105 results about "Multi layer network" patented technology

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 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 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 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 passive underwater maneuvering target state estimation method based on a long short-term memory network, a program, equipment and a storage medium, and belongs to the technical field of underwater acoustics. The method comprises the following steps: simulating a maneuvering track of a target by adopting a coordinated turning state space model to generate a motion state; performing normalization and time step sampling on the passive acoustic measurement data acquired by the multiple observers, and constructing a time sequence input sequence; inputting the time sequence data into a multi-layer LSTM network to capture a time dependency relationship and nonlinear characteristics of target motion; and training the network through a time back propagation algorithm, and minimizing a mean square error between a state estimation value and a true value. According to the method, verification is carried out in a complex maneuvering scene, and comparison with an interactive multi-model extended Kalman filter and an interactive multi-model unscented Kalman filter is carried out. Results show that the time sequence learning capability of the tracking system can be improved, and the robustness and accuracy of state estimation are enhanced in noise interference and dynamic change environments.

The application belongs to the technical field of natural language processing, and particularly relates to an aspect-level sentiment classification method based on a graph attention network, which comprises the following steps: obtaining word embedding representation of context text in which an aspect word is located; dynamically adjusting the weight of a context word according to the relative position of the context word and the aspect word, and obtaining context semantic features; aggregating syntactic information through an improved graph attention network to obtain syntactic features of the text; using a deep cross network to fuse the syntactic features of the text and the context semantic features to obtain final feature representation; and performing sentiment prediction on the final feature representation through a full connection layer to obtain the sentiment polarity distribution of the aspect word in the text. The application solves the problem of feature information loss that may occur in a multilayer network of the graph attention network, and considers the position information of the context word when extracting semantic features, so that the syntactic features and the context semantic features are fully fused, thereby improving the accuracy of aspect-level sentiment classification.

The application relates to the technical field of public service network, in particular to a public service radius calculation method of adaptive network evolution, which comprises the following steps: dividing the public service into multiple functional units according to the service content provided by the public service, wherein 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 adaptive evolution of capacity, connection and modular growth; setting a dynamic adjustment strategy to realize resource elastic scheduling of the public service and optimization of a service coverage range; obtaining the service coverage range, obtaining the service radius of each service point in the service coverage range based on a multilayer network model, combining the number of demand points covered by each service point and resource consumption under each service radius to form an optimal service radius vector set; and determining the optimal coverage radius and the optimal service radius of each service point.

Methods, systems, and apparatuses are provided for managing and routing data in a multi-tier network architecture. The methods include transmitting a data flow comprising a sequence of data packets to a source leaf switch and determining if an intended route exists in a forwarding table stored at the source leaf switch using routing metadata. If the intended route does not exist, a control packet is transmitted to a centralized coordinator switch, which modifies an allocation table to include an updated route. The forwarding table at the source leaf switch is then updated with the updated route, and the data flow is transmitted to its destination.

The invention provides an abnormal transaction monitoring method which can be applied to the technical field of artificial intelligence, the field of information security and the field of financial science and technology. The abnormal transaction monitoring method comprises the following steps: constructing a first multi-layer network based on multi-source transaction data obtained in real time; coupling the first multi-layer network to extract a first aggregated topological adjacency matrix; wherein the first aggregation topology adjacency matrix comprises aggregation transaction characteristics of each layer of network in the first multi-layer network; and monitoring abnormal transactions in the multi-source transaction data through a pre-trained graph network model based on the first aggregation topology adjacency matrix. The invention further provides an abnormal transaction monitoring device and equipment, a storage medium and a program product.

The invention provides a video stream lag intelligent optimization method and device based on AI prediction. The method focuses on the following core links: a data acquisition and feature engineering method covering multilayer network state monitoring, video content deep analysis, user behavior sequence modeling and a real-time data preprocessing algorithm; the AI prediction engine core algorithm comprises an improved Transform architecture, a cross-modal feature fusion mechanism and a multi-scale prediction model; the intelligent optimization decision-making mechanism relates to a reinforcement learning optimization framework, a self-adaptive buffer strategy and an intelligent code rate switching and predictive preloading method; the system architecture and engineering implementation comprises an end-to-end real-time processing architecture, a lightweight model deployment technology, a multi-platform compatible interface and a real-time feedback and tuning mechanism.

The invention belongs to the technical field of cell data processing, and relates to a single cell gene characterization system and method based on deep learning. The system comprises a gene information input module, a data processing module, a context sensing interpolation module, a gene feature fusion module, a deep representation learning and joint optimization module and a cell typing and differentiation track inference module. The input module integrates a data source; the processing module preprocesses and enhances the original expression matrix; the interpolation module performs weighted interpolation by using a conditional Gaussian graph model; the gene feature fusion module generates a gene module activity matrix through 1 * 1 convolution and the like; the fusion module fuses the sample meta-information with the active matrix, the sequencing depth and the gene feature vector; the depth representation module captures high-dimensional dependence through a multi-layer Transform network, and generates cell high-order potential representation; the typing module outputs a cell population division and differentiation trajectory based on the deduced pseudo-time trajectory. The method has the beneficial effects that cell expression characteristics are recovered under high sparseness and noise, and a new way is provided for cell type identification and the like.

The application discloses a respiratory infectious disease population flow network construction and feature mining method and device based on multi-source heterogeneous traffic big data. The method collects multi-source traffic travel data such as railways, civil aviation, highways and mobile signaling and pre-processes, constructs a time-space correlation database, and generates multiple standard data tables through cleaning, desensitization, time-space alignment standardization processing. Based on the standardized data, the network topology parameters are defined, the multi-traffic mode OD flow matrix is generated, and the multiple verification and calibration are completed, the civil aviation, railway and highway hierarchical networks are respectively built, the network edge weight is fused through node alignment and interlayer coupling, the super-adjacency matrix is constructed, the multi-dimensional topological features of the multi-layer network are mined, and finally the exclusive feature data set suitable for respiratory infectious disease cross-regional transmission prediction is generated. The scheme can efficiently fuse standardized multi-source heterogeneous data, accurately construct the population flow network, and effectively improve the accuracy and timeliness of the infectious disease transmission risk assessment.

An automatic detection method for CBCT cephalometric landmarks based on multi-geometric guidance and specific perceptual coding includes the following steps: Step S1, downsampling the 3D CBCT image and obtaining a preliminary coordinate set through a coarse localization network; Step S2, cropping image blocks centered on the coordinates and extracting local features through a visual encoder containing a shared basic encoder and a low-rank adapter; Step S3, calculating the relative position matrix of the landmarks, encoding spatial relationships using radial basis functions, and constructing a multi-anatomical heterogeneous map; Step S4, inputting visual and edge features into a multi-geometric guidance Transformer, fusing global constraints and updating features using an attention mechanism; Step S5, extracting directional geometric relationships using spherical harmonic functions to construct higher-order update terms, and dynamically updating the heterogeneous map using a gated residual mechanism; Step S6, predicting coordinate offsets through a multi-layer network and performing iterative optimization to output high-precision 3D coordinates. This method significantly improves detection accuracy and robustness.

Techniques are described for optimizing multipaths of a segment routing-enabled network. For example, a computing device is configured to: for each link in a network layer of a multi-layer network, compute a usage (metric) of the link by all paths of a first plurality of multipaths provisioned in the network layer to compute a total usage by the first plurality of multipaths, the first plurality of multipaths having been computed and placed to a model of the network layer in a first order; compute a second plurality of multipaths, wherein the second plurality of multipaths are computed and placed, to the model of the network layer, in a second, different order; and in response to determining that the total usage by the second plurality of multipaths is less than the total usage by the first plurality of multipaths, provision the second plurality of multipaths in the network layer.

The invention discloses a methanol-to-olefin production safety anomaly detection method based on an LSTM algorithm. The method comprises the following steps: acquiring methanol-to-olefin real-time process parameters and control instruction data; preprocessing the process parameters and the control instruction data to obtain a training set and a test set; constructing a reconstruction model based on the multilayer LSTM network, and carrying out training learning on the reconstruction model by adopting the training set to obtain a joint normal model; inputting the test set into the joint normal model, and calculating and outputting a reconstruction error of the test set and the test set in real time; and establishing a multi-stage abnormal response mechanism. According to the method, by analyzing the joint change mode of the multi-dimensional time sequence data, the complex abnormal condition can be accurately identified, the high-efficiency detection of the potential safety hazard in the olefin production process is realized, and the defect that the traditional method is difficult to fuse the multi-dimensional information and accurately identify the complex abnormal condition is overcome.

A user equipment (UE) includes a transceiver and a processor. The processor is configured to receive, from a network and via the transceiver, a configuration for a multiple layer physical uplink shared channel (PUSCH) transmission using more than one codeword. The processor is also configured to transmit, via the transceiver and in accordance with the configuration for the multiple layer PUSCH transmission that uses more than one codeword, the multiple layer PUSCH transmission. The multiple layer PUSCH transmission is transmitted on at least a first layer of the multiple layers using a first codeword of the more than one codeword, and on at least a second layer of the multiple layers using a second codeword of the more than one codeword.

Techniques for providing a machine learning (ML)-based framework for detecting and troubleshooting network-related issues in large storage fabrics. The techniques include detecting, based on an output of an ML model, a network-related issue in a distributed storage infrastructure. The ML model operates on telemetry data obtained from network elements, and computing / storage nodes on a storage network. A multilayer representation of the storage network includes a physical layer, a logical layer, and a service layer. The techniques include obtaining a correlation between the network-related issue and an activity, service, or status of the network elements / nodes in two or more layers of the multilayer representation. The correlation identifies a context of the network-related issue with respect to the network elements / nodes in the two or more layers. The techniques include providing an in-context alert pertaining to the network-related issue to at least one administrator of the network elements / nodes within the storage network.

Disclosed in the present application are a multi-layer network communication system and method, an operation control apparatus and a communication device. A multi-layer network constructed by the multi-layer network communication system comprises an access layer and a service layer, wherein the access layer comprises several entity cells, and the service layer comprises a flexible cell; the multi-layer network communication system comprises an entity cell processor, a flexible cell manager and a flexible cell processor; and the flexible cell and the entity cell share an infrastructure and a spectrum, and use the same air interface protocol.

The application discloses a kind of based on multi-source remote sensing wild tea tree dynamic monitoring and distribution prediction method, comprising: based on local self-similarity algorithm to optical satellite image and synthetic aperture radar image are registered in space, and the optical feature and synthetic aperture radar feature after registration are spliced into unified remote sensing feature;Based on spatial attention mechanism, spectral attention mechanism and cross-modal attention mechanism, each feature is weighted and fused to generate multi-source fusion feature;Extract initial feature set and carry out correlation and collinearity screening, obtain key feature set and construct time series feature data;Multi-layer Deep-LSTM network of integrated adversarial domain self-adaptive module is constructed, and the optimal prediction model is obtained after training is completed;Based on the optimal prediction model, the distribution density variation trend of wild tea tree in future period is obtained by predicting current time series feature data.The application can improve the accuracy and efficiency of wild tea tree dynamic monitoring and distribution prediction.

The present disclosure discloses a method for SFC deployment based on VNF-dependent software migration in a multi-domain network, and relates to the field of communications technologies. The method jointly optimizes the SFC deployment and the migration of VNF-dependent software to resolve issues concerning service provision in the multi-domain network. According to the method, information related to the multi-domain network is first initialized, and then a multi-layer network architecture is constructed to support SFC deployment. Then, a multi-layer weighted network is established by using an analytic hierarchy process (AHP), and an SFC deployment policy and a VNF-dependent software migration policy are executed on this basis. In the present disclosure, the impact between SFC deployment and VNF-dependent software migration decisions is comprehensively considered, achieving efficient SFC deployment and VNF-dependent software migration in the multi-domain network.

The application relates to an ISAR sparse imaging method, device and equipment based on a DA-ISTA network. An iterative threshold convergence algorithm (ISTA) is used to construct a deep neural network for ISAR sparse imaging. In the ISTA, the iteration process is tiled into a multi-layer network structure, and the linear sparse transformation in the ISTA algorithm is replaced by a nonlinear convolution operation in each layer of the network structure. In the DA-ISTA network, a large number of iterations are not required as in the ISTA algorithm. Only a small number of network structure layers are required to process ISAR sparse aperture echo data, and a high-quality ISAR imaging result can be obtained. The method improves the imaging quality and the calculation efficiency, and makes the DA-ISTA network interpretable.

This invention provides a multi-layer network adaptive routing method and system based on stability constraints, relating to the field of adaptive routing technology. The method includes dividing multiple wireless networks into communication areas to obtain a multi-layer topology representation; for multiple communication node pairs in the multi-layer topology representation, statistically analyzing historical transmission behavior during data forwarding to obtain link historical state vectors and path end-to-end historical state vectors; calculating link comprehensive evaluation indicators and path end-to-end success rates based on the link historical state vectors and path end-to-end historical state vectors; using the link comprehensive evaluation indicators to screen communication areas for stability, constructing a candidate area-level path set; and performing adaptive routing selection for multi-armed gambling machines based on the candidate area-level path set and path end-to-end success rates to obtain an adaptive routing path for each communication node pair. This invention solves the problem of routing oscillation caused by frequent switching of transmission paths in existing methods.

The invention discloses a traditional village fire scene cascade failure road vulnerability assessment method, and relates to the technical field of building group fire prevention and control and personnel evacuation. The method comprises the following steps: firstly, acquiring space vector data of traditional village building groups, single buildings and roads, and constructing a building group fire spreading network and a road network; then, constructing a road vulnerability evaluation system, introducing an accessibility index to dynamically represent the road traffic efficiency, and defining the vulnerability index to quantify the influence of the disaster on the road system; finally, a multi-layer network cascade failure simulation framework is provided, the framework can quantify the dynamic cascade failure process, and a multi-scene vulnerability thermodynamic diagram is generated to locate a high-risk area. According to the method, the influence of the traditional village building fire on the road network is fully considered, the fire-road double-layer coupling network model is established, dynamic quantification is realized, a scientific decision-making tool is provided for the traditional village building fire and the road evacuation risk, and the method has important application value for improving the toughness of the traditional village.

This invention discloses an image classification method based on spectral norm regularization using a multi-layer spiking Hopfield network, comprising the following steps: (1) replacing the neuron units in the multi-layer Hopfield network with spiking neurons to obtain a multi-layer spiking Hopfield network; (2) replacing the neuron state vector in the local gradient calculation formula of the multi-layer spiking Hopfield network with the firing frequency of the spiking neurons; (3) obtaining an image training set, and adding a Jacobian matrix spectral norm regularization term constraint during the training process of the multi-layer spiking Hopfield network; (4) inputting the image to be classified into the trained multi-layer spiking Hopfield network, performing image spiking processing on the first layer of the network, and classifying the image by counting the spiking of neurons in the last layer of the network. Using this invention, the image classification accuracy of the multi-layer spiking Hopfield network can be improved.

The application discloses a kind of based on multilayer network cascading failure multimodal transport network resilience evaluation method, belong to multimodal transport network operation and resilience evaluation technical field.First, according to multimodal transport network multilayer structure and freight flow transfer characteristics, ML-space method is used to construct highway and water weighted undirected multilayer network model;On this basis, fusion network topology and freight attribute double perspective, calculate node multilayer centrality and freight level to determine node importance, combined with the heterogeneity of subnetwork to build load-capacity cascading failure model containing initial load, node capacity and differential load redistribution strategy;Then based on the resilience triangle theory, build the whole stage resilience evaluation system covering resistance, absorption capacity and recovery capacity, through MATLAB software numerical simulation, compare the network resilience under different cascading failure and recovery strategy, reveal the influence law of node capacity coefficient on resilience.The application starts from the actual operation scene of multimodal transport network, considers multilayer characteristics and cascading failure propagation law, proposes the precise evaluation method of multimodal transport network resilience under cascading failure scenario, provides scientific reference for the planning construction, risk prevention and control and resilience improvement of multimodal transport network.

The invention provides a conference reporter follow-up action prediction method and system based on LSTM, and relates to the technical field of artificial intelligence decision making, and the method comprises the steps: inputting a multi-dimensional feature vector into a multi-layer LSTM network model, calculating an initial action prediction output through forward propagation, training the multi-layer LSTM network model by adopting a back propagation and stochastic gradient descent algorithm based on a mean square error loss function so as to obtain an optimized action prediction model; and performing preprocessing and feature fusion on reporter data collected in real time, and inputting the reporter data into the optimized action prediction model to obtain an action category. The conference organization efficiency is improved.

The application discloses a task scheduling method and device based on a distributed large language model, terminal equipment and a storage medium. The method comprises the following steps: obtaining communication data of each device node in a multi-layer network of the distributed large language model, a to-be-scheduled task, a task feature of the to-be-scheduled task, and real-time monitoring data of each device node; then, according to the communication data, the to-be-scheduled task, the task feature of the to-be-scheduled task, and the real-time monitoring data of each device node, performing model segmentation and task scheduling on the multi-layer network with the minimum total delay of the multi-layer network as the target, obtaining a target device node corresponding to each to-be-scheduled task, and distributing each to-be-scheduled task to the corresponding target device node for scheduling. Through implementation of the application, resource management of the multi-layer network in the large language model can be realized, the performance of the network is improved, and the delay is reduced.

In one embodiment, a computer-implemented method comprises using one or more computing devices that are communicatively coupled to one or more internetworking devices in two or more logical layers of a multiple-layer distributed computing environment, the logical layers comprising at least an operational technology (OT) network and an information technology (IT) network, configuring one or more network firewalls in one or more of the logical layers to admit UDP protocol traffic on one or more specified ports; using the one or more computing devices, setting a current logical network layer to a lowest logical layer of the multiple-layer distributed computing environment; using the one or more computing devices, opening a secure tunnel using the internetworking stack of a first authentication service node at the current logical network layer; using the one or more computing devices, initiating secure tunnel communication toward a second authentication service node at a next higher logical layer of the computing environment; using the one or more computing devices, setting the current logical network layer to be the next higher logical layer, and repeating the opening and initiating one or more times between the current logical layer and the next successive higher logical layer; using the one or more computing devices, synchronizing user access policies between the first authentication service node and the second authentication service node using state synchronization messages communicated through the secure tunnel.

This application discloses a speech clustering method, apparatus, storage medium, and electronic device, relating to the field of smart home technology. The speech clustering method includes: determining an encoded sequence of speech features of an acquired target speech; determining a label vector of the encoded sequence, wherein the label vector is used to represent the continuity of the target speech; inputting the label vector and the encoded sequence into a first neural network model to obtain a high-dimensional feature vector output by the first neural network model, wherein the high-dimensional feature vector is used to represent the category to which the object uttering the target speech belongs, and the first neural network model includes a multi-layer network encoder; inputting the high-dimensional feature vector and the label of the high-dimensional feature vector into a second neural network model to obtain a target probability value output by the second neural network model, wherein the target probability value is used to represent the probability that the target speech belongs to the same category as other speech, and the second neural network model includes a multi-layer network encoder, and the other speech is speech that has already undergone speech category clustering.

Provided in the present application are data transmission methods and apparatuses, and an electronic device, a storage medium and a program product. A method is applied to a multi-layer network architecture, wherein the multi-layer network architecture comprises an access layer and an aggregation layer, the access layer comprises a plurality of access switches, each access switch is connected to at least one first computing device, and each first computing device is provided with at least one network interface card. The method comprises: for any network interface card in a first computing device, acquiring a routing path for transmitting data to the network interface card; and sending the routing path to a second access switch in an access layer, wherein the second access switch is connected to at least one second computing device, and the second access switch is used for forwarding to the network interface card, on the basis of the routing path, data of a network interface card corresponding to a destination address, which data is sent by the second computing device.