222 results about "Network mapping" patented technology

A network mapping behavior anomaly detection method and system based on machine learning is provided. The method includes: collecting dual-source traffic data, generating a structured log data set through dual-source log fusion engine; performing subgraph matching calculation to obtain a mapping behavior deviation degree; generating communication data containing a watermark identifier in a session corresponding communication path; verifying whether attack events carry the watermark identifier; generating a network mapping behavior anomaly detection report. According to the disclosure, an adaptive attack behavior model is constructed through a multi-modal feature vector based on structured logs and a graph protocol mapping rule base, so that the cognitive robustness to protocol camouflage and path drift is fundamentally enhanced, a real-time verification chain of detection results is built, and traditional passive detection is transformed into self-proof active defense through cross verification of watermark carrying state and behavior trajectory.

The invention discloses a water quality prediction method based on graph neural network and spatial-temporal feature fusion, and the method comprises the steps: obtaining hydrological data and water quality data of a plurality of monitoring stations in a preset drainage basin in a preset time period, representing the monitoring stations as nodes of a graph, and representing the adjacency relation between the monitoring stations as edges of the graph, a graph topology network mapping multiple monitoring station pollutant data is constructed, a spatial-temporal feature fusion module is adopted to perform feature extraction and fusion on the graph topology network to obtain water pollution spatial-temporal feature data, and the spatial-temporal feature fusion module is obtained by training a graph convolutional network, a gating circulation unit and a self-attention mechanism. And performing fusion processing on the extracted feature data to form a time sequence data set of a graph topology structure, and finally predicting the data set through a graph time sequence prediction model to obtain water quality prediction results of the plurality of monitoring stations in a target time period. The method can effectively deal with the spatial and temporal change process of water pollution, reduce the water quality prediction cost and improve the water quality prediction precision.

The invention relates to the technical field of wind power generation, in particular to a long flexible blade monitoring method and system based on multi-source data fusion, which adopts a multi-source data fusion technology, collects optical fiber strain, vibration, environment and image data by arranging sensor nodes on a fan blade, and generates comprehensive feature data through preprocessing; and extracting potential feature vectors by a multi-modal auto-encoder, and mapping the potential feature vectors into predicted stress data by using a physical information neural network in combination with offline finite element simulation data and physical constraint conditions. Then, a control decision is generated through the multi-modal deep fusion network and hierarchical reinforcement learning, the blade state is adjusted in real time, and the model is optimized through a closed-loop feedback mechanism; according to the invention, precise monitoring and active regulation and control of special working conditions such as blade pollution and icing are realized.

The invention relates to the technical field of meteorological data processing and geographic information, in particular to a near-surface meteorological field downscaling method based on a topographic constraint Transform model, which comprises the following steps of: extracting multi-modal fusion features from reanalysis meteorological data and digital elevation model data; mapping to a target resolution by means of a topographic constraint Transform network to obtain initial downscaling meteorological data; latent variable modeling is carried out on the initial data through a variational auto-encoder to describe multi-scale uncertainty, physical constraints such as energy balance and mass conservation are incorporated in combination with an iterative updating mechanism based on meta-learning, and core downscaling meteorological data are generated; according to the invention, detail capture and dynamic adaptation of local meteorology are realized through self-supervised pre-training and online updating.

According to the multi-modal automatic knowledge graph construction method based on the large language model, a multi-modal data stream is preprocessed, features are extracted, and the multi-modal data stream is mapped to a unified semantic space through a cross-modal alignment network after being processed through the large language model, a visual converter and a time sequence neural network. In the space, entities and categories are recognized based on a large language model, a triple is generated by combining a multi-modal feature judgment entity relationship, mapping fusion is performed through an ontology alignment algorithm driven by a graph neural network and a predefined domain ontology, finally knowledge is stored in a graph database, and dynamic updating is performed by means of incremental learning and online reasoning. Standardized APIs and visualization components are provided. According to the method, the construction efficiency and the automation degree of the knowledge graph are remarkably improved, the cross-modal information fusion and knowledge maintenance capability is enhanced, and the application requirements of intelligent retrieval, recommendation, decision support and the like are met.

The invention relates to the technical field of deep learning, in particular to an AI chip test parameter adaptive optimization method based on deep learning, which comprises the following steps: acquiring historical test data of an AI chip, and calculating correlation strength among different failure modes based on the historical test data; identifying a failure coupling matrix according to the edge weight, and converting a preset static detection parameter constraint boundary into a dynamic constraint space changing along with a failure detection state; a multi-level optimization framework is constructed, the upper layer executes failure type correlation analysis and generates constraint propagation information, the middle layer optimizes a parameter cluster based on the constraint propagation information, and the lower layer adjusts a single detection parameter and outputs a parameter optimization result; establishing a neural network mapping model, and obtaining a nonlinear mapping relationship between the detection parameters and the failure types; based on the physical state parameters, the nonlinear mapping relation is adjusted, the dynamic constraint space is updated, parameter optimization is executed again, a parameter optimization result is output, and an optimal test parameter combination is output.

A method for screening inter-PLMN messages at a SEPP to implement roaming agreements includes storing an originating and target network mapping database containing records including mappings between originating network identifiers and target network identifiers. The method further includes receiving, at the SEPP, an inter-PLMN SBI request message originating from an NF in a network served by the SEPP. The method further includes determining originating and target network identifiers from the message, locating a matching record in the database, and determining, from the record, whether the message should be allowed to flow from the originating network to the target network. The method further includes forwarding the message to the target network or preventing the forwarding of the message to the target network based on results of the determining.

Systems and methods for real-time mapping and visualization generation of system components and inter system communications as well as for the generation of real-time recommendations for architectural recommendations. The systems and methods generate hierarchical workflow mappings of a computational network using event data from software applications lineage logs as well as component and artifact repositories. For example, while event data in software applications lineage logs is conventionally limited to identifying that a given process occurred, the systems and methods use the plurality of events detailed in the software applications lineage logs to create, using an artificial intelligence model, a network mapping of how system components are arranged and interact.

The invention belongs to the technical field of database management, and particularly relates to a knowledge base intelligent management system and method based on a large model, and the system collects question and answer texts and index information of different target subjects through a data collection module, and generates a first text hierarchical sequence and a directed index graph through word segmentation preprocessing and directed graph neural network mapping; the feature extraction module extracts text and index embedding representation in combination with a feature distillation model, and obtains similar overlapping degrees of different target main bodies through a three-dimensional overlapping degree model; the conflict simulation module constructs an initial knowledge base based on a tree database, detects index conflicts in target subjects and among the subjects by using a Bayesian simulation algorithm, and generates a conflict risk list; the conflict resolution module outputs a resolution strategy in combination with a conflict resolution confrontation model, and the feedback adjustment module iteratively optimizes the knowledge base until a conflict threshold and a user demand are met; according to the method, intelligent management of cross-subject question and answer indexes is realized, and knowledge base conflict resolution efficiency and cross-subject query accuracy are improved.

The invention discloses an initial orbit determination method for passive angle measurement of a ring fire orbit for Mars sampling return, and the method comprises the steps: building an absolute dynamic model, obtaining an absolute motion state of a spacecraft, and deducing a relative motion state X; constructing a data set by using the measurement quantities H and X of the sensor; a network architecture is designed, input data I are position and speed coordinates of the tracker in a Mars equator inertial coordinate system and angle information converted from H, and output data are X; and finally, adjusting an observation interval dt and times k according to a ring fire observation condition, training a physical information neural network by using limited observation data, defining a total loss function F comprising a loss function FY and a physical law penalty term FD, and optimizing a weight coefficient and bias by minimizing the loss function through an F gradient descent method. According to the method, the neural network is trained offline, a network mapping model from a target line-of-sight angle measurement sequence to a target orbit is constructed, and the network mapping model is solidified on a returner for online use, so that sampler short-arc passive measurement rapid orbit determination is realized.

The invention relates to the technical field of GNSS (Global Navigation Satellite System) deception detection, and provides a multi-modal GNSS deception detection method based on dynamic weighting, which comprises the following steps: acquiring a one-dimensional SQM time sequence and a two-dimensional correlation function and carrying out preprocessing; performing feature extraction on the standardized two-dimensional correlation function through a Transform channel to obtain a global feature; performing feature extraction on the standardized two-dimensional correlation function through a CNN channel to obtain local features; obtaining a Transform channel dynamic weight matrix and a CNN (Convolutional Neural Network) channel dynamic weight matrix through the one-dimensional SQM time sequence; multiplying and adding the global features and the local features by corresponding weights to obtain fusion features; global average pooling is carried out on the fusion features, and the fusion features are mapped into deception probabilities through a full-connection network; and detecting spoofing according to the spoofing probability and the probability threshold. According to the method, the detection accuracy in a complex environment is remarkably improved, and the safety and reliability of the system are enhanced.

The invention provides a classroom simulation method for virtual reality, and relates to the technical field of data processing, and the method comprises the steps: obtaining a high-dimensional data matrix with a time-space correlation characteristic; constructing an electrical connection relation graph; obtaining a quantum state evolution result of the transient process of the power system; calculating to obtain a multi-physical field distribution parameter corresponding to the equipment; based on the multi-physics field distribution parameters, combining molecular dynamics simulation and a fluid mechanics model to obtain arc dynamic characteristic data; collecting an electromyographic signal of an operator, and generating a force feedback control instruction through a neural network mapping model in combination with mechanical parameters of equipment; and according to the eye movement tracking data, the arc dynamic characteristic data and the force feedback control instruction, generating a virtual reality scene adaptive to the visual response and the operation state of the operator by using a dynamic rendering engine. According to the embodiment of the invention, the authenticity and interactivity of virtual reality classroom simulation can be improved.

The invention discloses a numerical simulation method for estimating equivalent seepage of a three-dimensional fracture network based on topological properties, which comprises the following steps: firstly, constructing a small three-dimensional fracture network sample set on the basis of related data of a fracture network in a research area, then mapping the sample fracture network into an equivalent two-dimensional undirected graph by using a graph theory, solving equivalent permeability, and calculating the equivalent seepage of the fracture network. Forming a training sample set; then, a maximum information index method is adopted to mine fracture network equivalent permeability main control factors, and the control relation between fracture network parameters and an underground flow field is determined; and finally, constructing a substitution model between the main control factors and the equivalent permeability based on a BPNN machine learning model, and realizing conversion from the target discrete fracture network to an equivalent medium. According to the method, a simulation scheme which reflects topological connectivity and is high in prediction precision and low in calculation cost is provided for numerical simulation of the fractured stratum underground flow field under the field scale.

The invention relates to a multi-ship route dynamic optimization method and device. The method comprises the following steps: firstly, constructing a water transportation network based on original ship automatic identification system data; then, on the basis of mapping nodes, arcs, passing points and route weights of the water transportation network, a mixed integer programming model is constructed, and an objective function and constraint conditions associated with dynamic optimization of routes of multiple ships are defined; and finally, solving based on the objective function and the constraint condition by adopting an improved adaptive large-field search algorithm to obtain a fleet path plan. And proposing a fleet route optimization mathematical model and a heuristic algorithm capable of processing a large-scale fleet. According to the model, various factors such as navigation characteristics, transportation requirements and time windows of ships are fully considered, and it is ensured that route planning not only meets economic benefits, but also can meet the requirements of safety, high efficiency and the like. Interference information in AIS data is deeply mined, the influence degree of interference on route planning is analyzed, and effective prevention and response measures are provided.

The invention relates to the technical field of digital information transmission, in particular to a protocol stack fingerprint-based host asset surveying and mapping method, which comprises the following steps of: designing a host discovery algorithm of a combined multi-protocol data packet by referring to an open system interconnection seven-layer network model layering protocol; performing feature analysis and data preprocessing based on a protocol stack fingerprint rule base of a network mapper transmission control protocol, and constructing a protocol stack fingerprint data set of a host asset operating system; the host asset operating system fingerprint identification classification model is constructed based on the random forest algorithm, the coverage rate of survival host discovery is remarkably improved, and the limitation of a traditional rule-based matching method is effectively overcome. According to the method, the accuracy rate in the fingerprint identification task of the coarse-grained operating system reaches 97%, the weighted F1 value in the fingerprint identification task of the fine-grained operating system reaches 0.87, the technical level of host asset surveying and mapping is improved, and a more comprehensive and accurate technical path is provided for network asset security assessment.

The invention relates to a progress tracking and early warning method and system for clinical test items. The method comprises the steps that subject recruitment progress data, follow-up visit completion data, test process data and project milestone completion data of a clinical test center are collected and subjected to quality scoring, and a data quality scoring matrix and a data node connection state are obtained; performing network mapping to obtain a project progress dependency graph; extracting spatial features and time sequence features to obtain risk feature vectors; performing double-target dynamic assessment to obtain a risk assessment report; dynamically adjusting the early warning index to obtain an early warning rule configuration file; and performing early warning trigger judgment, executing hierarchical pushing processing, and generating an early warning information pushing record. According to the implementation of the invention, the system resource consumption is reduced, and the processing capability of large-scale multi-center clinical test items is improved.

The invention provides an individualized TMS target positioning method and system for spastic cerebral palsy children, and aims to realize TMS precise nerve regulation and control for different types of SCP children. According to the method, the structural features of the healthy child are obtained, and after image processing, accurate division of the SCP child brain region is achieved, the defects of a current brain injury child brain region spectrogram are overcome, and a basis is provided for seed point selection of different SCP symptoms; multi-modal image parameter features are further scanned, whole-brain voxel correlation analysis is performed based on seed points to construct functional connection diagrams, the crossed and overlapped functional connection diagrams are searched to serve as a liability focus network of different symptoms, a voxel set close to the scalp surface serves as a TMS target, and SCP child treatment targets of different clinical symptoms are accurately positioned. Based on a focus network mapping technology, functional connection characteristics related to different symptoms or symptom complexes are better positioned, a specific brain network is identified, and an accurate intervention target is provided for TMS treatment.

The invention belongs to the technical field of satellite communication, and provides a distributed low earth orbit satellite inter-satellite routing method and device based on a minimum hop count path. The method comprises the following steps of: 1, constructing a low earth orbit satellite inter-satellite network with a topological structure, mapping the dynamically changed low earth orbit satellite inter-satellite network into a logic static network by utilizing a virtual node method, and determining the positions of a source satellite node and a target satellite node of a data packet to be forwarded in the logic static network; 2, determining a minimum hop count path and a path selection domain based on the positions of the source satellite node and the target satellite node; and step 3, starting from the source satellite node, gradually selecting a next-hop routing node of the data packet to be forwarded in the path selection domain according to the load state and survival probability of adjacent routing nodes until the next-hop routing node coincides with the target satellite node. According to the method and the device provided by the invention, the data transmission efficiency and reliability can be improved.

The invention relates to the technical field of digital twinning, in particular to a high-performance digital twinning system rendering method based on dual-grid and neural network mapping, and the method comprises the steps: constructing a high-precision calculation grid and a low-precision rendering grid, and building a space mapping relation between the two grids; executing multi-working-condition numerical simulation to obtain a high-dimensional simulation result snapshot matrix; the dimension reduction projection operator is used for reducing the dimension of a high-dimensional simulation result of any working condition into an r-dimensional feature vector; generating low-dimensional reference field data by using the space mapping relation; training the neural network until a mapping network parameter is obtained through convergence; obtaining current working condition parameters of the equipment in real time, and obtaining a current r-dimensional feature vector by using the dimensionality reduction projection operator; and quickly generating physical field prediction distribution on the low-precision rendering grid and performing pseudo-color rendering. According to the scheme, the problem that a traditional method is low in rendering efficiency is solved, and the method has the advantages of reducing computing resource consumption and keeping visualization precision.

The invention discloses a multi-view migration interpretable method based on soft variable embedding and discriminant structure preserving, and particularly relates to the technical field of machine learning, and the method comprises the steps: obtaining a plurality of multi-view features for EEG samples of a tagged source domain and an untagged target domain through the extraction of a plurality of features; after TSK-FS antecedent network mapping, constructing inter-domain connection by adopting migration soft variable embedded consequent learning; a data structure is retained through a local-global structure retention item, so that a discriminant neighborhood relationship of original data is retained to the greatest extent in a migration process; and a target function is constructed in combination with a multi-view learning strategy, iterative optimization is performed through an enhanced Lagrange multiplier algorithm, and finally a result is output by using a given classifier.

The invention discloses an assembly process knowledge graph construction and updating method based on graph attention network mapping, and the method mainly comprises the following steps: standardization system design construction, data collection, data classification, multi-modal data preprocessing, multi-source information extraction and graph node feature initialization. Semantic modeling and vector space mapping driven by a graph attention network, knowledge storage, construction of an assembly process knowledge graph by using stored knowledge data, and semantic complementation and dynamic updating of a process information model. Through the unique self-attention mechanism of the GAT, the weight can be dynamically distributed for the relationship among different information nodes, so that knowledge reasoning and expression are more accurately carried out, and the problems of knowledge fragmentation, management rigidity, semantic understanding deficiency, low knowledge utilization efficiency and the like in the prior art are systematically solved.

The invention discloses a vision and text alignment method and system, and belongs to the technical field of artificial intelligence and multi-modal semantic understanding. In order to solve the problem of insufficient vision and language deep fusion in the existing multi-mode question answering, the method mainly comprises the following steps: mapping visual features to a self-attention input space of a language model through a perceptron network, and introducing a fusion attention mechanism into each layer of decoder of the language model to realize layer-by-layer interaction processing of vision and texts. According to the method, deep alignment and fusion of visual information and text semantics can be realized, and the understanding and generation capabilities of a multi-modal question-answering system are improved.

The invention provides an efficient task scheduling strategy recommendation method and system for a distributed network surveying and mapping scene, and relates to the crossing field of network space surveying and mapping, network security, distributed scheduling and intelligent optimization recommendation algorithms. Analyzing the task request to extract a task type, a task target network address set, a regional distribution feature, a task execution timeliness requirement and calculation and bandwidth resources required by estimation, and forming a task feature vector; and performing state acquisition and capability evaluation on the distributed surveying and mapping nodes based on task resource requirements defined by the task feature vectors, and performing normalization processing to form node capability vectors. Through multi-source information vectorization and closed-loop optimization, adaptive matching of task scheduling and resource dynamics is realized.

The invention relates to the technical field of biomedical engineering, in particular to a digital twinning-based biological fermentation reaction kettle process control modeling method, which comprises the following steps: step 1, multi-source heterogeneous data acquisition and treatment: acquiring physical, chemical and biological data of a fermentation reaction kettle, and carrying out cleaning, complementing and time sequence alignment; step 2, mapping knowledge domain driven biological mechanism modeling: constructing a cell metabolism network mapping knowledge domain, associating process parameters with a biological mechanism, and optimizing a feeding strategy; step 3, performing digital twinborn driven multi-physical field simulation: constructing a CFD-molecular dynamics coupling model, and simulating a fermentation tank flow field and a cell metabolism process; 4, quality prediction and control of mechanism-data dual-mode driving are carried out; and 5, deploying and optimizing a cloud edge-end collaborative model. By constructing a'mechanism-data 'dual-drive architecture, the problems of insufficient adaptability, weak multi-modal data fusion capability and the like of a traditional model are solved.

A method, computer program product, and computing system for deploying a respective dependency agent on a plurality of computing devices within a cloud computing environment. A network topology subgraph associated with each computing device is generated. A placeholder for an unresolved connection is generated within the network topology subgraph associated with each computing device. Network mapping information associated with each computing device from the respective dependency agent is processed. The unresolved connection is resolved across the network topology subgraphs associated with each computing device in real-time by processing matching network mapping information from each computing device. An updated network topology subgraph associated with each computing device is generated by replacing the unresolved connection with a resolved connection.

The invention relates to an interpretable three-dimensional reconstruction method and device based on decoupling characterization learning and Gaussian splash, and a storage medium, and the method comprises the following steps: obtaining a monocular image, extracting two-dimensional features, obtaining compression features through convolution coding, obtaining a decoupled low-dimensional potential code through full connection processing, and obtaining a three-dimensional image; respectively converting into conditional representations of a geometric reconstruction branch and an appearance reconstruction branch; based on the two-dimensional coordinates on the predefined grid, three-dimensional points in a three-dimensional space are mapped through a plurality of MLP networks, a three-dimensional point cloud is obtained, and standard deviation-mean value modulation is carried out on the intermediate features by using conditional representation of geometric reconstruction branches; based on the three-dimensional point cloud, coding and projecting the three-dimensional point cloud to a plane to serve as initial three-plane features, and based on conditional representation of appearance reconstruction branches, coding the initial three-plane features into final three-plane features through a stylized U-Net network; and obtaining three-dimensional Gaussian scatter points based on the three-plane features and the three-dimensional point cloud to realize three-dimensional reconstruction.

The present invention discloses a robot adaptive force control and admittance control method and system for wall panel assembly. First, a high-precision photogrammetry system is combined to measure the position and posture of the robot end in real time, and the trajectory is corrected according to the measurement result and the desired position and posture to achieve the positioning of the robot in the position control direction. In the force control direction, a mapping relationship between the robot end force and the contact surface contact force is established through a heteroscedastic neural network mapping model, and an inverse model is jointly trained to map the desired contact force back to the desired end force, so that the contact force on the contact surface is controlled by adjusting the end force, and an adaptive force control and admittance control model is constructed; by interconnecting a virtual energy tank and the adaptive force control and admittance control model, the update of the control model parameters is guided to ensure the passivity of the system; at the same time, the parameters of the virtual energy tank are dynamically adjusted according to the system state, which can maintain the passivity and maximize the assembly accuracy and performance of the robot.

The invention relates to an intelligent teaching interaction feedback method and system driven by multi-modal sentiment analysis. The method comprises the following steps: firstly, acquiring multi-modal data; and carrying out separation and fusion on the multi-modal data by using a hybrid neural network model to generate a comprehensive emotion vector. And then, performing feature extraction on the comprehensive emotion vector, capturing a dynamic emotion sequence by means of a long-short term memory network, and comparing the dynamic emotion sequence with a preset threshold to obtain a current emotion state. Based on the current emotional state, a real-time emotional change curve is mapped by using a generative adversarial network, key time nodes are extracted from the real-time emotional change curve, and then a dynamic feedback parameter vector is generated. And finally, optimizing the dynamic feedback parameter vector by using a reinforcement learning algorithm and correcting a quantitative index to obtain an optimized emotional state evaluation result. The above steps can reflect the emotional state of the student more truly, realize dynamic tracking, accurate evaluation and optimized feedback of the emotional state of the student, and significantly improve the teaching interaction effect.

The invention relates to the technical field of duplexers, and discloses a duplexer temperature compensation parameter automatic adjusting method and device. The method comprises the following steps: performing working temperature sampling on a duplexer to obtain temperature sampling data, and performing distribution feature extraction on the temperature sampling data to obtain temperature feature data; inputting the temperature feature data into a mechanism information neural network for feature reconstruction to obtain temperature compensation features; performing radial basis function neural network mapping on the temperature compensation characteristics to obtain a duplexer frequency drift state quantity; performing sliding window adaptive Kalman filtering operation on the frequency drift state quantity of the duplexer to obtain a compensation parameter; the compensation parameters are input into a feedforward compensation controller, a voltage-controlled resonator of the duplexer is adjusted through a sliding mode control structure, a frequency compensation signal is obtained, and stable compensation of the frequency of the duplexer is achieved.

The invention discloses a robot self-adaptive force control and admittance control method and system for wallboard assembly, and the method comprises the steps: firstly, measuring the pose of the tail end of a robot in real time in combination with a high-precision photogrammetry system, carrying out the track correction according to a measurement result and an expected pose, achieving the positioning of the robot in a position control direction, and carrying out the positioning of the robot in a force control direction; the method comprises the following steps: establishing a mapping relation between robot end force and contact force of a contact surface through a heterovariance neural network mapping model, and jointly training an inverse model to map expected contact force back to expected end force, thereby controlling the contact force on the contact surface by adjusting the end force, and constructing a self-adaptive force control and admittance control model; a virtual energy tank is connected with a self-adaptive force control and admittance control model, so that updating of parameters of the control model is guided, and the passivity of a system is ensured; meanwhile, the parameters of the virtual energy tank are dynamically adjusted according to the system state, passivity can be kept, and the assembly precision and performance of the robot can be improved to the maximum extent.