157 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.

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.

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 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 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 discloses a bridge damping ratio identification method under a dimensionless control equation based on PINNs, and relates to the technical field of bridge damping ratio identification, the bridge damping ratio identification method comprises the following steps: carrying out network mapping on an input variable to obtain an output displacement; performing automatic differentiation on the output displacement to obtain each differential item; performing dimensionless processing on each parameter and differential item, and constructing a dimensionless free vibration differential equation containing an unknown damping ratio; loss is calculated, and a total loss function is constructed; and updating network parameters and trainable variables, and judging a training end point according to a network convergence mechanism. According to the bridge damping ratio identification method based on the PINNs under the dimensionless control equation, the rigidity and the linear density of the bridge are implied in dimensionless parameters, the magnitude order difference between all differential term coefficients is fundamentally eliminated, and bridge damping ratio identification based on the PINNs method is achieved.

The invention relates to an active switching method and device of master and slave nodes, equipment and a medium. The method comprises the following steps: in response to a received master node switching request for a remote dictionary service network, sending a blocking instruction to a current master node of the remote dictionary service network, so that the current master node stops executing a data processing instruction of a client within a preset duration required by the blocking instruction; selecting one slave node in the remote dictionary service network as a target slave node, wherein the target slave node is in an available state and data synchronization of the target slave node and the current master node is completed; switching the target slave node to a new master node of the remote dictionary service network within a preset duration, switching the current master node to slave nodes, and constructing a first network mapping between each slave node and the new master node and a second network mapping between the new master node and a master node address of the remote dictionary service network, the active operation of master-slave switching is realized, and the integrity and consistency of data in the master-slave switching process are ensured.

The invention belongs to the technical field of wireless communication safety monitoring and intelligent supervision, and particularly relates to a distributed low-cost hotel view-stealing and photo-stealing intelligent supervision system which comprises a wireless probe and a sky-wing cloud wireless analysis management and control platform. The wireless probe is connected to a hotel room router, collects space electromagnetic signals and terminal and AP wireless data, and pre-processes and reports the space electromagnetic signals and the terminal and AP wireless data; the cloud platform is constructed based on OpenStack, network mapping is updated through an ANO adversarial online automatic learning algorithm, wired / wireless attributes and event characteristics are subjected to correlation analysis by means of a VAE-CWGAN model, risks are identified, and an alarm is issued. The system realizes lightweight deployment, low-cost operation and maintenance, real-time intelligent supervision and room-level accurate positioning, can detect hundreds of wireless security incidents and dozens of cameras, is suitable for the fields of secrecy, national security and public security, and assists in security guarantee of smart civilized cities.

The invention discloses a multi-source heterogeneous network converged communication method and system in an emergency command scene, and the method comprises the steps: obtaining remote relay link data, distributed ad hoc link data and mobile access link data, carrying out the protocol analysis of the remote relay link data, and recognizing a protocol feature identifier; executing cross-network mapping according to the protocol feature identifier to generate a unified access table; executing link complementarity analysis by using a unified access table, and extracting strong complementary link pairs to form a multi-path transmission scheme; identifying group communication nodes, establishing a protocol conversion channel, executing protocol conflict detection to identify conflict resolution nodes, and performing protocol remapping to generate a collaborative routing table; performing channel reverse extraction, performing routing compensation processing, performing priority reversal identification, and performing transmission fault-tolerant configuration to construct a convergence communication state; and determining a converged communication domain, converting the collaborative routing table into a multi-network converged communication deployment scheme, realizing the fusion of satellite communication, ad hoc network communication and mobile communication, and providing a communication guarantee scheme for emergency command.

A method is performed by a network node for precoding of downlink communications predicting downlink channel state. The method receives a compressed-dimensional representation of a channel state that is encoded through an encoder neural network of a UE. The method maps the compressed-dimensional representation through a forward-prediction neural network to generate a compressed-dimensional predicted representation of a forward channel state at least one step forward in time k+Δ, wherein Δ is a number of steps forward in time. The method decodes the compressed-dimensional predicted representation of the forward channel state through a decoding neural network to generate an increased-dimensional predicted representation of the forward channel state, where the increased-dimensional predicted representation is a higher dimensional representation than the compressed-dimensional predicted representation. The method precodes signals for transmission through the downlink channel to the UE based on the increased-dimensional predicted representation of the forward channel state.

According to the ship waste heat multi-source coordinated recovery method and system, ship multi-source temperature signals and heat exchange pipeline layout are collected, and heat value rating is carried out on the temperature signals to form heat grade distribution; high-quality hot spots are positioned to establish heat exchange contact, and a dense heating area is formed through cascade strengthening; constructing a thermal load fluctuation curve to identify an intermittent heat supply source, and establishing a thermal circuit intersection to form a flexible heat supply field; dividing effective temperature levels, configuring hierarchical heat exchange to generate a stepped utilization chain, and forming a heat storage and release network output coordination control table; exporting a flow direction guide field, identifying a heat loss node, setting a compensation channel, remodeling a network thermal state through gradient utilization network mapping to obtain a whole network thermal spectrum, and identifying heat flow convergence characteristics to determine a convergence center; and carrying out supply-demand coordination analysis to identify a supply-demand difference value and a reserve capacity, carrying out fluctuation evaluation to generate a coordination margin configuration regulation and control response strategy, completing waste heat multi-source coordination recovery, and improving the ship energy utilization efficiency and the system operation stability.

The embodiment of the invention provides a network surveying and mapping behavior defense alarm method and system, and belongs to the technical field of network surveying and mapping behavior defense. The method comprises the following steps: collecting network flow mirror image data, equipment logs and external surveying and mapping information, and constructing a surveying and mapping characteristic data set; matching a scanning behavior through a preset surveying and mapping tool fingerprint database, and generating a graded alarm event according to the scanning density and the target sensitivity of the matched scanning behavior; generating behavior combination features based on the aggregation analysis result; performing intention calibration on the behavior combination characteristics through a preset behavior rule base, and generating a surveying and mapping intention analysis report; and executing differential defense alarm including honeypot network redirection and equipment fingerprint counterfeiting. According to the invention, by constructing the surveying and mapping feature data set, the scanning behavior is accurately matched and the graded alarm event is generated, so that misjudgment is avoided. Differential defense alarm measures can effectively cope with surveying and mapping behaviors of different risk levels, and safety risks caused by malicious surveying and mapping behaviors are reduced.

The invention relates to a brain anomaly network positioning method and system based on functional connection network mapping in the technical field of neural image data processing. The brain abnormal network positioning method comprises the following steps: calculating a whole brain function network diagram connected with each abnormal site by using resting state function connection data of large-scale health subjects based on a plurality of dispersed abnormal sites reported in previous literatures; then superposing the function network diagrams to obtain a network probability graph; and finally, filtering the probability graph through a threshold value to obtain a final core anomaly network graph. According to the method, the inherent functional connection architecture of the brain is used as a reference system, abnormal sites which seem to be uncorrelated in different researches are successfully traced and unified to a common and stable functional network, and compared with a single brain region marker, the generated network-level biomarker integrates more source evidences, so that the network-level biomarker has the advantages that the network-level biomarker can be widely applied to the field of biomarkers of the brain region. Therefore, the problems of result heterogeneity and inconsistency in brain abnormality discovery are solved.

The invention provides a method and system for applying uncertain prior information in a deep learning network, relates to the technical field of model training and application, and solves the technical problem that prior information cannot be applied in the deep learning network in the prior art. The method comprises the following steps: inputting prior information into a prior information mapping network, and mapping to obtain a first prior feature; verifying the prior information to obtain a reliable state of the prior information; if the reliable state represents that the prior information is reliable, determining the first prior feature as a target prior feature; if the reliable state characterizes that the prior information is unreliable, determining a default second prior feature as a target prior feature; the second prior feature is consistent with the data feature of the first prior feature; and embedding the prior information mapping network and the target prior features into the target deep learning network.

The invention provides a delay-aware dual-time scale resource allocation method, which belongs to the technical field of virtual network mapping, and optimizes on different time scales by decomposing a delay optimization task into two levels, namely a short time scale and a long time scale. Within a short time scale, a deep reinforcement learning algorithm is used to output a mapping decision in time, network changes are quickly responded, and resource allocation is carried out in real time; and on a long-time scale, long-term network load and resource requirements are optimized through a remapping mechanism, and balance and stability of global resource allocation are ensured.

This invention discloses a method and system for multi-source heterogeneous network converged communication in emergency command scenarios. It acquires remote relay link data, distributed self-organizing link data, and mobile access link data. For the remote relay link data, it performs protocol parsing to identify protocol feature identifiers and generates a unified access table based on these identifiers through cross-network mapping. Using the unified access table, it performs link complementarity analysis to extract strongly complementary link pairs and form multi-path transmission schemes. It identifies group communication nodes, establishes protocol conversion channels, performs protocol conflict detection to identify conflict resolution nodes, and performs protocol remapping to generate a collaborative routing table. It performs channel reverse extraction, route compensation processing, priority inversion identification, and transmission fault tolerance configuration to build a converged communication state. Finally, it determines the converged communication domain and converts the collaborative routing table into a multi-network converged communication deployment scheme, achieving the convergence of satellite communication, self-organizing network communication, and mobile communication, providing a communication guarantee solution for emergency command.

The invention discloses a blunt metal corrosion assessment method and system based on a piezoelectric response mechanism, and the method comprises the steps: preparing a piezoelectric coating, electrically connecting a blunt metal sample with the piezoelectric coating through a wire, placing the blunt metal sample in a corrosion medium, obtaining the original current-time discrete data of the blunt metal corrosion process, and calculating the current-time discrete data of the blunt metal corrosion process. Preprocessing to obtain standard current-time discrete data, and integrating the standard current-time discrete data into a current input vector and a time input vector; a deep learning blunt metal corrosion stage classification prediction model is constructed, linear mapping embedding processing is performed on current and time input vectors in a data embedding layer, current feature and time feature output vectors are generated, a current-time joint vector is generated in a data splicing layer in a splicing manner, and the current-time joint vector is subjected to classification prediction. Inputting multilayer perceptron network mapping to obtain probability distribution of a blunt metal corrosion stage; an evaluation result is reported to a cloud end, and early warning is triggered when abrupt change of the corrosion current of the blunt metal is monitored. According to the invention, the accuracy of blunt metal corrosion evaluation can be improved.

The invention discloses an indirect carbon emission rapid accounting method and system. The method comprises the steps of obtaining power generation side data, load side data and network topology data of a target regional power grid; performing equivalent load flow calculation on the target regional power grid based on the power generation side data, the load side data and the network topology data to obtain a load flow calculation result; constructing an equivalent lossless network mapping model of power carbon emission of a load side and a power generation side according to a load flow calculation result to obtain a user side carbon emission reference value in a full thermal power scene; based on the new energy unit data, the carbon emission intensity difference value of the thermal power unit and the new energy unit and the total electricity consumption in the target area, calculating an area average carbon reduction factor; calculating the carbon reduction emission of each user according to the average carbon reduction factor and the electricity consumption of each user, and obtaining the actual indirect carbon emission of each user according to the carbon reduction emission and a user side carbon emission reference value; and integrating the actual indirect carbon emission of each user to obtain the total indirect carbon emission of the user side.