data compliance export in the operation process are supported. The system has intelligent identification, adaptive analysis and response closed loop capabilities, and improves the precision and credibility of public security management in the Internet of Things environment.

The invention relates to the technical field of micro-grid control, in particular to a multi-layer distributed micro-grid control system and method based on edge cloud collaborative lightweight reinforcement learning. The method comprises the following steps: a cloud management unit trains a DQN model by using historical environment data, generates a lightweight DQN model through pruning and quantification, issues the lightweight DQN model to an edge node unit, and formulates day-level and weekly-level optimization scheduling strategies based on a function layering + layering cooperation strategy; the edge node unit monitors electrical quantity and environmental parameters in real time through a sensor, deploys a lightweight DQN model to monitor abnormal conditions and trigger local early warning and emergency strategies, and makes a fast decision by using the lightweight DQN model; the micro-grid subgroup units collect data of the edge node units. According to the invention, through close cooperation of the edge node layer, the micro-grid subgroup layer and the cloud management layer, a distributed cooperative control mode of edge node real-time decision, subgroup cooperative management and cloud global optimization is realized.

The invention provides a chemical industrial park illegal behavior intervention and tracing system based on edge calculation, and aims to solve the problems that an existing system is single in perception capability, insufficient in intelligent analysis, limited in edge collaboration, passive in intervention, weak in tracing capability and the like. According to the system, an improved ResNet-FPN and Transform architecture is adopted to realize multi-modal data fusion, and violation behavior analysis is carried out through a double-flow intention understanding framework and a graph neural network in deep combination with chemical process knowledge; a self-adaptive edge node networking and federated learning mechanism is adopted to realize dynamic scheduling of computing resources and knowledge migration; an intervention decision model based on risk entropy supports predictive intervention, and a multi-objective optimization algorithm ensures balanced safety and efficiency of an intervention strategy; a complete evidence chain is constructed by adopting a multi-modal trajectory reconstruction technology and a distributed evidence storage network, and credible tracing of violation behaviors is realized. The technical bottleneck of a traditional system in the aspects of professional scene understanding and real-time processing is broken through, and the intelligent level of safety management of the chemical industry park is remarkably improved.

The invention relates to the technical field of energy scheduling, in particular to a multi-modal data fusion method and system based on energy scheduling and a storage medium. The method comprises the following steps: obtaining multi-modal data, and carrying out abnormal fluctuation feature extraction to obtain a space-time fusion abnormal feature labeling set; deducing a multi-objective optimization path according to the space-time fusion abnormal feature labeling set to obtain a dynamic scheduling decision map; performing edge node game equilibrium calculation according to the dynamic scheduling decision map to obtain a trusted scheduling verification chain; performing digital twinborn constraint optimization on the trusted scheduling verification chain to obtain a closed-loop scheduling digital twinborn body; compiling a dynamic scheduling instruction set based on the closed-loop scheduling digital twin to obtain an anti-disturbance energy scheduling strategy library; and obtaining real-time energy supply and demand data, and performing scheduling deviation tracing on the real-time energy supply and demand data according to the anti-disturbance energy scheduling strategy library to obtain an energy distribution decision. According to the invention, the efficiency and reliability of energy scheduling can be improved.

The invention provides a shield tunneling machine fault detection method and system based on edge computing, and relates to the technical field of industrial Internet, and the method comprises the steps: deploying edge computing nodes on a shield tunneling machine, and carrying out the time-space alignment and feature level fusion of data collected by different sensors, and obtaining unified feature data; inputting the fused feature data into an online learning model pre-deployed in an edge computing node to obtain a fault detection result; the edge computing node uploads the fault detection result and feature data related to the fault to the cloud server, generates a fault analysis report and feeds back the fault analysis report to the edge node and the user terminal; and the edge node periodically pulls the updated online learning model and knowledge base from the cloud to realize cloud-edge collaborative model updating and knowledge evolution.

The invention discloses an industrial equipment real-time monitoring system based on edge computing, and the system comprises the following steps: collecting, processing and analyzing the operation data of industrial equipment through distributed edge computing nodes, constructing a node cooperation mechanism, and completing the processing of the data; the dynamic data scheduling module is used for dynamically optimizing a data transmission path and task allocation according to node performance; the intelligent model dynamic migration module is used for adjusting lightweight model deployment in combination with node resource conditions; the multi-modal fault sensing module is used for extracting and analyzing multi-modal features by using a cross-node collaborative algorithm; the edge node self-adaptive management module is used for adjusting node operation parameters in real time and repairing an abnormal state; and the equipment relevance reasoning module is used for constructing an equipment relevance model through a graph calculation method and generating a real-time relevance graph. According to the method, efficient monitoring and anomaly detection of industrial equipment are realized through resource optimization and intelligent analysis of collaborative edge computing nodes.

A block chain double-chain cold chain traceability management system for meat quality fluctuation belongs to the technical field of traceability management systems and comprises a data acquisition and preprocessing module, a composite index module, a security verification module, a federated learning and AI resource management module, a fragmentation and verification architecture module and an automatic error correction module. According to the method, data chain storage environment data hash and transaction chain record operation logs are innovatively separated through a block chain double-chain architecture, hash fingerprints are synchronized in combination with an anchoring technology, cross-chain consistency is ensured, long-term non-tampering of data is realized through an anti-quantum hash algorithm, resource allocation is optimized through hierarchical storage design, and the method is suitable for large-scale popularization and application. The composite index technology is fused with hypergraph dynamic index to trace production batches, vehicles, warehouses and space-time quadtree dynamic subdivision areas, complex query response time is remarkably compressed, path deviation real-time alarm is achieved, the edge node cache hit rate is increased, and full-link data safety and query efficiency are remarkably improved.

The invention belongs to the technical field of data transmission, and particularly relates to an SD-WAN low-delay data transmission method and system based on edge computing, a plurality of edge computing nodes are deployed in an SD-WAN architecture, and the edge computing nodes are distributed on a network edge side, close to terminal equipment or a branch mechanism and have data preprocessing and local computing capabilities; monitoring network state parameters of each link in the SD-WAN in real time, wherein the network state parameters comprise delay, bandwidth, packet loss rate and computing resource utilization rate of edge nodes; routing to-be-transmitted data to the target edge node, and if the node has local processing capability, executing data preprocessing or caching operation; otherwise, forwarding the data to an adjacent edge node or a cloud data center; a distributed cache strategy is adopted, when network congestion is detected according to data access frequency and timeliness requirements, non-real-time data is temporarily stored in a local cache, and asynchronous transmission is executed after a link is recovered, so that the method has the effect of providing higher-quality and more reliable network service for a user.

The invention discloses an electric power capital construction process early warning method and system based on a high-precision three-dimensional model, and relates to the technical field of electric power capital construction, and the method comprises the steps: collecting multi-source data of an electric power capital construction site, and constructing a high-precision three-dimensional model comprising equipment space coordinates, construction time sequence labels and environmental parameters; inputting the space-time diagram into a space-time diagram convolutional network, outputting a risk propagation probability matrix of a three-dimensional space and a construction time sequence dimension, and identifying high-risk nodes and associated construction stages; and dynamically marking root cause coordinates and a risk diffusion range in the high-precision three-dimensional model, generating an early warning instruction containing a visual path, a root cause report and a disposal priority, and pushing the early warning instruction to a terminal in real time through an edge node. Based on the hierarchical space-time attention-physical constraint graph convolutional network, the inter-node cross-stage association weight is dynamically allocated, the convolution kernel parameters are optimized in combination with the physical connection strength, and the space-time association of high-risk node identification is significantly improved.

The invention relates to the technical field of machine tool intelligent monitoring, and discloses a numerical control machine tool intelligent monitoring method and system based on the Internet of Things and a storage medium. The method comprises the steps of collecting machine tool multi-source data and transmitting the data to an edge node; original data is processed to obtain a working condition feature set, the working condition feature set is input into a recursive convolution hybrid memory network to construct a common dependency relation graph, and state parameter prediction is realized through time-frequency domain joint analysis; converting the two-dimensional feature map into a two-dimensional feature map, and extracting interaction features through SE-ResNet; fusing the two types of features to execute state classification; and predicting the life and generating a diagnosis report based on the health state judgment result in combination with the process parameters and historical data. According to the method, effective fusion analysis of multi-source heterogeneous data of the numerical control machine tool is realized, and a self-adaptive state recognition and fault early warning mechanism is constructed, so that accurate monitoring of the operation state of the machine tool, intelligent diagnosis of early faults and accurate prediction of residual life are realized.

The invention discloses an operation and maintenance method and system of a multi-cloud management platform based on cloud edge collaboration, and the method comprises the following steps: S1, deploying cloud service and edge nodes, and initializing a cloud edge collaboration environment; s2, constructing an edge autonomous optimization network, adjusting and optimizing a task strategy, and predicting a task load of an edge node; s3, adjusting task priorities, and classifying task strategies; s4, constructing a task feature matrix, a cloud edge resource matrix and a task and resource matching model; s5, allocating resources to each task to generate a task resource allocation table; and S6, identifying operation abnormity through the time sequence abnormity detection model, and triggering task migration, resource switching or task redistribution when detecting that the edge node is abnormal. According to the method, the edge autonomous optimization network and the intelligent scheduling algorithm are utilized, dynamic task allocation and exception recovery are achieved, and the method has the advantages of being high in resource utilization rate, high in scheduling real-time performance and good in system stability.

An Al -based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure, coordination, and orchestration with distributed systems to support important use cases for a range of enterprises. An Al -based energy edge platform may include a graph neural network including a set of nodes respectively representing at least one distributed energy resource (DER) and a set of edges respectively interconnecting the set of nodes, wherein each edge represents at least one energy - related feature among at least two nodes of the set of nodes. 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 employ intelligent provisioning, data aggregation, and analytics to leverage energy market connection, communication, and transaction enablement platforms.

The invention provides a computing resource optimization method and system for analysis tasks, and belongs to the technical field of computers.The computing resource optimization method comprises the steps that the priority of each analysis task is determined according to feature information of each analysis task and current available computing resource state information of the system, and a priority queue is generated; obtaining a task load prediction value according to the historical task load data and the real-time system state data; according to the type of the analysis task, the data scale and the data source position, the analysis task with the real-time requirement higher than a preset standard is allocated to an edge node to be executed, and resource allocation of the edge node is dynamically adjusted according to a task load predicted value; and dynamically allocating available resources from the computing resource pool according to the priority queue and the task load prediction value so as to execute the plurality of parallel analysis tasks. According to the resource management method based on task priority dynamic adjustment, task load prediction and edge computing optimization scheduling, real-time prediction and dynamic self-adaptive scheduling of computing resources are achieved.

The invention discloses a big data auxiliary key generation method and system in communication data encryption transmission, and relates to the technical field of big data analysis and processing. The dynamic entropy source processing module is used for generating a high-randomness entropy pool by combining an information entropy quantification model and adopting a Shannon entropy and minimum entropy fusion algorithm; the anti-quantum key generation module is used for generating a dynamic variable-length key seed based on an entropy pool driven post-quantum cryptographic algorithm; the hierarchical key negotiation module adopts a clustering Diffie-Hellman protocol, dynamically divides negotiation according to network topology, and precomputes and reduces the load of a core network through edge nodes; and a lightweight verification and update module. According to the method, high-entropy sources such as environmental noise, user behaviors and equipment hardware fingerprints are fused with low-entropy sources such as network messages and sensor data, and an intelligent acquisition strategy and a nonlinear decorrelation technology are combined, so that the anti-quantum dynamic entropy pool is generated, and the randomness of a secret key and the reliability of the entropy sources are improved.

The invention provides a bandwidth regulation and control and visitor flow routing optimization method and system for multi-scene live broadcast, and relates to the technical field of intelligent routing, and the method comprises the steps: collecting live broadcast data, constructing a feature vector, and training a scene classification model; analyzing bandwidth requirements by using a time sequence prediction algorithm, and formulating a differentiated bandwidth allocation strategy; and an edge node load monitoring mechanism is established, a multi-target routing optimization model is constructed, and real-time scheduling optimization of visitor flow is realized. The method can effectively improve the bandwidth utilization rate and the user experience quality of the live broadcast platform.

The invention relates to the technical field of tunnel power supply and distribution, in particular to a tunnel modular prefabricated cabin power supply and distribution intelligent substation self-adaptive regulation and control system based on edge calculation. Comprising an edge calculation and AI decision-making unit which is used for realizing rapid acquisition, processing and instant decision-making of tunnel power supply and distribution multi-dimensional data, generating a power supply and distribution adaptive regulation and control strategy by deploying calculation resources and a machine learning algorithm at edge nodes close to a data source, and converting the strategy into an executable regulation and control instruction; a cloud platform collaborative management unit; and an intelligent sensing and internet-of-things unit. According to the invention, hierarchical decision control of the tunnel power supply and distribution system is realized by constructing a hybrid architecture of edge computing and cloud platform collaboration and a priority judgment mechanism; according to the invention, multi-modal data are integrated through the multi-protocol communication link module and the full-scene data fusion analysis module, and data association analysis is realized through Kalman filtering, D-S evidence theory and other algorithms.

The invention discloses a resource scheduling method, system and device in an edge cloud computing platform and a medium, and relates to the field of resource scheduling. In the method, a corresponding digital twinborn model is constructed for each edge node according to a resource state and an environmental parameter, a resource change condition in a future preset time is predicted according to the digital twinborn model, and a global resource state map is generated according to the resource change condition; obtaining a task demand of each task, and determining an execution position and a priority of each task according to the task demand and the global resource state map; generating a global allocation scheme through a federated learning algorithm according to the global resource state map, the execution position and the priority; and acquiring a resource allocation state in real time, determining the difference between the resource allocation state and the global allocation scheme, and correcting the global allocation scheme based on the difference through a hierarchical collaboration mechanism. By implementing the technical scheme provided by the invention, the effects of optimizing resource allocation efficiency, reducing delay and energy consumption and improving task processing performance are achieved.

The invention relates to an internet-of-things data processing method, system and device based on edge computing and a medium. The method comprises the following steps: acquiring original data acquired by an internet-of-things sensor, and acquiring resource state data and network topology information of an internet-of-things edge computing environment; preprocessing the original data to obtain preprocessed data, and performing transmission strategy analysis on the preprocessed data to obtain transmitted data; mapping the transmitted data into an edge node, and performing distributed analysis on the transmitted data to obtain a corresponding edge analysis result; performing multi-source data fusion according to the edge analysis result to obtain corresponding global data, and performing deep analysis based on the global data to obtain a corresponding intelligent analysis result; and carrying out resource scheduling analysis according to the resource state data and the intelligent analysis result to obtain edge node scheduling strategies, and carrying out comprehensive analysis on all the edge node scheduling strategies to obtain a global resource optimization strategy. According to the invention, dynamic allocation and optimal utilization of resources can be realized.

The invention discloses an Internet of Things data integration method and system based on dynamic semantic atlas and edge collaboration, and the method comprises the steps: carrying out the equipment access and protocol conversion, capturing the original data of equipment through an edge node, analyzing the average communication interval of 100ms + / -20ms through an LSTM model, generating an equipment fingerprint hash value, analyzing a message structure through a TinyBERT model, and extracting a key field, a temperature value and a timestamp. The method comprises the following steps: establishing a dynamic semantic graph, converting the dynamic semantic graph into a standard JSON format, uploading the standard JSON format to a cloud end through a service grid, constructing a dynamic semantic graph, collecting multi-source metadata by the cloud end, generating an entity relationship by utilizing a Transform model, dynamically optimizing a mapping rule based on reinforcement learning, automatically associating a Pressures field to a database column PSI, and carrying out collaborative optimization by an edge cloud; according to the method, the knowledge graph and the Transform model are combined, the data entity relation is updated in real time, the semantic drift problem of a traditional static graph is solved, the cross-domain data association accuracy is improved by 35%, the unique device fingerprint is generated through the message frequency and load distribution, and dynamic tracking of the mobile device is supported.

Various aspects of methods, systems, and use cases for multi-entity (e.g., multi-tenant) edge computing deployments are disclosed. Among other examples, various configurations and features enable the management of resources (e.g., controlling and orchestrating hardware, acceleration, network, processing resource usage), security (e.g., secure execution and communication, isolation, conflicts), and service management (e.g., orchestration, connectivity, workload coordination), in edge computing deployments, such as by a plurality of edge nodes of an edge computing environment configured for executing workloads from among multiple tenants.

The invention relates to the technical field of data processing, and discloses a real-time data processing analysis method and system of an industrial PLC. The method comprises the following steps: transmitting temperature, pressure, current, vibration and acoustic parameters acquired by multiple sensors to an industrial PLC (Programmable Logic Controller) in real time to obtain multi-source heterogeneous original data; preprocessing the multi-source heterogeneous original data to obtain standardized fusion data; correlation calculation and anomaly recognition are carried out through the multivariate analysis model, and an abnormal state classification result is obtained; dynamically adjusting data interaction frequency and sampling rate between the edge nodes and the central PLC, and generating a real-time control decision instruction; and matching the real-time control decision instruction with the current motor load fluctuation state, and outputting the optimal frequency conversion control parameter. According to the invention, the response delay of the system is reduced, the control precision and reliability are improved, the dynamic balance between the safety and the energy efficiency is realized, and the system can intelligently adjust the control strategy according to the real-time safety situation.

The invention provides a cloud collaborative three-dimensional building modeling rendering method and system based on WebGL (Web Graphics Library). The method comprises the following steps: acquiring design parameters of a target building, analyzing the design parameters into geometric topological data of a plurality of independent components on the basis of a functional attribute division rule, and generating a multi-stage detail geometric model; adding material physical attributes to the surface vertex data, calculating a diffuse reflection coefficient and specular reflection intensity through a ray tracing algorithm, and generating lightweight component data carrying physical rendering parameters; in response to a view angle switching instruction of a terminal user, according to a space block corresponding to a current view angle, a main GPU node is distributed from a distributed GPU cluster, the main GPU node is divided according to the space block, adjacent blocks of the space block are distributed to edge GPU nodes, parallel ray tracing calculation is executed based on a WebGL protocol, and the parallel ray tracing calculation is executed based on the WebGL protocol. And returning to the main GPU node synthesis terminal display image and displaying. According to the technical scheme provided by the invention, the response speed is remarkably improved while the physical-level rendering effect is kept.

A federated mining method for multimodal data based on multiple security policies includes: a federated learning framework is used as a data mining model for distributed data mining; a multiple authentication mechanism is designed; a local edge node is selected to participate in a federated computation to obtain authenticated local edge node and aggregate a dataset; a multimodal data fusion and a multimodal data classification are performed on the dataset by a generalized multimodal data feature fusion model based on a multi-headed attention mechanism; and an adaptive perturbation mechanism based on cyclic correlation analysis and differential privacy is designed to add noise round by round and dynamically. A federated mining method for multimodal data is further provided.

The invention provides a secure channel networking method based on edge computing power, and the method comprises the steps: obtaining the security parameters of a link between nodes in a network topology structure based on a selected networking strategy, including link encryption strength, an authentication mechanism and a key updating period, and combining the bandwidth and time delay of the link, obtaining an optimal secure communication route between the nodes through an edge calculation method; issuing the optimized node communication route to each edge computing node, and dynamically adjusting an authentication mechanism and a key management strategy of a secure channel between the nodes according to link security parameters including link encryption strength, a delay jitter threshold and a packet loss rate threshold; and according to the updated networking strategy, dynamically optimizing node roles and link redundancy, updating a network topology structure, and dividing edge nodes into three node function roles of computing nodes, storage nodes and forwarding nodes in combination with node computing capability, storage capacity and energy consumption level.