42 results about "Network characterization" patented technology

The invention provides a building dynamic structure health monitoring method. The method comprises the steps that original data of a multi-mode sensor is acquired and preprocessed; constructing topology sensing network representation reflecting the spatial distribution and structure connection relation of the sensors; extracting multiple spatio-temporal scale features; local-to-global structure state mapping is realized by using a recurrence plot evolution predictor; integrating structural dynamics knowledge into the neural network model to perform physical constraint correction; and constructing a multi-dimensional health index set and outputting damage probability evaluation. According to the invention, accurate mapping from the local dense monitoring area to the global structure state under the non-uniformly distributed sensor network is realized, and the accuracy and reliability of structure health monitoring are improved.

A method includes scanning a network having a first and second host, obtaining, via the scanning, a first and second type information of the first and second host, respectively, the first or second type information including a device category, obtaining, via the scanning, a first and second scaling factor of the first and second host, respectively, calculating, a first criticality score of the first host based on the first type information and the first scaling factor, calculating a second criticality score of the second host based on the second type information and the second scaling factor, calculating a first host risk score (HRS) for the first host based on the first criticality score, calculating a second HRS for the second host based on the second criticality score, and applying a security patch on the first host prior to the second host first HRS is higher than the second HRS.

A method includes scanning a plurality of hosts in a network to obtain risk information of each instance of vulnerability associated with each host during a period, calculating a vulnerability risk score (VRS) for each instance of the vulnerability based on the associated risk information, determining a number of vulnerabilities associated with each of the plurality of hosts during the period, obtaining a criticality score of each of the plurality of hosts, obtaining for each host, a representative VRS based at least in part on the VRS for each instance of vulnerability associated with the host, calculating a host risk score (HRS) for each host based on the representative VRS, the number of vulnerabilities and the criticality score of the host, calculating a network risk score (NRS) for the network based on the HRSs, and facilitating a security action based on the HRS for each host and the NRS.

The invention provides a hydraulic fracturing pilot test method, electronic equipment, a storage medium and a program product, and relates to the technical field of hydraulic fracturing test. The method comprises the following steps: determining a fractured well in a to-be-tested site and a plurality of test wells meeting a preset position relationship with the fractured well; determining the crustal stress state of the target test well; according to the crustal stress state and preset hydraulic fracturing process parameters, fracturing testing is conducted on the fractured well, and collected data of the detection equipment is obtained; based on the rock mass sectioning image of the preset position of the to-be-tested site, crack network characterization information is determined, and the crack network characterization information is used for indicating the shape of a crack formed by fracturing and the distribution condition of a propping agent; according to the collected data, the fracturing test process is inverted, and fracturing dynamic process information is obtained; and generating a hydraulic fracturing process optimization scheme according to a comparison result between the fracturing dynamic process information and the fracture network characterization information. According to the method, the accuracy and practicability of a hydraulic fracturing test result are improved.

A method includes scanning a network having a first and second host, obtaining, via the scanning, a first and second type information of the first and second host, respectively, the first or second type information including a device category the first or second host belongs to, obtaining, via the scanning, a first and second scaling factor of the first and second host, respectively, calculating, a first criticality score of the first host based on the first type information and the first scaling factor, calculating a second criticality score of the second host based on the second type information and the second scaling factor, and facilitating to apply a security patch on the first host prior to the second host when the first criticality score is higher than the second criticality score.

The invention discloses a fetal heart uterine contraction automatic interpretation system based on network characterization, which comprises a network model, and the network model comprises a CV module, an AT module, an SW module and a TC module which are connected in sequence; the CV module encodes low-level spatio-temporal information in the CTG signal through three convolutional layers; the AT module comprises a plurality of self-attention layers; the TC module uses a time convolution network to extract advanced time features in a time sequence, and inputs the advanced time features into a full connection layer with a Sigmoid classifier. According to the method, a multi-representation and multi-model integration thought is adopted, related features beneficial to CTG interpretation are fully extracted, the problem that a manual feature extraction algorithm is insufficient in capturing a long-time-span dependency relationship is solved, subsequent various analysis and research on CTG signals are facilitated, and the method is suitable for popularization and application. More accurate and reliable diagnosis information is provided for doctors and applied to the field of medical signal processing, and a more accurate and reliable auxiliary diagnosis tool is provided for clinicians.

The invention provides an Internet infrastructure knowledge graph construction method based on network representation learning and classification and grading thought. The method comprises the following steps: constructing an ontology model of Internet infrastructure; collecting map data; utilizing the ontology model and the atlas data to create entities and extract relationships between the entities; classifying and grading the entities to obtain classification results and grading results corresponding to the entities; an initial knowledge graph is constructed through the relations between the entities, and each entity in the initial knowledge graph is additionally associated with the corresponding classification result and grading result; and based on the network representation learning and the graph data, updating the edges, the classification result and the grading result in the initial knowledge graph to obtain a final knowledge graph. The knowledge graph is constructed and updated through network representation learning and classification and grading thoughts, so that the dynamic association relationship between the entities is accurately captured, wrong association edges are screened out, and the network representation accuracy and availability of the entities are improved.

The invention provides a complex network key node identification method based on an attention mechanism and multi-scale feature fusion. The method comprises the following steps: S1, generating a feature matrix; s2, inputting the data into a training model composed of a multi-scale convolution branch and a channel attention branch; and S3, outputting an identification result by using the trained model. According to the method, sparse network representation can be solved through a dynamic mechanism, so that more robust and accurate key node identification is realized.

A method includes scanning a plurality of hosts in a computer network to obtain, during a predetermined time period, risk information of each instance of vulnerability associated with at least one host of the plurality of hosts, wherein the risk information comprises a common vulnerability scoring system (CVSS) score, an exploitability measurement and a measurement parameter of identified link to one or more bad actors associated with the at least one host, calculating, for the at least one host, a vulnerability risk score (VRS) for each instance of the vulnerability of the at least one host based on the associated risk information, obtaining a representative VRS based at least in part on the VRS for each instance of vulnerability of the at least one host, and facilitating at least one security action based on the representative VRS.

A method includes scanning a network having a first and second host, obtaining, via the scanning, a first and second type information of the first and second host, respectively, the first or second type information including a device category the first or second host belongs to, obtaining, via the scanning, a first and second scaling factor of the first and second host, respectively, calculating, a first criticality score of the first host based on the first type information and the first scaling factor, calculating a second criticality score of the second host based on the second type information and the second scaling factor, and facilitating to apply a security patch on the first host prior to the second host when the first criticality score is higher than the second criticality score.

A method includes scanning a plurality of hosts in a computer network to obtain, during a predetermined time period, risk information of each instance of vulnerability associated with at least one host of the plurality of hosts, wherein the risk information comprises a common vulnerability scoring system (CVSS) score, an exploitability measurement and a measurement parameter of identified link to one or more bad actors associated with the at least one host, calculating, for the at least one host, a vulnerability risk score (VRS) for each instance of the vulnerability of the at least one host based on the associated risk information, obtaining a representative VRS based at least in part on the VRS for each instance of vulnerability of the at least one host, and facilitating at least one security action based on the representative VRS.

A method includes scanning a network having a first and second host, obtaining, via the scanning, a first and second type information of the first and second host, respectively, the first or second type information including a device category, obtaining, via the scanning, a first and second scaling factor of the first and second host, respectively, calculating, a first criticality score of the first host based on the first type information and the first scaling factor, calculating a second criticality score of the second host based on the second type information and the second scaling factor, calculating a first host risk score (HRS) for the first host based on the first criticality score, calculating a second HRS for the second host based on the second criticality score, and applying a security patch on the first host prior to the second host first HRS is higher than the second HRS.

A multi-modal uncertainty perception mechanical life prediction method based on random model updating comprises the steps that firstly, a drift network is constructed, a representation extraction block RDB comprises four layers including a data structure DS, short-term local feature refinement, long-term time trend development and down-sampling, and residual connection is used for capturing cross-layer information and promoting gradient flow; a diffusion network is constructed, Lipschitz continuity is followed, and ReLU is selected as an activation function; then each subnet is trained, the first two parts of a target function of the subnet are regression terms of a distributed drift network and an adjustment diffusion network, and the latter component belongs to an OOD sample; then, the uncertainty of each subnet is quantified, and finally, multi-modal fusion is carried out based on the respective uncertainty; according to the method, modeling is carried out on the uncertainty of each mode through the fractional order stochastic differential equation sub-network, dynamic fusion is carried out according to the uncertainty of modal characteristics, and a final prediction result and the corresponding uncertainty are obtained through regression reasoning.

The invention discloses a multi-category industrial anomaly detection method based on a state space model. The method comprises the following steps: 1, collecting and preprocessing image data of multi-category industrial products; 2, constructing an encoder network to encode the image to obtain a multi-scale semantic feature map; 3, constructing a multi-scale feature fusion network to reduce feature redundancy and strengthen network characterization capability; 4, constructing a decoder network to reconstruct multi-scale features; and 5, detecting an abnormal region according to the reconstruction error. The multi-class industrial anomaly detection method based on the state space model is different from a single-class anomaly detection method, and expresses accurate anomaly classification and positioning effects on multiple classes of industrial products at the same time; different from a convolutional neural network and a self-attention mechanism, the method utilizes a state space model to realize global relation modeling and improve the detection efficiency according to the characteristics of keeping linear complexity along with the input size.

The invention belongs to the field of core analysis, and discloses a high-temperature and high-pressure model pore network characterization system which comprises an analysis rack and an analysis model, the analysis model comprises a model main body, an execution assembly and a main body lower pressing cap, and an upper pressing plate and a lower top plate are arranged at the upper end and the lower end of the model main body respectively; a hanging basket for placing a detection sample is arranged at the bottom end of the upper pressing plate, and a Wood alloy block is arranged at the upper end of the lower top plate. Through mutual cooperation of the model main body, the upper pressing plate, the lower top plate, the upper execution assembly, the lower execution assembly and other parts, a rock core analysis experiment is realized in a molten alloy mode, and the upper execution assembly and the lower execution assembly pressurize the rock core analysis experiment in an oil pressure mode. In this way, the alloy has higher contrast contrast with the matrix particles, the imaging effect is better, and the problem that in a traditional method, fine depiction of the pore structure in the whole pore diameter range is difficult to achieve is effectively solved.

The application discloses a kind of text enhanced multi-modal road network representation learning method, belong to city computing field.The method includes: first, extract target area road vector data, road section static attribute and the panorama street view image of each road section center point;Multi-modal large language model is generated natural language description by street view image, combined with static attribute and prompt word is output structured text description by first large language model, further obtain the directed road network graph containing structured text description;Topology enhanced semantic representation is output based on structured text description by graph neural network, further by word element random masking, the graph neural network and topology enhanced semantic representation of training are completed;Second large language model can output specified downstream task result according to topology enhanced semantic representation.The application effectively solves the problems that the existing road network representation method is difficult to model the correlation of modes, the reasoning ability of data missing is weak, and the high-order semantic modeling is insufficient.

This invention discloses a circRNA-disease association prediction method based on multi-source feature fusion, belonging to the field of disease-aided diagnosis technology. The method first constructs a circRNA-disease association network and extracts the GIPK functional features of the disease and the Transformer sequence features of the circRNA; then, it systematically learns the network topology embedding at three scales: microscopic, mesoscopic, and macroscopic; finally, it deeply fuses the above multi-source features and inputs them into an XGBoost classifier. This invention systematically solves the three major technical defects of existing technologies—"lack of biological semantics," "single network representation," and "inefficient feature fusion"—by introducing biological attribute features, multi-scale network structure features, and a deep fusion strategy, achieving high-precision, high-robustness, and highly biologically interpretable association prediction.

The present invention discloses a method for cancer driver gene mining and interpretability analysis based on heterogeneous networks. The method comprises: constructing a multi-omics heterogeneous network and extracting initial node features through the multi-omics heterogeneous network; constructing an information transfer subgraph by random walk along a specific meta-path, and performing meta-path-based heterogeneous network feature extraction to obtain a representation vector of the gene node; inputting the representation vector of the gene node into a multi-layer linear classifier, using the multi-layer linear classifier to perform node classification, and outputting the analysis results of the cancer driver gene. The present invention calculates the representation vector within the meta-path through a network representation algorithm that includes multi-head attention and self-attention mechanisms, and calculates the contribution weight of each meta-path to the classification problem through global attention, thereby obtaining a more robust and effective cancer driver gene prediction result.

The present invention discloses a system for detecting network security threats using static and runtime data, belonging to the field of network security technology, including: a data collection module, a data processing module, a threat detection module, and an intelligent control module. The present invention solves the problem that the prior art cannot timely detect the threats existing in network security, resulting in poor network security control effect. The present invention collects real-time network data, performs comprehensive processing on the collected real-time network data based on retrieval, grouping, sorting, feature extraction, and calculation to determine real-time network characterization data, performs network security threat detection on the real-time network characterization data to determine the network security threat detection result, performs mining analysis on the network security threat detection result to determine the network security intelligent control method, and intelligently controls network security based on the network security intelligent control method, which can timely detect the threats existing in network security and improve the network security control effect.

The application discloses a coal body hydraulic fracturing fracture three-dimensional space network reconstruction and characterization method, which is based on real CT three-dimensional body data, can realize the transformation of fracture structure from geometric visualization to parameterization and evaluation, can directly reflect the real connection relationship of the fracture in the three-dimensional space by identifying and three-dimensionally reconstructing the internal fracture of the coal body before and after fracturing, avoids the connection misjudgment and scale deviation caused by two-dimensional slice inference, improves the authenticity of the evaluation result from the source, and simultaneously adopts skeletonization and node-edge modeling on the fracture network, converts the three-dimensional voxel fracture structure into a node-edge form fracture network model, significantly reduces the calculation complexity under the premise of ensuring the fracture space connection relationship, enables the connectivity and flow conductivity characteristics of the fracture network to be expressed in the form of unified parameters, and is especially suitable for the fracture network characterization of the laboratory true triaxial test and the digital coal rock sample data based on CT scanning.

The invention discloses a method for rapidly characterizing the average grain size of a ferromagnetic material based on magnetic Barkhausen electromagnetic nondestructive testing. The method comprises the following steps: preparing a sample; based on an EBSD material characterization method, carrying out quantitative characterization on the average grain size of the ferromagnetic material; based on magnetic Barkhausen noise detection equipment, electromagnetic nondestructive detection is carried out on the ferromagnetic material, and electromagnetic characteristic parameters representing the magnetic characteristics of the ferromagnetic material are obtained; and by taking the electromagnetic characteristic parameters as input and the average grain size as output, establishing a characteristic microstructure deep neural network characterization model of the magnetic Barkhausen characteristic parameters so as to realize nondestructive testing of the characteristic microstructure-average grain size of the ferromagnetic material. According to the invention, nondestructive testing of the average grain size of the ferromagnetic material is realized.

The application discloses a data processing and feature scoring method and device based on a supply chain heterogeneous network, and relates to the technical field of supply chain data management.The application is executed by a physical device with a data acquisition port, a data cache medium, a central computing unit and a data output port, a dynamic updating supply chain heterogeneous transaction network including multiple types of nodes and edges is constructed, the network is divided into a credit turnover frequency sub-network representing transaction frequency between enterprises, a credit turnover amount sub-network representing transaction scale, a financing frequency sub-network representing financing activity and a financing amount sub-network representing financing scale according to business dimensions, multi-dimensional features are extracted, preprocessed and output to a standardized score through two-layer mapping, and the model weight is iteratively optimized through supervised learning.The application overcomes the technical defects of homogeneous networks losing heterogeneous information, and realizes global graph data processing and quantitative output of multi-source heterogeneous data.

The invention relates to the technical field of conference training scene behavior analysis, and discloses a conference training scene behavior analysis method based on time sequence vision, and the method comprises the steps: employing a Backone + Neck + Head feature extraction mode for a single video frame in a time sequence through an analysis system; according to the method, in the aspect of feature extraction on a visual frame, a Swindow-transformer module is adopted to play a role similar to convolution operation in a CNN in image processing, but the Swindow-transformer module has advantages in the aspects of global relation processing, long-distance information transmission and the like based on the characteristics of a transformer structure, and the abstract ability and expression ability of the model on image content can be effectively improved; a CBAM module is adopted, and through comprehensive utilization of channel attention and space attention mechanisms, the image feature processing and learning ability of a CNN model can be improved, the network characterization ability is enhanced, and the performance and accuracy of an image processing task are improved; the four detection heads are adopted to improve the detection capability of targets with different scales, so that the problem of missing detection or poor detection effect is solved.

A method and system for enhancing large language model auxiliary network operation and maintenance capability, the method comprising: collecting a training sample, the sample collection process comprising: inputting analog network data into a network simulator, generating a text attribute graph of an analog network and a corresponding natural language query, and collecting network behavior data according to the natural language query; a large language model is constructed and trained, and the working process is as follows: natural language query and equipment configuration texts are generated through a configuration encoder, text attribute graphs are obtained through a topology encoder, then the text attribute graphs are spliced into network representation, and then the network representation is mapped into a Q layer and a V layer of an attention mechanism of the large language model in a low-rank fine tuning mode; and the large language model receives natural language query input by the user and the text attribute graph of the current network for reasoning output. The invention relates to the field of information communication networks, can efficiently model multi-modal network data, enhances the understanding ability of a large model for the current network state, and effectively assists network operation and maintenance.

The invention discloses a fracture network optimization characterization and uncertainty control method and system, and the method comprises the steps: S1, constructing an SA-UNet substitution model and a DAOCRN deep learning inversion model based on a self-attention mechanism according to the multi-source monitoring data of a high-level radioactive waste disposal site and a typical fracture network sample; s2, according to the SA-UNet substitution model, initial population generation and simulation calculation, mismatch degree evaluation and optimal set screening are carried out through genetic algorithm inversion, and a fracture probability result is obtained; and S3, according to the fracture probability result and a DAOCRN deep learning inversion model, through deep learning inversion optimization representation, obtaining a final fracture network representation result with the minimum mismatch degree. By adopting the technical scheme of the invention, accurate characterization and uncertainty reduction of the fracture network in a high-level radioactive waste deep geological disposal scene are realized.

The invention discloses a data recommendation method and device, electronic equipment and a computer readable medium, and relates to the technical field of computers.The method comprises the steps that user context data and user history browsing item data are obtained according to a received data recommendation request; based on the historical browsing article data of the user, obtaining a pooling vector through a pooling network, and according to the historical browsing article data of the user and the to-be-recommended article data, obtaining a deep interest network representation vector through a deep interest network; determining a first mapping matrix and a second mapping matrix based on the preset representation dimension and the preset real number space configuration data, and further determining a weighted proportionality coefficient based on the user context data; determining a target interest vector according to the weighted proportionality coefficient, the pooling vector and the deep interest network representation vector; and based on the target interest vector, determining target recommended article data in the to-be-recommended article data. The performance of the recommendation system is improved, and personalized recommendation is realized.

The invention relates to the field of geological exploration, and discloses a fracturing fracture net depicting method and device, a storage medium and a processor, and the method comprises the steps: calculating the cumulative flow capability and the cumulative storage capability of a fracturing fracture net according to a tracer agent actual output curve; determining the number of main fractures in the fracture network based on the accumulated flow capacity and the accumulated storage capacity; establishing a tracer migration mathematical model according to the number of main fractures in the fracture network, and determining a target function based on the tracer migration mathematical model; fitting the tracer agent actual output curve based on the objective function to obtain a fitted tracer agent output curve, and obtaining the structural parameters of the main fracture in the fracture network based on the fitted tracer agent output curve. The structural parameters of the main crack in the fracturing crack net can be obtained, so that the accuracy of depicting the fracturing crack net can be improved.

The invention discloses a factor network structure self-construction and characterization method and device, equipment and a medium. The method comprises the steps that a plurality of samples are collected, each sample comprises a plurality of nodes, and an initial main value and an initial other value of each node of each sample are established; determining the category number of each node, constructing a clustering model according to the data of each node and the category number, and determining the category to which each node belongs according to the clustering model; traversing each sample, and training a factor network structure model by using an attention model according to the category to which each node belongs; and constructing a neural network message passing network according to the factor network structure model, and representing each node and each sample by using the neural network message passing network to obtain network representation of each node and each sample. According to the method, the network relationship between the single factor and other remaining factors is automatically learned by using the attention model, the network structure can be automatically learned without supervision, and the representation of each factor and the representation of the whole network can be calculated in a neural network mode.

The invention relates to the technical field of data analysis, in particular to a cell differentiation trajectory analysis method based on topological entropy, which comprises the following steps: acquiring gene expression data of cells in the same cell type; standardizing the gene expression data to obtain a cell standardized expression matrix, and constructing a single-cell gene regulation network according to the cell standardized expression matrix; constructing a single-cell gene regulatory network representation matrix according to the single-cell gene regulatory network; obtaining a single cell differentiation trajectory and a cell topological entropy matrix according to the single cell gene regulation and control network characterization matrix; and analyzing according to the cell topological entropy matrix and the single cell differentiation trajectory to obtain the key evolution determining gene of the adjacent state in the differentiation trajectory. According to the method, the key evolution determining gene is identified based on the topological entropy, and the fluctuation key nodes influencing the stability of the adjacent state network structure can be effectively screened.

The invention is suitable for the technical field of data processing, and provides an anti-money laundering detection method based on a graph neural network, which comprises the following steps: constructing bank transfer transaction data into a topological graph, taking an account as a node, taking a transaction as an edge, and realizing the detection of a money laundering behavior by using a feature enhanced graph attention network (FEGAT). According to the FEGAT provided by the invention, multi-dimensional features of accounts and transactions are aggregated at the same time, and key transaction paths are dynamically captured in combination with an attention mechanism, so that the recognition capability of a model on a complex money laundering mode is remarkably enhanced, and missing detection and misjudgment are reduced; a multi-time slice input and feature accumulation mechanism can learn crime committing skills of criminals in a long term, and can identify hidden money laundering behaviors across time periods; the network characterization capability is enhanced through edge feature splitting and a multi-dimensional splicing technology; the two-layer network architecture gives consideration to feature expression and calculation efficiency; carrying out normalization processing to eliminate data scale difference; the method supports account and transaction feature joint modeling, and is suitable for money laundering detection requirements in diversified financial scenes.