227 results about "Multi layer network" patented technology

The invention relates to the technical field of port automatic loading and unloading, in particular to a ship loading and cabin changing track planning method and system based on machine vision, and the method comprises the following steps: constructing a multi-modal information sensing array based on machine vision, obtaining the space information of a cabin, the stacking information of cargoes and the state information of loading equipment, a multi-modal information sensing data set is constructed; constructing a space-time correlation characteristic matrix of the cabin, the goods and the loading equipment; a multi-layer network architecture is used for preliminarily planning the ship loading and cabin changing track to obtain an initial ship loading and cabin changing track; constructing a dynamic digital fitting model, and fitting the initial ship-loading and cabin-changing track to obtain an optimized ship-loading and cabin-changing track; and establishing a real-time environment quantification model to perform real-time dynamic adjustment on the optimized ship-loading and cabin-changing track, and completing planning of the ship-loading and cabin-changing track. The system flexibly adapts to complex loading task requirements, and the intelligent level and execution efficiency of ship loading and cabin changing are remarkably improved.

The invention relates to the technical field of image recognition, in particular to a method, a system and equipment for generating a face recognition fusion model. The method comprises the following steps: acquiring a historical face recognition image set and historical equipment sensing data through face recognition equipment, and performing multi-modal data fusion integration to obtain a multi-source fusion data set; performing multi-layer network spatial-temporal feature integration on the multi-source fusion data set to obtain a high-dimensional feature spectrum; performing modal information flow modeling based on the high-dimensional characteristic spectrum to obtain a modal correlation matrix, and performing adaptive modal contribution degree weighting to obtain a modal relation matrix; performing face recognition time sequence adaptive modeling according to the modal relation matrix to obtain a face recognition time sequence adaptive model; and performing model evaluation on the face recognition time sequence adaptation model, establishing a precision optimization matrix, and optimizing the face recognition time sequence adaptation model by using the precision optimization matrix to obtain a face recognition fusion model. According to the invention, the accuracy and real-time performance of face recognition can be improved.

The invention discloses a voice decoding method, system and device based on electroencephalogram signals and a medium, and relates to the technical field of electroencephalogram signal processing.The method comprises the steps that reading electroencephalogram signals and voice signals in the reading process of a to-be-tested person and imagination electroencephalogram signals in the imagination reading process of the to-be-tested person are collected; inputting the reading electroencephalogram signals and the voice signals into the DRCL to generate electroencephalogram characteristics containing voice information; training the DBM by using the electroencephalogram characteristics as input and using the voice signals as output, and adjusting the DBM by using imaginary electroencephalogram signals to construct a voice synthesizer; a mapping relation between the imaginary electroencephalogram signals and the voice signals is generated through the voice synthesizer, and decoding from the electroencephalogram signals to the voice signals is completed; according to the method, a deep representation correlation learning method is provided, potential correlation between electroencephalogram and voice signals can be deeply mined through a multi-layer network structure, a complex mode which is difficult to recognize by a traditional model is captured, and the voice decoding process is more accurate.

The invention provides a hospital infection propagation path tracing method based on multi-source data fusion, and belongs to the technical field of infection propagation paths in hospitals, and the method comprises the steps: building a data set through collecting hospital multi-source infection monitoring data, constructing a multi-layer network propagation model comprising physical contact, spatial proximity and time co-occurrence, and carrying out the tracing of the multi-source infection propagation path. Community detection is carried out by using a random block model, infection propagation path identification is converted into graph coloring problem solving, an individual behavior heterogeneity model is established, and SEIR model propagation parameters are corrected by fusing individual characteristic parameters such as the immune state of a patient and the protection level of medical staff; a propagation situation awareness model based on a hybrid expert mechanism is adopted to optimize propagation parameter estimation, and a propagation parameter weight coefficient is obtained through a game optimization mechanism and Nash equilibrium solution for iterative optimization. And finally, outputting a high-confidence infection propagation path sequence and a propagation source positioning result by adopting a propagation path backtracking algorithm in combination with spatial-temporal correlation analysis.

The invention relates to the technical field of node identification, in particular to a network key node identification method fusing improved measurement and propagation influence, which comprises the following steps: setting an edge weight and a cross-layer dependency relationship of an infrastructure network; constructing weighted cross-layer local contact centrality, attenuation cross-layer path centrality and random walk centrality indexes; taking the maximum value of the three adaptive scores as an improved measure of the node; defining metric value standardization; calculating a propagation influence value, standardizing the measurement value, and carrying out weighted fusion on the measurement value and the propagation influence value to obtain a fusion score; carrying out propagation influence value convergence judgment by utilizing an LT linear threshold model; and arranging the nodes based on the fusion score value, and outputting a key node identification result. The method solves the problems that a traditional key node identification method only regards a multi-layer network as a plurality of isolated single-layer networks, lacks measurement of node heterogeneity in the multi-layer network, ignores a coupling relation between network layers and is difficult to be accurately applied to an urban multi-layer infrastructure network.

The invention relates to the technical field of power transmission and distribution network collaborative optimization, in particular to a power transmission and distribution network collaborative optimization method and system considering a power and traffic multi-layer network coupling model, and aims to solve the problems that a power transmission and distribution network collaborative optimization model considering power-traffic network coupling is difficult to converge and time-consuming to solve. A McCormick envelope relaxation method is adopted to carry out relaxation of non-convex constraint, and a large M convex optimization balance method is adopted to determine an upper bound and a lower bound of a better variable. As the operation feasible region of the power distribution network at the single moment is influenced by the current state parameters of the system, the prediction of the operation feasible region of the power distribution network at the single moment is realized by adopting a deep learning method, and meanwhile, in order to improve the prediction accuracy, approximate processing is performed on the lower boundary of the operation feasible region of the power distribution network.

The invention discloses a spatial domain identification method and device based on multi-modal topological consistency, and belongs to the field of transcriptome spatial domain identification, and the method comprises the steps: constructing a multi-layer network which is in one-to-one correspondence with modal information contained in a biological tissue based on spatial transcriptomics data of the biological tissue; extracting a consensus structure feature shared by the multi-layer network and a specific structure feature specific to each layer of network; constructing a cell consistency network of the biological tissue according to the consensus structural features, the specific structural features and the multilayer network; and performing clustering processing on the cell consistency network to obtain recognition results of different spatial domains in the biological tissue. According to the method, the heterogeneity problem among different modal data can be overcome, and the spatial domain recognition effect is better.

A multi-party joint fine-tuning language model method and device based on data protection, multiple parties comprise a first party holding a target language model and a second party providing computing power, the target language model comprises an embedding table and a plurality of serially arranged network layers, and the first party determines a plurality of confusion layers and a confusion embedding table; at least sending the multi-layer confusion layer to a second party; the confusion embedding table is obtained by performing second confusion on the embedding table, the confusion layer is obtained by performing first confusion on a parameter matrix in the corresponding network layer, and the first confusion mode corresponds to the second confusion mode; the second party obtains a training sample comprising a target confusion word embedding sequence and a label thereof, wherein the training sample is determined based on the text and the confusion embedding table; based on the target confusion word embedding sequence, obtaining a first output result through multiple confusion layers; and based on the difference between the first output result and the tag, adjusting a multi-layer confusion layer to realize a multi-party joint fine tuning model on the premise of protecting model parameters and data privacy.

The invention provides an automatic image matting method based on a generative adversarial network, and designs a generative adversarial network framework which comprises a generator and a discriminator. The generator adopts a multi-layer network structure, can receive an original image, and outputs a fine foreground object and transparency thereof through an encoder-decoder network. And the discriminator guides the learning process of the generator by evaluating the authenticity of the generated result, so that the quality of the generated alpha graph is gradually improved. Besides, the invention also designs an SE-ASPP module, which is combined with cavity convolution of different expansion rates, captures information from local to global, enables the model to capture and utilize key features of images at different resolution levels, a dynamic gating attention DGMA module, reduces parameter quantity through depth separable convolution, and improves calculation efficiency through a dynamic gating weight generator. And the accuracy and the meticulous degree of the matting effect are improved. According to the method, the foreground object is accurately extracted from the given input image, the transparency value of each pixel is estimated, and the method is suitable for processing the image with the foreground object with a complex background and rich details.

The invention relates to the technical field of power grids, and provides a multi-modal feature-based fault diagnosis method and a multi-modal feature-based fault diagnosis system for a network construction type energy storage system, which are used for sequentially carrying out normalization, multi-scale convolution, dynamic weighting of an attention mechanism, spatial-temporal feature extraction of a bidirectional long-short-term memory network and weighted pooling of the attention mechanism on acquired multi-modal data. According to the multi-modal-feature-based fault diagnosis method for the network-building type energy storage system, through multi-scale convolution, attention mechanism dynamic weighting, bidirectional long-short-term memory network spatial-temporal feature extraction and attention mechanism weighted pooling, each modal feature can be effectively extracted, the multi-layer network depth extension requirement of deep learning is avoided, and the fault diagnosis efficiency is improved. Model parameter quantity can be effectively reduced, dependence on cloud computing is reduced, and the method can be conveniently deployed on edge equipment; and transient faults and progressive faults in the multi-modal features can be effectively identified, and the fault diagnosis precision is guaranteed.

In one embodiment, a method may implement a multi-layer cybersecurity model for a multi-layer distributed computer system which comprises a sensitive data resource, such as a computing environment with an operational technology (OT) layer with multiple zones, an information technology (IT) layer, a DMZ, and a cloud layer. The method can assess a policy based on a zero-trust model for the sensitive data resource. The method can receive one or more requests, at any layer of a multi-layer distributed computing system, to access the sensitive data resource and acquire identity information for a user account specified in the first request. The method can perform a multi-layer multi-factor authentication of the user account using the identity information and the multi-layer cybersecurity model. In response to authenticating the identity information, the method can acquire sensitive access data corresponding to the identity information. The method can determine a sensitive resource access value using the sensitive access data and the zero trust model. In response to determining the sensitive resource access value is above a predetermined threshold, the method can authenticate the user account.

The invention discloses a fine-grained image classification method based on large model enhancement, which guides a model to learn a specific judgment mode of a task by directly introducing prior knowledge. A series of image descriptions are generated by performing question and answer interaction with a multi-modal large language model (MLLM). In order to filter illusion information and redundant content existing in description, a dual-guide text feature optimization module is introduced, and the quality of text features is improved through task-guided feature selection and similarity-guided feature pruning. And finally, adopting a multilayer network structure based on an attention mechanism to realize vision-language fusion for final classification prediction.

The invention discloses a spatial auditory attention dynamic coupling analysis method and system based on a multilayer network, and the method comprises the following steps: collecting electroencephalogram signals of a plurality of brain regions induced by a spatial auditory attention task in a complex acoustic environment, and carrying out the electroencephalogram data preprocessing; constructing a multi-layer rhythm network and a multi-layer time-varying network; constructing a cross-time-frequency coupling super-connection network based on the multi-layer rhythm network and the multi-layer time-varying network brain configuration, and determining a core network layer of the cross-time-frequency coupling super-connection network by using interlayer correlation and interlayer conditional probability; and carrying out quantitative analysis on the network attributes of the core network layer to obtain a connection mode and a cooperative working mode between the responsible brain regions. According to the method, the four-dimensional hyperconnection network is constructed, frequency and time information is integrated, and a complex interaction mechanism of spatial auditory attention is accurately and quantitatively analyzed.

The present invention provides a smoke detection method, system, medium, electronic device and smoke detection model; the smoke detection model is a multi-layer network structure, and the smoke detection model includes: a feature extraction network and a target feature enhancement network; wherein, the feature extraction network is used to receive monitoring data of a target area, and is used to extract features from the monitoring data to obtain multi-scale features; the target feature enhancement network is connected to the feature extraction network, and the target feature enhancement network is used to receive the multi-scale features, and is used to obtain target features based on the multi-scale features, so as to realize smoke detection of the target area based on the target features; in view of the problem of low accuracy of existing smoke detection algorithms, the present invention proposes a novel smoke detection model. Through the design of the target feature enhancement network, the smoke detection model can better focus on the smoke movement area, enhance its recognition ability for small targets, and thus improve the accuracy of smoke detection.

The invention belongs to the field of drainage pipeline siltation intelligent diagnosis, and particularly discloses a drainage pipeline siltation intelligent diagnosis method fusing an improved IKAN and a Transform network, and the method comprises the steps: collecting a drainage pipeline inlet and outlet flow and flow velocity data sequence, and carrying out the standardization preprocessing; an improved IKAN module based on adaptive learning is constructed; performing local feature extraction by using a one-dimensional convolutional layer; the feature data is mapped to a high-dimensional token space through a Tokenization module; the method comprises the following steps of: constructing a multi-layer Transform network fused with an improved IKAN (Internet Kalman Area Network); and a double-task learning framework is adopted, and regression prediction results of the deposition thickness and the deposition length and a classification result of whether deposition exists are output at the same time. The optimal performance is obtained when the time step is 15 seconds, the RMSE, the MSE and the MAPE reach 0.282, 0.248 and 0.185 respectively when the noise sample proportion is smaller than 50%, and the method has high robustness and prediction accuracy. According to the method, the accuracy and efficiency of drainage pipeline siltation diagnosis are remarkably improved, and a new technical path is provided for intelligent operation and maintenance of an urban drainage pipe network.

The invention relates to the technical field of public service networks, in particular to an adaptive network evolution public service radius measuring and calculating method, which comprises the following steps that: according to service contents provided by a public service, the public service is divided into a plurality of functional units, and each functional unit comprises at least one service point; constructing a public service network matched with the public service; setting a network evolution mechanism for the public service network to realize self-adaptive evolution of capacity, connection and modular growth of the public service network; setting a dynamic adjustment strategy to realize resource flexible scheduling of the public service and optimization of a service coverage range; obtaining a service coverage area, obtaining a service radius of each service point in the service coverage area based on a multilayer network model, and forming an optimal service radius vector set in combination with the number of demand points covered by each service point and resource consumption under each service radius; and determining the optimal coverage radius and the optimal service radius of each service point.

Systems and methods for dynamic bandwidth allocation and jitter handling in multi-tiered satellite networks such as virtual communication networks and / or physical communication networks. The system includes bandwidth distributing (BD) unit, in-route group managers (IGMs), in-route bandwidth manager (IBM). The system functions in multi-tier network entity management mode, and multi-beam management mode. The system operates by periodically collecting bandwidth reports from IGMs. The BD unit analyzes data in report and compares aggregated bandwidth demand with pre-defined thresholds. If thresholds are exceeded, system switches to multi-tier network entity management mode. In this mode, BD unit determines individual bandwidth adjustments for virtual network and beams and allocates bandwidth for individual devices within virtual network. Further, BD unit transmits this information to IBM, which applies allocations, and receives and transmits a scaling factor back to IGM indicating utilization of allocated bandwidth. Further, BD unit switches back to multi-beam management mode based on comparison result.

The invention provides a weighting network key node identification method based on layering. The method comprises the following steps: step 1, establishing a multi-layer network initial model based on a layering network thought; 2, selecting two layers of networks with the maximum similar modularity gain after combination to perform iterative combination, and obtaining a multi-layer network model by taking the similar modularity gains of any two layers of networks are non-positive as a loop termination condition; step 3, considering betweenness indexes of each network node in the multi-layer network model, measuring importance of each network node in the multi-layer network model, and dividing the network nodes into interactive nodes, first-level non-interactive nodes and second-level non-interactive nodes; and 4, evaluating the importance of the interactive nodes, the primary non-interactive nodes and the secondary non-interactive nodes by adopting three betweenness indexes. According to the method, multi-layer network modeling is carried out on the premise that the global attributes of the key nodes of the complex network have limitation, and the recognition accuracy of the key nodes in the network nodes of the multi-layer network is ensured.

The invention relates to the field of integrated circuit testing, provides an SRAM (Static Random Access Memory) fault testing structure, and particularly relates to an IEEE (Institute of Electrical and Electronic Engineers) 1687 standard and an SRAM self- According to the method, a common fault model of an SRAM (Static Random Access Memory) is analyzed, improvement is performed on the basis of a March C-algorithm, and a March C-pro algorithm capable of covering most common faults of the SRAM is derived; a modularized SRAM self-test structure is designed based on a March C-pro algorithm, so that the design cost caused by built-in self-test of a memory and possible additional interface requirements are avoided; a multi-layer network structure based on the IEEE 1687 standard is designed, access to an embedded instrument is achieved, an SRAM self-test module is connected, and an SRAM test in the SOC is achieved.

The invention belongs to but is not limited to the technical field of artificial intelligence, and particularly relates to a multi-strategy RAG process optimization method and system for large model questioning and answering, and the method comprises the steps: carrying out the classification processing of a document of a user, and selecting semantic blocks or customizing the sizes of the blocks; receiving a text input by a user; the retrieval is divided into two parts, one part is semantic vector retrieval, and the other part is keyword retrieval; the system further screens and filters candidate text paragraphs in combination with structured meta-information on the basis of vector retrieval; after the preliminary vector retrieval, a bge-ryanker-v2-m3 model is introduced to carry out secondary sorting on the results; the model firstly receives input Prompt and converts the Prompt into vector representation; the vector is processed through a multi-layer Transform network so as to extract and model semantic features in the text; a corresponding output result is generated in combination with specific task requirements defined by Prompt; and outputting an answer generated by the large model for the user question.

The invention discloses a data transmission network optimization method and system for a low-altitude aircraft, and relates to the technical field of unmanned aerial vehicle network optimization, and the method comprises the steps: firstly starting the low-altitude aircraft, setting local communication parameters, and building an initial network registry; periodically broadcasting data by the aircraft, and creating a multi-layer network topological graph; link communication quality prediction is carried out, and relay node path adaptation optimization is carried out according to the link communication quality prediction; a relay node is selected according to an optimization result to establish a real-time data communication path; and finally performing data transmission management based on the selected node and path. The technical problems of poor communication stability, insufficient real-time performance and low data transmission reliability caused by single-dimensional evaluation, insufficient dynamic adaptability and low path optimization efficiency of a traditional communication network optimization method are solved, and multi-dimensional feature fusion, dynamic topology response and multi-target path optimization are achieved. And the technical effects of stability, real-time performance and data transmission reliability of a communication network of the low-altitude aircraft are further improved.

The invention discloses a cascade fault analysis method based on digital twinning and physical power and related components. The method comprises the following steps: establishing a double-layer coupling network model through a digital twinning virtual network and a physical power network; setting a cascade fault propagation process through the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy so as to construct a multi-layer network cascade fault model; updating the multi-layer network cascade fault model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascade fault model; and performing cascade fault analysis on a specified power system by using the target cascade fault model. According to the method, the target cascade fault model is constructed based on the coupling symbiosis between the digital twin virtual network and the physical power network in combination with the communication delay, and the analysis accuracy and reliability of the cascade fault of the power system can be effectively improved through the target cascade fault model.

The invention belongs to the technical field of ship navigation risk management, and particularly relates to a ship navigation risk control method. Comprising the following steps: acquiring a ship prepared standard data file through a ship navigation service system; determining corresponding elements under the accident type; sorting the comprehensive weight and the influence weight to form a risk accident element graph model; extracting historical navigation accident data according to element types in the risk accident element graph model; establishing a graph form sample database; a multi-layer network classification model is adopted to carry out classification training on the navigation map form samples; and matching ship risk accident features to be analyzed to carry out risk management and control. The method is used for completing risk accident feature classification and identification of different types of ships in the sailing process by analyzing various element features in the historical data of the ship sailing risk accidents, performing classification prediction on the to-be-analyzed ship sailing risk accidents, and determining the sailing accident features so as to take targeted accident prevention measures.

The invention relates to the technical field of ship and ocean engineering, in particular to a global-local dependency cooperative expression ship rolling motion extremely-short-term forecasting method which comprises the steps that an Informer branch and BiGRU-GSA branch parallel architecture is constructed, an encoder of the Informer branch screens key query key pairs through a ProbSparse self-attention mechanism, and the sequence length is compressed through a self-attention distillation mechanism; the decoder adopts generative reasoning and single forward propagation to generate a complete prediction sequence; the BiGRU-GSA branch utilizes a multi-layer BiGRU network to simultaneously capture forward and backward time sequence dependence through a bidirectional gating unit, and extracts time domain fine-grained features; the GSA network constructs local representation of time sequence preference by using two-stage gating linear attention through a gating slot attention model; and the global features of the Informer branch and the local features of the BiGRU-GSA branch are spliced, feature fusion is realized through a full connection layer, a prediction result is generated, and collaborative expression of global-local dependence is realized. According to the invention, high-precision prediction can be carried out on the future motion state of the ship in an extremely short time.

The invention discloses a fuel cell fault diagnosis method based on fusion of a physical model and a neural network, and belongs to the technical field of fuel cell system monitoring and intelligent diagnosis. According to the method, in order to solve the problems that a proton exchange membrane fuel cell stack is complex in operation state, the signal noise of a sensor is large, and a traditional model is difficult to reflect aging and faults in real time, the prior knowledge of a physical model is combined with the time sequence learning ability of a long-short-term memory (LSTM) neural network. The method specifically comprises the following steps: synchronously inputting an actuator or a control signal into a real PEMFC pile and a physical model, and dynamically correcting model parameters by utilizing online parameter identification; performing anomaly detection, filtering and smoothing on a sensor signal, and aggregating with observable and unobservable process variable estimators output by the physical model to form an enhanced feature vector; a multi-scale sliding time window is adopted to construct a multivariable time sequence, the multivariable time sequence is input into a multilayer LSTM network after normalization, and network weights and adjustable parameters of a physical model are updated at the same time through a joint optimization strategy. According to the method, multiple typical faults such as flooding, drying, air depletion and hydrogen depletion can be diagnosed in real time in a classified mode under the dynamic working condition, health indexes such as the performance degradation rate and the remaining life can be output, online intelligent diagnosis and life prediction of the PEMFC pile are achieved, and the method has the advantages of being high in precision, high in robustness, capable of being deployed in an embedded mode and the like.

The invention discloses a social media key user identification system based on information non-uniform propagation characteristics, and relates to the technical field of key user identification, and the system comprises a dynamic module which is used for calling an information propagation model through a weighted directed graph of N time windows, simulating information propagation, obtaining initial influence scores of a user in the N time windows, and obtaining initial influence scores of the user in the N time windows; and a cross-layer module which constructs a multi-layer network, quantifies the influence of the user in different layers in the multi-layer network based on the dynamic influence score, obtains a cross-layer influence score, and performs weighted average on the initial influence score of the user in N time windows to obtain a dynamic influence score. The recognition module is used for calculating comprehensive scores according to the initial influence scores, the dynamic influence scores and the cross-layer influence scores of the users, and the comprehensive scores are arranged in a descending order to recognize key users; through the key user identification method, the social platform can automatically identify key users really having transmission force and guiding force.

The invention relates to the technical field of seawater bromine extraction production, and discloses a seawater bromine extraction production line digital management method and system, and the method comprises the steps: collecting and integrating meteorological ocean data, raw material seawater real-time components, a process sensor network and equipment state data in real time, and forming a four-dimensional data set; constructing the four-dimensional data set into a graph structure, excavating a nonlinear coupling relationship among seawater characteristics, process parameters and equipment efficiency based on the graph structure, establishing a coupling analysis model, and pre-judging a fluctuation condition through the coupling analysis model; the condensation temperature and the tail gas bromine concentration are monitored in real time, and a multi-layer LSTM network model is adopted to predict the optimal value of the steam consumption; constructing a digital twinborn model of the production line, and simulating and predicting the production process in the digital twinborn body by using a model prediction control algorithm to obtain a predicted production result; when the deviation between the predicted production result and the actual production data exceeds a set threshold value, a DQN algorithm is adopted for intelligent regulation and control; according to the invention, the efficiency and precision of production management are improved.

The present disclosure relates to the field of artificial intelligence. The present disclosure provides a neural network updating method, a classification method and an electronic device. In the neural network updating method, the neural network comprises a plurality of network layers, at least one of the plurality of network layers comprises a plurality of neurons, and the method comprises: obtaining input data of the neural network; and determining an activation state of at least one neuron in the neural network based on the input data.

The invention discloses a multi-layer network switch based on intelligent routing, and the structure of the multi-layer network switch comprises a slot, a protection device, a heat dissipation net, an indication lamp, a network switch, and a wire plugging port. The adsorption base is stably arranged at the middle position of the lower end in the placement groove under the action of the powerful sucking disc, so that the top of the steering assembly can be connected with the network switch, and meanwhile, the butt joint pressing plate is pressed at the upper end of the network switch in parallel; the attaching piece arranged at the lower end of the butt-joint pressing plate can assist the butt-joint pressing plate to be connected to the network switch in parallel along with the butt-joint pressing plate, the steering assembly is supported by the adsorption base to adjust the angle of the network switch to a corresponding position, and therefore a worker can rapidly and stably insert a corresponding network cable into a corresponding insertion groove. The working intensity of workers is effectively reduced, and the operation convenience of the network switch is improved.

The invention discloses a method for enhancing robustness of a multilayer network based on representation learning, which comprises the following steps of: constructing initial features, and comprehensively extracting local information and global information of the multilayer network by capturing local structure features and node pair relation features so as to provide high-quality input for subsequent steps. Uniform node representation is generated through intra-layer feature learning, inter-layer feature mapping and an inter-layer attention mechanism, and the modeling capability of a complex coupling relation is enhanced. And calculating a node selection probability by using a multi-head attention mechanism, selecting nodes and adding connecting edges according to the node selection probability, completing an action decision, and updating a network state.