113 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.

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 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 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.

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 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.

The application discloses a kind of third party construction fixed and mobile intelligent monitoring device, it is related to safety monitoring technical field, specifically includes: acquisition positioning unit, pipe network mapping unit, intelligent fusion evaluation unit and early warning disposal unit;Acquisition positioning unit carries out continuous image acquisition to gas pipe network key protection area by fixed monitoring equipment, and supplementary acquisition is carried out to fixed monitoring blind area or temporary construction area by mobile monitoring equipment;Pipe network mapping unit maps the space-time collection information corresponding to the collection image to the gas pipe network protection range, determines the correlation between construction target and gas pipe network;Intelligent fusion evaluation unit identifies construction target and its behavior state, generates construction risk grade;Early warning disposal unit corrects risk position and revises construction risk grade when meeting preset early warning condition, generates alarm information, the application can improve third party construction target positioning accuracy and risk early warning reliability.

The invention relates to the field of parking lot planning, in particular to a dynamic planning method for parking lot facilities, which comprises the following steps: enabling patient information associated with a patient vehicle entering a hospital parking lot to pass through a neural network mapping model based on a multi-layer perceptron architecture to generate a parking lot facility patient utilization index; calculating an idle parking space criterion score based on the hospital entrance distance, the barrier-free facility distance and the parking space environment characteristics of the current idle parking space of the hospital parking lot; based on the parking lot facility patient and the vacant parking space criterion score, generating a vacant parking space patient comprehensive matching score through an analytic hierarchy process; and determining a recommended parking space based on the comprehensive matching score of the idle parking space patient, and carrying out parking planning and guiding on the patient vehicle based on the recommended parking space. According to the invention, multi-element personalized demands of different patients can be accurately identified, the adaptation degree of the parking space to the patient is comprehensively covered through the analytic hierarchy process, and intelligent parking planning of the hospital parking lot with the patient as the center is realized.

The invention relates to multi-domain virtual network security mapping for cross matching and optical path separation fault tolerance, and belongs to the technical field of optical fiber communication. According to the method, a double-layer block chain network architecture is adopted, a main chain is used for inter-domain resource collaborative scheduling and information synchronization of an optical network, and a secondary chain realizes intra-domain resource autonomous management and trust maintenance of the optical network; in the virtual node mapping stage, mapping preference values between virtual nodes and physical nodes are introduced to construct a mapping bigraph, and virtual node mapping is optimized through a cross matching mechanism; in the virtual link mapping stage, the security and reliability of quantum key distribution are guaranteed by adopting multi-path routing and a security redundant path, and an encrypted data transmission optical path based on minimum spectrum consumption is adopted; punishing malicious nodes according to a virtual network mapping result, and tracing and updating credibility values of non-malicious nodes participating in virtual network mapping through block information. According to the method, the security and the resource utilization rate of virtual network mapping in the optical network multi-domain environment can be improved.

A system and method for self-adjusting cybersecurity analysis with network mapping, wherein a reconnaissance engine gathers data about a client's computer network from the client, from devices and systems on the client's network, and from the Internet regarding various aspects of cybersecurity. Each of these aspects is evaluated independently, weighted, and cross-referenced to generate a cybersecurity score by aggregating individual vulnerability and risk factors together to provide a comprehensive characterization of cybersecurity risk using a transparent and traceable methodology. The scoring system itself can be used as a state machine with the cybersecurity score acting as a feedback mechanism, in which a cybersecurity score can be set at a level appropriate for a given organization, and data from clients or groups of clients with more extensive reporting can be used to supplement data for clients or groups of clients with less extensive reporting to enhance cybersecurity analysis and scoring.

This application discloses a penetration testing method, apparatus, device, and medium. The method includes: performing network mapping processing on a target object to obtain first network asset data and first vulnerability data of the target object, wherein the first vulnerability data is known vulnerability data; determining unknown vulnerability data of the target object based on the first network asset data, the first vulnerability data, and an attack prediction model; generating a target attack graph of the target object based on the first network asset data, the first vulnerability data, and the unknown vulnerability data; inputting the target attack graph into a trained deep learning engine to obtain the optimal attack path of the target attack graph; and performing automated penetration testing on the target object based on the optimal attack path of the target attack graph. According to the embodiments of this application, the limitations of penetration testing schemes can be reduced and the penetration testing effect can be improved.

The invention relates to a method (100) for configuring a mesh topology Ethernet optical network connecting a plurality of devices and switches. Each switch includes at least one transceiver. Each transceiver has a transmitting section and a receiving section. Each transceiver section is associated with a virtual port. The method (100) includes an initialization (101), a network mapping (103) including the identification of possible links enabling communication between two devices, a possible link being a sequence of virtual ports enabling communication. The method (100) then includes a definition (104) of a network configuration including selected links, a deployment (105) of the network configuration including the disabling of virtual ports not used by the selected links, and a power-off (106) of the transceiver sections equipping each disabled virtual port.Figure for the abbreviation: [Fig. 1].

The application relates to a wireless debugging port external network forwarding method and system under a Hongmeng 5.0 system, which comprises the following steps: a Hongmeng device end starts a remote debugging daemon process through a service manager, generates a device digital certificate, registers to a relay server, stores a returned certificate ID to a TEE security storage area; a debugging connection security area model is constructed based on historical debugging data and is solidified to a TEE trusted execution environment; a dynamic token is generated in the TEE, and after verification, the relay server is requested to map a port to an external network; real-time debugging data is captured through a kernel probe, and features of the real-time debugging data are extracted to generate real-time debugging points; the boundary shortest distance of the real-time debugging points and a security area boundary is calculated, and when the boundary shortest distance is smaller than a threshold value, a session is interrupted and a hierarchical optimization strategy is triggered. The application realizes high-security wireless debugging port external network mapping, and guarantees debugging safety through dynamic token verification, behavior feature monitoring and a fuse mechanism.

The application provides a virtual reality classroom simulation method, and relates to the technical field of data processing, and the method comprises the following steps: obtaining a high-dimensional data matrix with space-time correlation characteristics; constructing an electrical connection relationship graph; obtaining quantum state evolution results of a power system transient process; calculating a plurality of physical field distribution parameters corresponding to the equipment; based on the plurality of physical 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 combining mechanical parameters of the equipment to generate force feedback control instructions through a neural network mapping model; and generating a virtual reality scene that is adaptive to visual response and operation state of the operator by using a dynamic rendering engine according to eye tracking data, arc dynamic characteristic data and the force feedback control instructions. The embodiment of the application can improve the authenticity and interactivity of virtual reality classroom simulation.

The application discloses a multi-view transfer interpretable method based on soft variable embedding and discriminative structure preservation, and particularly relates to the technical field of machine learning, and is characterized in that, for EEG samples with labeled source domains and unlabeled target domains, a plurality of multi-view features are obtained through a plurality of feature extraction; after being mapped by a TSK-FS antecedent network, an inter-domain connection is constructed by using a transfer soft variable embedded consequent learning; a data structure is preserved through a local-global structure preservation term, so that the discriminative neighborhood relationship of original data is preserved to the greatest extent in the transfer process; a target function is constructed in combination with a multi-view learning strategy, and is iteratively optimized through an enhanced Lagrange multiplier algorithm, and finally, an output result is output by using a given classifier.