477 results about "Node deployment" patented technology

The invention discloses a power distribution network collaborative management method and system based on artificial intelligence, and relates to the technical field of electrochemical detection, and the method comprises the steps: constructing a distributed edge computing node network, deploying nodes at key positions of a power distribution network, achieving the collection and preprocessing of local power data, and reducing the cross-regional data transmission pressure; an AI real-time communication scheduling model is established based on the preprocessed data, communication resources are dynamically allocated according to the operation state of the power distribution network, and fault data transmission is guaranteed preferentially; seamless interaction of multi-protocol equipment is realized through a self-adaptive protocol conversion mechanism containing protocol identification, format conversion and data verification; training a fault diagnosis model by using a federated learning framework, and enabling edge nodes to only upload parameters to a coordination center for aggregation and updating, so as to balance model precision and data privacy; when a fault is detected, a millisecond response mechanism is started, and a processing strategy is generated and executed in combination with edge local decision and central global optimization.

The invention relates to the technical field of agricultural information management, and particularly discloses an agricultural information management system and method based on a big data platform, and the method comprises the steps: firstly deploying a multi-source data collection module at an edge calculation node, and obtaining and standardizing the soil moisture content, meteorological environment and equipment operation data in real time; secondly, constructing a local dynamic irrigation strategy model, and realizing multi-objective optimization through a reinforcement learning algorithm; establishing a federated learning framework at the cloud, dynamically distributing node weights by adopting an attention mechanism, and realizing model aggregation of privacy protection in combination with secure multi-party computing; an optimal irrigation instruction is generated through a multi-source data fusion engine, and a three-level response exception handling mechanism is established; and finally, a closed-loop feedback system containing short-term incremental learning and long-term architecture optimization is formed. The corresponding management system comprises six functional modules, namely a data acquisition module, a local modeling module, a federated learning module, a real-time decision-making module, an abnormal monitoring module and a closed-loop optimization module.

The invention discloses a network communication dynamic optimization method based on multi-module collaboration, and relates to the technical field of network communication, a dual-mode communication module is deployed at each network node, and the network communication dynamic optimization method comprises the following steps: each network node broadcasts own existence information and power line channel characteristics through an HPLC (High Performance Liquid Chromatography) channel of the dual-mode communication module; each network node scans surrounding wireless networks through an HRF channel of the dual-mode communication module and reports own wireless channel quality information to the gateway; the global state sensing module collects all information and constructs a global network view containing physical topology and a channel quality map. A dual-mode cooperation mechanism, a machine learning prediction model and a dynamic decision strategy can adapt to complex dynamic environments such as power line noise fluctuation and wireless interference change, and communication parameters can be autonomously optimized without manual intervention; and meanwhile, the modular design is convenient to expand to a multi-mode communication scene, and has a wide application prospect.

The invention discloses a big data driven dynamic resource scheduling method for an IPv6 edge computing node, and relates to the technical field of edge computing, and the scheduling method comprises the following specific steps: S100, resource data collection and integration: deploying monitoring equipment at the IPv6 edge computing node, collecting computing, network, storage and energy resource data, and transmitting the data to a server; through multi-dimensional resource data acquisition and storage, comprehensive monitoring and quantitative integration of calculation, network, storage and energy resource data are realized, the resource scheduling is more accurate and efficient through the comprehensive data acquisition mode, and through construction of the energy consumption performance model, the performance value can be accurately calculated, and the energy consumption performance is improved. And the model is trained and optimized by using big data, so that the balance of energy consumption and performance in the resource scheduling process is realized, the resource utilization efficiency is improved, the energy consumption cost is reduced, and powerful support is provided for green and sustainable development of edge computing nodes.

The invention relates to a wireless sensor network coverage optimization method based on a star-graffiti optimization algorithm, and belongs to the technical field of wireless sensor network coverage optimization. In order to solve the problems of low convergence precision, easy falling into local optimum and convergence hysteresis of a traditional coverage method, the invention provides an enhanced star-graffiti optimization algorithm (INOA) fusing a hyperbolic sine and cosine optimizer and a nonlinear storage strategy. Firstly, a population is initialized through Bernoulli mapping, and the global exploration capability in a high-dimensional scene is improved; secondly, designing a nonlinear convergence mechanism based on hyperbolic sine and cosine, and dynamically balancing global optimization and local development; and a dynamic nonlinear storage strategy is introduced, so that the convergence speed is remarkably increased. The method effectively improves node deployment optimization in a complex environment, has higher adaptability, improves convergence speed, precision and stability, and realizes node efficient coverage and energy consumption balance.

The invention relates to the technical field of power converters, in particular to a power converter real-time fault diagnosis method and system based on edge calculation. According to the method, operation data, including electrical parameters, thermal parameters and the like, of the power converter are obtained firstly, aging and quality are ensured through synchronous filtering, limitation of a traditional single parameter is broken through, tiny abnormity and instantaneous fluctuation can be captured, and redundancy is avoided; according to the equipment structure, edge computing nodes are deployed near a data source, the transmission distance is shortened, delay is reduced, electromagnetic interference is avoided, accuracy is improved, resources with excellent computing power are matched, and local real-time monitoring is supported. Next, an edge computing node lightweight algorithm is used for localization analysis, normal data and abnormal data are quickly distinguished, identification time consumption does not need to be reduced by transmitting to a cloud end, it is ensured that abnormality is found immediately according to misjudgment and sensitivity improvement of a dynamic baseline, and finally, thermal radiation information is obtained in combination with abnormal data, and a fault is accurately positioned by associating a device structure and parameters. And refining to an element level to shorten the troubleshooting time, and analyzing the trend to realize predictive maintenance.

The invention relates to the technical field of 5G-R network detection and monitoring, discloses a 5G-R network situation awareness method based on distributed monitoring and multi-source information fusion, and aims at solving the problems that a 5G-R network is large in scale, high in dynamic performance and complex in data isomerism. Multi-source data of a core network, a wireless network, special equipment, interface monitoring, detection equipment, a GIS and the like are collected in real time; according to the method, technologies such as Pearson's correlation coefficients, FP-Growth, Bayesian analysis, a time sequence point process, a Hookes theory, a Gaussian mixture model, a decision tree, S-ARIMA, Boxplot, N-sigma, iForest, regression analysis, a neural network and KL divergence are combined to realize multi-source data fusion, intelligent analysis and visual perception, including network alarm, application quality, operation and maintenance and resource management. According to the method, the comprehensiveness, the real-time performance, the fault diagnosis accuracy and the operation and maintenance efficiency of the 5G-R network are improved, and the requirements of low delay and high reliability of a railway scene are met.

The invention relates to an intrusion detection method based on cross-domain security management and a shared behavior model. The method comprises the following steps: acquiring multi-dimensional original data according to a preset cross-domain data acquisition rule and multi-domain node deployment; performing compliance, integrity and format matching degree verification on the original data, shielding sensitive information by using a dynamic desensitization technology based on a verification result, converting a heterogeneous data format, filtering missing field abnormal data, and obtaining compliance data; the method comprises the following steps: extracting a multi-dimensional feature set of user cross-domain access, constructing a shared behavior feature vector through weighted calculation, constructing a reference behavior model library in combination with a cross-domain security policy, and screening out intrusion behaviors and feature deviation data through feature comparison and behavior deviation calculation; according to the method, cross-domain security audit logs are fused for correlation analysis, intrusion behavior types and risk levels are judged by means of a Bayesian network model, differential security response strategies are generated and executed, and cross-domain intrusion detection and protection are achieved.

The invention discloses a service node deployment method, system and device, equipment and a storage medium, and can improve the deployment and debugging efficiency of service nodes. The method comprises the following steps: acquiring MCP Server configuration information and resource configuration information of service resources; generating deployment parameters used for operating the MCP Server and the service resources; sending the deployment parameters to a target server, so that the target server deploys an operation environment corresponding to the MCP Server based on the deployment parameters; in response to a debugging instruction for the service resources, performing debugging operation based on the running environment to obtain a debugging result; under the condition that the debugging result indicates that the service resource meets the preset operation condition, a service node construction request is sent to a target server, so that the target server constructs an MCP Server node in a to-be-called state, and node metadata of the MCP Server node is generated; and receiving the node metadata sent by the target server, and registering the node metadata in the MCP Server management platform.

The invention discloses a power grid internal network flow prediction and anomaly detection method and system based on federated learning and generative AI. According to the system, a local network flow collection and prediction model is deployed at each terminal node in a power grid, each node model is aggregated on a central server through a federated learning mechanism, and whole-network data collaborative learning is realized on the premise of not directly sharing original data. And the system integrated generative AI module is used for generating a synthetic network traffic sample to enhance model training data. The federated learning adopts an asynchronous aggregation strategy, dynamically adjusts the weight of each node, and carries out lightweight processing on the model to adapt to a power terminal environment with limited resources. The real-time flow is predicted and detected through the global prediction model, and when it is detected that the deviation between the network flow and a predicted value exceeds a preset threshold value, the system automatically starts a safety response mechanism. According to the technical scheme, the anomaly detection accuracy and response speed can be improved, and meanwhile, the power grid data privacy is protected.

The invention provides an intelligent contract driven workflow engine automatic execution method and system based on a block chain. The method comprises the following steps: constructing a knowledge graph through a historical workflow task, and identifying an association rule between a task type and a resource demand; deploying a monitoring agent at an edge computing node, collecting node load data, and interacting with the block chain smart contract through an encrypted data channel; when a workflow trigger event is detected, a resource pre-allocation strategy is generated in combination with the knowledge graph and the node load data; the block chain intelligent contract verifies the matching degree of the strategy and the current node resource, and after verification is passed, an encryption allocation voucher is generated and written into a block chain account book; the intelligent contract triggers the workflow engine to start the container instance group at the target node according to the voucher, and the task is automatically executed. According to the technical scheme, efficient and safe edge computing resource allocation and workflow automatic execution are achieved, and the resource utilization rate and the task execution efficiency are improved.

The invention relates to the technical field of business process management, in particular to a data processing method, system and device based on business process management and a medium, and the method comprises the steps: constructing a business process model driven by an abstraction layer, and configuring a multi-protocol standardized interface; according to the business process model driven by the abstraction layer and the multi-protocol standardized interface, a process instance matched with the business scene is generated, and a task is distributed to a target client according to a task demand; managing form state circulation and node jump logic according to a task control engine; an event monitor is deployed through a process key node to monitor the process, and when the process reaches a specified node, external service calling operation is triggered to form an end-to-end automatic closed loop. Therefore, the problems of code redundancy, high maintenance cost, difficulty in quickly adapting to diversified customer requirements and the like due to the fact that business process management generally needs to be subjected to customized development for different subsystems in related technologies are solved.

The invention relates to the technical field of large models, in particular to a large model distributed reasoning acceleration method based on multi-modal feature fusion and dynamic weight optimization, and the method comprises the following steps: S1, carrying out real-time semantic analysis through a reasoning context analysis module embedded in a load balancer to obtain semantic features; s2, calculating a cache adaptation degree according to the semantic features; s3, querying a global cache directory service to obtain a matched node list, and making a decision by using a multi-fusion decision algorithm in combination with various parameters of cache adaptation condition, node load condition and network quality; and S4, deploying a global cache directory service in the load balancing layer according to the decision, maintaining a local cache pool at each computing node, and synchronizing the cache metadata to the global cache directory service in real time. The method solves the problems that when a conventional load balancing strategy is adopted for multi-node deployment, waste of computing resources is easily caused, the overall energy consumption of the system is increased, and the request processing speed is reduced.

The invention provides a data lake construction method and system based on multi-source distributed data, and relates to the field of data lake construction. The method comprises the following steps: firstly, deploying a data acquisition agent at each node of a distributed data source and establishing a distributed collaborative mechanism, and performing structured analysis on data through the data acquisition agent to obtain data characteristics and establish a relationship between fields; and then semantic description information is constructed, and an RDF triple is generated based on the information and stored in a source end node. And then inputting the RDF triple into an ontology registration service for processing to generate a distributed ontology, and analyzing a corresponding relationship between the RDF triple and concepts in the distributed ontology to obtain a semantic equivalence rule and a mapping rule. And finally, organizing the distributed data sources into a unified data view according to the rules, establishing a distributed index, configuring an access permission, and finally constructing a data lake containing the unified data view, the distributed index and the access permission. According to the method, the performance bottleneck problem caused by centralized processing is avoided.

The embodiment of the invention provides a self-adaptive resource allocation method and device, and the method comprises the steps: collecting calculation node metadata and corresponding physical equipment information through deploying a daemon process at each node, associating the calculation node metadata with the corresponding physical equipment information, and constructing an allocable equipment pool; the real-time state of the node is sent to a cloud native scheduler, the cloud native scheduler generates a corresponding scheduling decision for the delay exceeding node obtained through judgment, the scheduling decision comprises an isolation decision and a migration decision, and in the migration decision, migration resource allocation is constrained based on an equipment pool, the scheduling decision of the cloud native scheduler is received, and node resources are scheduled. The method can improve the certainty of the hard real-time task and the overall efficiency of the system.

The invention discloses a carbon emission prediction method and system, and belongs to the technical field of carbon emission prediction, and the method specifically comprises the steps: collecting multi-source data, carrying out the preprocessing of the multi-source data, constructing a hierarchical federated block chain architecture, dividing a target area into block chain nodes, and carrying out the prediction of the multi-source data, each node deploys a lightweight sub-chain, generates a hash abstract with a timestamp and carries out chaining storage, data intercommunication is carried out between the sub-chains through a cross-chain interaction mechanism, the confidence degree of multi-source data is dynamically evaluated based on a data quality scoring model, the confidence degree is screened by using an intelligent contract, and the data quality is evaluated. Training a carbon emission prediction model by each district node by using local data, predicting the carbon emission of the district nodes, and generating a regional carbon quota allocation scheme according to a prediction result; according to the invention, the data is screened twice, so that the data quality and the accuracy of carbon emission prediction are effectively improved.

The invention relates to an underwater wireless sensor network path sensing routing method based on deep reinforcement learning, which comprises the following steps that: firstly, a node constructs and periodically updates a transmission preference model based on local and neighbor node interaction information; secondly, deploying a deep reinforcement learning model at each underwater sensor node to perform distributed routing strategy learning; and finally, generating a global guide vector by the sink node according to the routing path information of the received data packet, reversely spreading the global guide vector to the source node, fusing the global guide vector with a local transmission preference vector of the node to generate a guide reward, optimizing the deep reinforcement learning model, and updating a routing strategy. According to the method, the problems of difference and complexity of underwater transmission tasks can be solved, and the network data transmission efficiency and the overall service quality are improved in combination with local preference and global guidance while the node online learning is kept to adapt to the dynamic underwater environment.

The invention provides a load balancing method and device, electronic equipment and a storage medium, and relates to the technical field of servers, load monitoring is carried out on resource nodes in a server, load monitoring data is acquired, virtual machines are deployed in the resource nodes, and computing services of the virtual machines are bound to the resource nodes of corresponding classifications according to service types; performing load prediction on the resource nodes to obtain load prediction data; determining whether to migrate the virtual machine to other resource nodes according to the load monitoring data and the load prediction data; and in response to the determination of migrating the virtual machine to the other resource nodes, migrating the virtual machine to the target resource node, and binding the virtual machine with the target resource node. According to the invention, the load monitoring is combined with the service type binding mechanism, so that accurate matching between the resource node and the virtual machine demand can be ensured, and waste caused by resource mismatching is avoided. The virtual machine is actively migrated to the low-load node through load prediction, and the node load balance degree can be improved.

The invention relates to the technical field of heterogeneous network optimization, and discloses a heterogeneous network dynamic routing collaborative optimization method based on reinforcement learning, which comprises the following steps: S1, deploying an agent at each node of a heterogeneous network, and initializing a state sampling period and an action space; s2, sensing the state of the heterogeneous network in real time and obtaining sensing data; s3, encoding the sensing data into a state vector; s4, designing a layered dynamic reward function, including short-term reward and long-term reward; dynamically setting a weight ratio according to the scene type and outputting the weight ratio as a comprehensive reward; s5, carrying out multi-agent collaborative decision-making, which comprises the following steps: constructing a local topological graph based on neighbor state information broadcasted by nodes; each agent selects an action based on the state vector through the DQN; and updating the network weight of the DQN by using the comprehensive reward. According to the invention, transmission real-time performance and sustainability can be considered, transmission delay and transmission cost are reduced, and the method is suitable for film and television production and industrial Internet of Things scenes with variable topology and complex interference.

The invention discloses a cross-chain transaction-oriented smart contract collaboration method, and belongs to the technical field of cross-chain contract collaboration, and the method specifically comprises the steps: deploying a log collection assembly at each cross-chain transaction smart contract node, and recording log information in real time; based on a cross-chain collaborative typical risk scene, various risks are converted into quantifiable detection rules, and a risk rule model reads node log streams in real time; when the risk rule model detects an exception, forming a complete cross-chain calling graph according to the transaction association identifier in the node log; extracting operation features of nodes in the cross-chain calling atlas, comparing the operation features with a normal node feature library, and screening out suspected fault nodes of which the operation features deviate from a threshold value; the suspected fault node abnormal data, the graph fragments and the log information are integrated, a unique responsibility affirmation abstract is generated, and the unique responsibility affirmation abstract and complete materials are stored in a shared evidence storage network to form a non-tampering certificate; and after the voucher is generated, calling a responsibility investigation intelligent contract, and executing cross-chain responsibility investigation operation according to the fault node identifier and the responsibility terms.

The invention belongs to the cross technical field of crowd sensing, air monitoring and artificial intelligence, discloses an intelligent air monitoring node deployment method based on crowd sensing, and aims to solve the problems of insufficient node deployment density, response lag, monitoring blind areas and the like in an existing air quality monitoring system. Firstly, crowd sensing information related to air pollution is extracted from multi-source data such as social media and a public reporting platform, pollution events and spatial positions of the pollution events are recognized through natural language processing and image recognition technologies, and a dynamic pollution sensing thermodynamic diagram is constructed. On the basis, a deep reinforcement learning algorithm framework is designed, factors such as the perception coverage rate, the deployment cost and the communication connectivity are comprehensively considered, and an optimal deployment scheme of the air monitoring nodes is learned and output. The intelligent deployment method designed by the invention has self-adaptive capability, and dynamically adjusts the deployment strategy of the spatial quality monitoring nodes according to the crowd sensing data, thereby improving the response capability of the system to sudden pollution events.

The invention discloses an intelligent power distribution network fault prediction and self-healing method based on big data analysis and edge computing, which comprises an edge computing node, a cloud server and a communication module, and is characterized in that the edge computing node and the cloud server are respectively in communication connection with the communication module; the edge computing nodes are used for collecting current, voltage and temperature data in real time, the edge computing nodes are deployed at section switches of a power distribution network, and the cloud server is used for storing historical data and training a fault prediction model; and the communication module is used for realizing data interaction between the edge node and the cloud, and supports 5G / optical fibers. The method has the advantages that the real-time performance is improved, and the fault response time is shortened from the hour level to the second level. And multi-source data fusion enables the fault identification accuracy to be greater than or equal to 95%. And privacy and cost optimization: the cloud data transmission amount is reduced by more than 50% through edge calculation, and user data is protected by differential privacy. And adaptability is enhanced, and a complex power distribution network topology containing distributed energy is supported.

The invention provides a multi-agent collaborative decision-making system and method for intelligent manufacturing, and relates to the technical field of multi-agent collaboration. Multi-source heterogeneous sensing data are collected and processed, and various sensing feature data are extracted; constructing an edge computing node, deploying a convolutional neural network model at the edge computing node, processing various sensing feature data, and judging abnormal conditions of various sensing data; constructing a digital twinborn model, and when an abnormal condition of an edge computing node is received, integrating various sensing feature data by the digital twinborn model, generating a collaborative decision strategy, and performing collaborative control on multiple groups of agents; and task allocation is performed on a plurality of intelligent monomers in each group of intelligent agents based on an operation cost function, a capability balance condition and a priority, so that real-time perception and dynamic optimization of the manufacturing process are realized.

The invention belongs to the technical field of online quality inspection, and relates to an operation and maintenance management and running state monitoring method of an online quality inspection system, which comprises the following steps: deploying sensors and monitoring equipment at key nodes of the quality inspection system, and collecting equipment running state data, quality inspection data and key parameters of specific algorithm running; and carrying out calibration, verification and encrypted transmission on the acquired data, and setting a synchronization period to synchronize the data to a central server. By constructing an accurate equipment state evaluation and fault diagnosis model, the accuracy of fault early warning and positioning is improved, and powerful support is provided for rapid fault repair; maintenance arrangement is carried out in combination with a production plan, the influence of maintenance on production is reduced to the greatest extent, a system performance optimization module improves the load balancing capacity and the alarm response efficiency of the system, the stability and reliability of the online quality inspection system are enhanced, high-efficiency proceeding of quality inspection work is guaranteed, the production efficiency and the product quality of enterprises are improved, and the economic benefits of enterprises are improved. And the market competitiveness of enterprises is enhanced.

The invention relates to the technical field of distributed control systems, and provides a fault diagnosis and early warning method and system for a distributed control system, and the method comprises the steps: collecting mechanical vibration data, equipment surface temperature field distribution data and power supply current waveform data in real time through deploying a plurality of groups of sensor arrays at key nodes of the distributed control system; and comparing the fault mode with a preset safety threshold and a fault mode in a historical fault case library. And when the data is abnormal, the system tracks a vibration propagation path of adjacent nodes through graph structure modeling, locates a fault source in an amplitude difference direction, and matches an undetermined fault topology network generated in real time with a predefined reference fault topology network. The propagation characteristics of the vibration waveform are utilized to reversely deduce the fault origin, and intelligent diagnosis is realized in combination with a historical fault mode library. The dynamic analysis of the fault trend is realized, and the fault identification accuracy is improved through real-time data comparison and topology matching.

The invention provides a simulation deduction method and system based on distributed parallel scheduling and a storage medium, and is applied to the technical field of data processing.The method comprises the steps that proxy services are deployed at a plurality of preset computing nodes, resource state information of all the nodes is dynamically collected, and a computing resource pool is obtained; arranging a pre-registered plug-in based on the data dependency relationship to obtain a simulation task process; in response to simulation scene configuration submitted by a user, generating a distributed scheduling task containing a fragmentation strategy; determining a plurality of sub-tasks corresponding to the distributed scheduling task based on the simulation task process; and based on the real-time load of the computing resource pool and the task priorities of the plurality of sub-tasks, respectively distributing the plurality of sub-tasks to a plurality of target computing nodes for task execution, and obtaining task execution result information. According to the method and the device, the time delay performance of distributed scheduling and the high availability performance of scheduling simulation tasks can be improved.

The invention relates to the technical field of power system monitoring, and discloses an intelligent power grid fault monitoring method based on artificial intelligence, which comprises the following steps: step 1, acquiring multi-modal sensing data through a multi-modal sensing network deployed on power grid equipment, the multi-mode sensing data comprises gas characteristic data acquired by an olfactory chip array, temperature data acquired by a distributed optical fiber sensor and electrical data acquired by a current transformer; and step 2, sending the multi-modal sensing data to an edge computing node, wherein a first data processing model is built in the edge computing node. According to the method, the technical scheme that the lightweight model is deployed through the edge computing nodes and graded early warning is linked is adopted, the technical effects of real-time response and rapid fault isolation are achieved, and compared with the scheme depending on cloud centralized processing in the prior art, the defect of protection action lagging caused by fault diagnosis delay is overcome.

Techniques are disclosed relating to upgrade groups. A node of a computer system may access metadata assigned to the node during deployment of the node. The node may be one of a plurality of nodes associated with a service that is implemented by the computer system. The node may perform an operation on the metadata to derive a group identifier for the node and the group identifier may indicate the node's membership in one of a set of groups of nodes managed by the service. The node may then store the group identifier in a location accessible to the service.

The invention relates to the technical field of smart city public safety, in particular to an urban gas pipeline leakage risk intelligent early warning and positioning method and device.Firstly, a digital twinborn construction and dynamic coupling module constructs and drives a virtual image of a gas pipe network, then, a multi-modal data acquisition and fusion module comprehensively senses the state of the pipe network, and finally, the leakage risk of the gas pipeline is monitored. The intelligent early warning and positioning decision module realizes a core intelligent algorithm; a cloud-edge-end collaborative architecture is adopted, and a terminal sensor is responsible for data acquisition; the edge computing node is deployed in a regional pressure regulating station and is used for carrying out primary data processing and local real-time alarming; the cloud center deploys a digital twin platform and a core AI algorithm model, and performs big data analysis, simulation deduction and global decision. Compared with the prior art, the method can realize early and accurate identification and prediction of gas leakage and potential risks (such as abnormal stress) of the pipeline.

The invention provides an aluminum electrolysis cell multi-source data fusion acquisition system, and relates to the technical field of electrolytic aluminum. The system comprises a 5G edge computing node and a central control system which are in communication connection, the 5G edge computing node is further electrically connected with field execution equipment, and the 5G edge computing node is deployed in an electrolytic cell field and has a 5G communication module, an embedded computing unit and a multi-interface access capability. The 5G edge computing node comprises a multi-source data acquisition module, a data fusion and preprocessing module and a data uploading and feedback module, the multi-source data acquisition module is electrically connected with a plurality of sensors and cameras on an electrolytic cell, real-time acquisition of multi-dimensional data is realized, timestamps are stamped on various sensor data through a unified time reference, and the data are uploaded to the electrolytic cell through the data uploading and feedback module. And the heterogeneous data is fused based on the timestamp, so that the data acquisition time difference is eliminated, and the data consistency is improved. According to the invention, high frequency, multi-source fusion and low delay can be realized so as to adapt to higher requirements of the industrial Internet of Things on real-time performance and intelligence.