258 results about "Network analysis" patented technology

The invention discloses an intelligent early warning method for pipeline blockage of a slurry circulation system of a slurry shield, which relates to the field of intelligent early warning, and comprises the following steps of: performing spatial-temporal feature analysis on a standardized multi-dimensional data stream, constructing a blockage feature knowledge graph based on pipeline position and time sequence correlation analysis, and generating a blockage feature vector through a graph neural network; based on the blockage feature vector, analyzing the dynamic change trend of particle distribution through a long-short-term memory network and predicting the particle blockage risk in combination with an acoustic signal, then performing adaptive judgment by fusing geological conditions and construction stage information to obtain a risk assessment result, and inputting the risk assessment result and the blockage feature vector into digital twinborn simulation to obtain the particle blockage risk. A blockage scene is predicted based on fluid dynamics and a particle sedimentation model, early warning parameters are adjusted through Bayesian optimization, and graded early warning signals are generated; according to the invention, by generating the blockage feature vector, the recognition capability of the early local abnormal propagation trend is enhanced, and a reliable basis is provided for accurately predicting the blockage risk.

The invention discloses a city management AI dispatch algorithm and system based on historical mining and responsibility matching, and the method comprises the steps: building and dynamically updating a city management element evolution graph through obtaining the multi-mode description information of a city management case and the real-time state data of disposal resources; calculating potential disposal effects of different candidate dispatching schemes by using a causal inference engine, and generating a comprehensive efficiency estimation vector; on the basis, a multi-target reinforcement learning strategy is adopted to generate an optimal dispatch instruction, and system parameters are continuously optimized through online element learning during execution; cooperative processing network analysis is activated for sudden complex events, and responsibility atlas reconstruction is triggered when the matching efficiency is low. According to the method, the accuracy and efficiency of case disposal are remarkably improved, disposal timeliness optimization, resource load balancing and improvement of the first solution rate are realized, and meanwhile, the adaptive capacity and continuous optimization efficiency of the system to complex scenes are enhanced.

The invention relates to the technical field of elevator safety monitoring, and discloses an elevator guide rail abrasion online monitoring and deep learning early warning system. The system comprises an active excitation multi-mode sensing end and a causal inference residual network analysis engine, and the analysis engine compares multi-physical field response data collected in real time with a theoretical health response signal under a current working condition based on a health response baseline model trained under a health state; the method comprises the steps that firstly, a multi-dimensional residual signal capable of separating working condition interference is generated, then, a system executes online self-calibration of a cross-modal sensor through physical constraints contained in a model, the effectiveness of the signal is judged, finally, the effective residual signal is input into a causal inference network, and the specific reason of guide rail abrasion is recognized and traced. The technical problem that the monitoring result is unreliable due to working condition interference, unknown abrasion reasons and sensor faults is solved, and high-precision, traceable and high-reliability online monitoring and early warning of elevator guide rail abrasion are achieved.

The invention discloses a top coal fracture network reconstruction method based on three-dimensional modeling, and belongs to the field of mining informationization, and the method comprises the steps: collecting top coal fracture initial data through on-site survey and a sensor, analyzing the morphological change characteristics of the top coal fracture initial data, and obtaining a narrowing distribution model of fractures from an opening to a deep part; geological condition parameters are obtained based on the narrowing distribution model to determine the non-uniformity degree; a three-dimensional dynamic index is extracted from the non-uniformity degree, a preliminary space frame is constructed, and the expansion state of the fracture in the depth direction is obtained; fusing the expansion state of the fracture in the depth direction with the geological condition parameters, performing fracture behavior simulation, and determining a dynamic adjustment scheme of the spatial network; obtaining an optimized three-dimensional model based on the dynamic adjustment scheme; and a fracture behavior prediction value is extracted from the optimized three-dimensional model, and a final top coal fracture network reconstruction result is obtained. According to the method, the accuracy and reliability of fracture network analysis in a complex geological condition environment are effectively improved.

The invention belongs to the field of transformer pile head recognition, and particularly relates to a distribution transformer pile head automatic recognition system which comprises a data integration module, a data processing module, a system control module, a waveform parameter calculation module, an excitation circuit analysis module, an equivalent inductance conversion module and a looseness recognition algorithm module. According to the scheme, the high-cost limitation of a traditional vibration sensor or 3D modeling is broken through, and remote monitoring of looseness of the transformer pile head can be achieved through equivalent excitation inductance conversion and waveform similarity analysis without additional hardware transformation based on voltage and current data collected by an existing digital platform (a resource middle platform, a marketing middle platform and the like) of a power grid. Compared with the traditional scheme in the industry, the method has the advantages that the hardware transformation cost is reduced by more than 70%, the problem of missing inspection of manual inspection is avoided, the average recognition time of the pile head loosening fault is shortened to be within 15 minutes from 4 hours of traditional manual inspection, the monitoring efficiency is remarkably improved, and non-intrusive and low-cost accurate sensing of the state of the transformer is realized.

The invention relates to an earth pressure balance shield construction safety toughness dynamic evaluation system and method based on an extended cloud model and a network analysis method, and the system comprises an index system construction module which builds a multi-level toughness evaluation system based on a literature measurement and factor analysis method; an entropy weight TOPSIS weight calculation module objectively calculates the initial weight of the index through an information entropy theory; the ANP network weight optimization module corrects and optimizes the global weights of the indexes by constructing an inter-index nonlinear dependency network and a feedback mechanism; and the extended cloud toughness evaluation module quantifies qualitative indexes into membership degrees for different toughness levels based on expectation, entropy, hyper-entropy and other digital characteristics, comprehensively integrates weights and the membership degrees, and finally outputs soil pressure balance shield construction safety toughness levels and targeted optimization strategies. According to the method, multi-dimensional toughness quantitative evaluation and dynamic prevention and control of the construction safety risk of the earth pressure balance shield (EPB) can be realized under the complex stratum condition.

The invention relates to the field of life insurance management, and discloses a life insurance risk management system based on an AI large model, which is used for constructing an intelligent, dynamic and closed-loop optimized life insurance anti-money laundering risk management framework. Comprising the following steps: automatically analyzing an external unstructured supervision text by utilizing a large language model, and generating a structured rule set which can be dynamically updated; jointly inputting the rule set and real-time transaction data into a network analysis model based on multi-body system dynamics, and constructing a transaction network situation map to identify abnormal cooperative behaviors; a suspicious transaction report draft meeting supervision requirements is automatically generated by a large language model based on the graph; carrying out cross validation and confidence rating by fusing the offline data extracted by the OCR technology; and finally generating a standard report file and submitting the standard report file to a bank system. According to the invention, automation and intelligentization of the whole process from rule interpretation, risk identification, report generation to supervision report are realized, and the anti-money laundering risk management efficiency of the life insurance industry is improved.

The invention discloses a prognosis evaluation method and system for diffuse large B-cell lymphoma. According to the method, firstly, a gene module closely related to lipid metabolism is screened from DLBCL transcriptome data through weighted gene co-expression network analysis (WGCNA), and then eight key prognosis genes including FNDC1, IL22RA2, C15orf48, OMD, MFAP2, BC017398, CXCL6 and TNFAIP6 are identified from the module by adopting multi-step regression analysis (single factor Cox, LASSO and multi-factor Cox). A risk scoring model is constructed based on the expression levels and regression coefficients of the genes, and DLBCL patients can be divided into a high-risk group and a low-risk group with significant survival differences. The risk score and the clinical pathological factors are further integrated to construct a column graph, and individualized survival probability prediction can be achieved. The invention further provides a corresponding prognosis evaluation system, electronic equipment and a storage medium. An independent data set verifies that the prognosis model has excellent prediction performance and clinical practical value.

The invention relates to the field of financial risk protection, in particular to a financial risk protection method and system based on data mining, and the method comprises the following steps: collecting enterprise multi-dimensional financial logs, carrying out the deep mining of transaction behaviors, and constructing an enterprise financial behavior map; performing multi-signal correlation degree calculation and liability structure change perception based on the enterprise financial behavior map, and marking potential fund chain vulnerabilities; upstream and downstream supply chain information of an enterprise is identified, supply chain ecological topology network analysis and risk effect calculation are carried out, and a key risk critical link is identified; carrying out multi-scene financial risk simulation on the potential fund chain vulnerability points and the critical links of the key risks, and constructing a financial risk simulation sand table; and carrying out impact bearing capability evaluation based on the financial risk simulation sand table. According to the method, the potential financial risk of the enterprise is accurately identified in advance, a protection decision is made in a targeted manner, the risk protection capability of the enterprise is improved, and enterprise assets are effectively protected.

The invention discloses a dental plaque visual detection system based on micro-ecological recognition and trend prediction. The dental plaque visual detection system comprises a data acquisition module, a prediction modeling module, a network analysis module and a risk quantification module. According to the system, a miniaturized oral cavity sensing array and a high-throughput micro-fluidic chip are used for collecting the relative abundance of dental plaque flora and dynamic characteristics such as oral cavity acidification, buffering and remineralization, and future flora data are generated through a depth time sequence prediction model. A flora interaction network is constructed by combining spatial co-localization and causal inference, kinetic parameters such as node centrality, edge weight and promotion and suppression polarity are extracted by using a graph neural network, components such as pathogen expansion amount, antagonism decline amount, network modulation amount and protection amount are calculated, and the components are fused into a dynamic pathogenic risk index (D-PRI). The method has the beneficial effects that the index can be visually presented on a time axis, personalized intervention suggestions are generated, and quantitative prediction and accurate early warning of the dental plaque risk are realized.

Aspects of the subject disclosure may include, for example, obtaining building information indicative of physical characteristics of a building; obtaining first network status information indicative of first wireless network capabilities provided by first equipment inside the building; obtaining second network status information indicative of second wireless network capabilities provided by second equipment outside the building; obtaining user demand information indicative of user demand for wireless communication services within the building; providing the building information, the first network status information, the second network status information, and the user demand information to a machine learning (ML) mechanism in order to facilitate generation by the ML mechanism of an output; responsive to the providing, receiving from the ML mechanism the output; and presenting the output in visual form, in audio form, as data, as a graph, as a chart, as a table, or any combination thereof. Other embodiments are disclosed.

The invention discloses an urban public facility layout optimization method and system based on multi-source space-time big data, and relates to the technical field of urban planning. Comprising the following steps: firstly, acquiring and fusing multi-source space-time big data such as mobile equipment signaling data and point-of-interest data; identifying and delimiting an urban public service three-level center system based on the fusion data through a spatial clustering and network analysis algorithm, and determining each center level and a spatial influence range; constructing a differentiated service demand quantification model for each level of service center; constructing a multi-stage collaborative layout optimization model, and completing facility hierarchical assignment and site selection optimization; evaluating the layout scheme in the simulation environment and outputting a grading evaluation report; through dynamic analysis and multi-level collaborative optimization driven by multi-source space-time big data, accurate matching of public facility layout and service requirements is achieved, and meanwhile the service coverage rate and cross-level connection efficiency are improved.

The invention, which relates to the technical field of phased-array antenna testing, discloses an automatic phased-array testing system comprising a vector network analysis module, an antenna horn and servo control module, a phased-array antenna module, a testing tool module and an upper computer control module. The invention also discloses a phased array automatic test method. The method comprises the following steps: preparing before testing; configuring test parameters; executing an automatic test; processing and storing test data; and finishing the test and resetting the equipment. Full-process automatic testing is achieved through the upper computer automation unit, traditional testing time is shortened, large-scale phased array batch testing is adapted, personal errors are eliminated through the testing tool, the vector network module and the servo module, data credibility is improved, operation is simplified, data are automatically stored, a report is generated, modular design is adapted to multiple types of phased arrays, and testing efficiency is improved. And the cost is reduced, the application is wide, and better use prospects are brought.

The invention discloses a cell interaction identification method based on cell space transcriptome data, which comprises the following steps: constructing cell pairs according to cell space proximity, and calculating an interaction signal matrix and space information of each pair of cells by combining a ligand-receptor database; then generating cell pair embedding representation by using a graph neural network, calculating the similarity between cell pairs, and calculating the similarity between cells based on a gene expression matrix; updating the similarity matrix through iteration until convergence to obtain a final similarity matrix between cells and between cell pairs; and clustering the similarity matrix, and constructing a cell type and cell pair type model, thereby realizing accurate cell interaction identification. According to the method, space and expression information can be fully utilized, inter-cell heterogeneity and context specificity interaction can be captured, the analysis precision and biological authenticity of a cell communication network are improved, and an effective tool is provided for research on tissue development, disease mechanisms, immune response and the like.

The application discloses a kind of group level fMRI brain function network analysis methods based on graph convolutional neural network, comprising: obtaining the fMRI of the brain of multiple groups of different categories of subjects, after preprocessing, establish brain function network for each subject, carry out single sample t test to each group brain function network, calculate the graph theory attribute of node as node feature;Establish and train the classification model of GCN, the input of neural network is the edge of brain function network, node feature, and the output is the category of subject;According to the explainability of GCN, find the most important subgraph structure for classification, the subgraph represents the biggest brain function connection of the difference of brain function network between different groups, and can be used for further analysis on the neural mechanism of brain disease.The application can more comprehensively compare the brain function network of different groups, obtain more accurate results, and can be widely used in aphasia, depression, alzheimer's disease and other brain function network analysis of brain disease.

The invention discloses a space station on-orbit maintenance SA network analysis method based on DSA and a Bayesian network, and the method comprises the steps: firstly building a topological structure of an SA reliability network, then obtaining the structure parameters of the SA reliability network, carrying out the SA reliability analysis, building an applicable proposition network according to a given manned spaceflight extravehicular activity research object, and carrying out the analysis of the SA reliability. And finally, carrying out systematic and qualitative DSA analysis on the researched manned spaceflight extravehicular activity tasks based on the propositional network. The method can effectively provide support for personnel decision making of the task system, provide support for training design of task personnel, evaluate rationality of information interaction design of the current task system, identify key nodes of information interaction and provide support for information interaction design of the task system. The method is suitable for evaluation and optimization of EVA task system design of space stations in China and astronaut ground training system design.

The invention relates to the technical field of maintainability evaluation, in particular to an aircraft maintainability evaluation method and system based on maintenance index element coupling, and the method comprises the steps: constructing a maintainability evaluation index system comprising a plurality of indexes based on a domain standard and expert suggestions; based on the human-computer interaction model, interface interaction between people and software, hardware, environment and people in the maintenance process is analyzed, and maintainability design elements are recognized; based on a network analysis method, analyzing a coupling relationship between indexes and elements, constructing a complete network model, and determining the importance of each index and element; and through an automatic algorithm, importance weights of indexes and elements in the network model are calculated, and quantitative evaluation is realized.

This invention discloses a method for constructing a natural-artificial water network model and analyzing its connectivity, including: collecting basic data; generalizing nodes and edges of the natural and artificial water networks and adding weights to the edges; constructing a natural-artificial water network model based on complex networks; constructing the degree distribution of structure and flow based on node degrees; and calculating the structural connectivity and flow connectivity of the natural-artificial water network based on the degree distribution. This invention combines complex network theory to model natural-artificial water networks and analyze their connectivity, solving the technical problem of lacking comprehensive modeling and connectivity assessment in existing water network analysis methods, and providing new analytical ideas and technical support for water resource management and water network structure optimization.

The application provides a transient ground potential rise induced coupling determination method through cable shielding layer, and belongs to the technical field of cable shielding layer.The application establishes a distributed parameter partial element equivalent circuit model of a grounding network, solves transient ground potential distribution by using a finite difference time domain method, obtains equivalent transfer impedance frequency characteristics of a braided shielding layer by using a four-port S parameter vector network analysis method and a variational asymptotic homogenization multi-scale algorithm, solves core line common mode voltage and differential mode voltage by establishing a multi-conductor transmission line equation group, constructs an artificial intelligence prediction model based on a topology perception graph neural network, solves a large-scale sparse admittance matrix by using a multilevel fast multipole method to obtain full network induced voltage distribution, and establishes a quantitative relationship model between ground potential rise and core line induced voltage, so that the technical problems that cable core line induced voltage caused by transient ground potential rise is difficult to accurately predict and effectively suppress are solved.

The application discloses a circuit pre-training method and device based on a graph neural network and electronic equipment, comprising the following steps: for a large-scale circuit, a circuit network graph is constructed, a random walk road is generated, and the road is used as a sample for model training; a node2vec model is trained using the road sample; a model with the minimum loss function value is selected as an optimal model; and the optimal model is loaded, and representation vectors of all nodes are output and saved. In the application, the node2vec pre-training model of the graph neural network is used, the information of a long-distance standard unit is fully absorbed to absorb local circuit structure features, deep representation of nodes is automatically learned from the circuit structure, and tedious manual feature engineering is avoided, thereby providing high-quality representation vectors for a downstream model. The node representation learned by the node2vec model is a task-independent general feature, can be flexibly applied to various downstream tasks, and provides support for subsequent circuit analysis and optimization design work.

The present application relates to the technical field of network analysis, and further relates to a network resilience index multi-dimensional quantitative evaluation method, which comprises the following steps: step 1: obtaining original topology data of a target network, and constructing a topology base resilience index of the network under the condition of no external impact; step 2: calculating a crisis exposure factor; step 3: calculating the intermediate centrality of each node; calculating the structural hole limit system of each node; combining the intermediate centrality and the information redundancy degree to generate an adaptation-recovery index for measuring the self-healing and regenerative capacity of the network; step 4: correcting the topology base resilience index and the crisis exposure factor to resist impact, then amplifying the index weight according to the adaptation-recovery index, and combining the stability correction of network resilience fluctuation to finally generate a multi-dimensional network resilience comprehensive index. The present application is helpful to realize network vulnerability identification and resilience strategy optimization based on structural attributes and dynamic historical response.

The application discloses a kind of live content auditing method and system based on AI, belong to network live content supervision technical field.The method extracts the multi-dimensional characteristics of anchor by cognitive consistency analysis model, calculates the deviation degree of feature correlation matrix to detect cognitive dissonance;Adopt optical flow tracking and LSTM network analysis microexpression infers implicit intent;Monitoring semantic trajectory drift identifies sensitive content;Construct audience group dynamic graph to analyze emotional transmission intensity;Through bayesian network, from audience abnormal reaction, deduce illegal behavior;Evaluate cognitive load to identify information overload cover;Calculate node centrality to implement transmission blocking;Fusion multi-source information outputs rule determination.System includes cognitive feature analysis, microexpression identification, semantic trajectory monitoring, emotional transmission analysis, reverse reasoning, cognitive load calculation, transmission blocking and decision output eight modules, work cooperatively through event-driven mechanism.The application breaks through the surface limit of traditional audit, can identify cognitive fraud, psychological manipulation and other hidden violations, realizes active defense.

Vehicle license plate auditing is provided. Distance and time traveling information corresponding to a vehicle is extracted from a data network analysis graph model of spatiotemporal traffic flow data based on a license plate number of the vehicle in response to removing irrelevant nodes from the data network analysis graph model. A distance and time mutually exclusive node relationship is determined within the data network analysis graph model of spatiotemporal traffic flow data based on the distance and time traveling information corresponding to the vehicle extracted from the data network analysis graph model of spatiotemporal traffic flow data. An anomalous license plate node is determined in the data network analysis graph model of spatiotemporal traffic flow data based on the distance and time mutually exclusive node relationship. A set of action steps is performed regarding an anomalous license plate number corresponding to the anomalous license plate node.

The present application relates to the technical field of biological information data processing, in particular to a kind of arthritis data processing method and system based on ceRNA-network analysis, comprising the following steps: obtaining arthritis sample RNA data and target annotation, extract shared element screening regulation pair, by pathological pathway enrichment and function verification, measure correlation to determine priority, integrate ceRNA network structure to evaluate regulatory influence, parse node role to adjust centrality, generate specific core hub node identification atlas.In the present application, by introducing dynamic transcription consistency pattern recognition and transcription correlation and target co-annotation relationship joint comparison, the accuracy of arthritis data analysis is improved, the evaluation of candidate regulatory pair is strengthened, the result reliability and regulatory confidence level are improved, combined with ceRNA network node regulatory influence evaluation, the identification ability of key node is enhanced, effectively solve the blind spot of traditional method, ensure the accurate identification of arthritis specific core hub node.

The invention discloses a positioning and delimiting method and system for a cortex pathological focus based on an intracranial electroencephalogram signal. The method comprises the following steps: constructing an individualized three-dimensional navigation model; obtaining an original intracranial electroencephalogram signal; generating a plurality of representative segments according to the original intracranial electroencephalogram signals; analyzing the original intracranial electroencephalogram signal and the plurality of representative segments so as to obtain a network analysis core index; generating a fusion type three-dimensional brain model according to the network analysis core index and the individualized three-dimensional navigation model; generating a space-time three-dimensional electroencephalogram model and an electrophysiology functional area distribution diagram according to the fused three-dimensional brain model and the network analysis core indexes; and according to the space-time three-dimensional electroencephalogram model and the electrophysiology functional area distribution map, generating accurate electrophysiology positioning and delimiting information of the cortical pathological focus. According to the invention, accurate electrophysiological delimitation of intra-operative cortical pathological lesions (glioma, hemangioma, focal cortical dysplasia and the like) is realized.

The invention discloses a material fire hazard evaluation method based on Nash equilibrium fusion with multi-dimensional weight. The method comprises the following steps: screening and testing fire hazard index data; principal component analysis is carried out on the acquired data, a hierarchical evaluation system is constructed, and corresponding weights are calculated; thirdly, constructing an evaluation network based on a hierarchical network analysis method, and calculating a subjective weight based on an index mutual influence relationship; then, calculating an index weight based on an information entropy theory; and finally, based on the Nash equilibrium theory of the game theory, integrating the three weights to obtain a comprehensive weight, and based on the comprehensive weight, carrying out fire hazard quantitative evaluation on the material. Through the multi-dimensional weight fusion method, the limitation of a single evaluation method is overcome, the complexity of fire hazard of the flame-retardant material can be comprehensively and objectively reflected, and the method is particularly suitable for evaluating the comprehensive fire risk change of the material in multiple aspects such as heat, smoke and toxicity after the flame retardant is introduced; and a scientific basis is provided for rational design and safety evaluation of the flame-retardant material.

We conduct analysis on specific communication networks and create detailed advice. [Solution] One aspect of the present invention is a network analysis system comprising: an input unit for inputting performance data of a target network device; a processing unit for generating processed data by processing the performance data based on the performance data and a threshold; an analysis unit for inputting the processed data to a first machine learning model trained on a training dataset including network configuration data of the network device, processed data, and analysis results showing past trends and future trends, and acquiring analysis results output from the first machine learning model; and a generation unit for inputting the analysis results acquired by the analysis unit to a second machine learning model trained on training data including analysis results and information indicating countermeasures, and generating advice information based on countermeasures information output from the second machine learning model.

The invention belongs to the technical field of network analysis, and particularly relates to a crowd-sourcing high-order network critical threshold prediction method and device based on a graph neural network. The method comprises the following steps: integrating local structure features of nodes, position codes of positions of the nodes in a whole graph and parameters influencing the global state of a system, and providing comprehensive and three-dimensional cognition on the network for a model; in a network information processing level, a local and global mixed GNN architecture is adopted, and a GIN module and a global self-attention module are arranged in parallel: the former is used for accurately capturing a local neighborhood structure of a node, and the latter is used for capturing a long-distance dependency relationship in a whole graph range; by fusing the information of the two dimensions, the model can understand how the local structure affects the global dynamics more deeply. The problems that the prior art depends on dynamic evolution data, and critical threshold prediction cannot be accurately carried out on the high-order network are solved.

This application relates to the field of relational network analysis in artificial intelligence, specifically to a user differentiation method, apparatus, device, and medium based on content and network features, comprising: acquiring a social network graph; acquiring node features of each node based on the social network graph; inputting the node features into a Markov random field model to obtain a first classification result of the node; extracting content features from the content information; inputting the content features into a trained content classification model to obtain a second classification result of the content features; and determining the user type of the node based on the first classification result and the second classification result. This application combines the different characteristics of content features and network features, utilizing more comprehensive user information to detect whether users on social network platforms are spam accounts, making it less likely for malicious users to bypass the detection.

Techniques for on-demand transmission of analyte data are disclosed. In certain embodiments, a system for on-demand transmission of analyte data includes an analyte sensor, at least one input sensor, and analyte sensor electronics circuitry. The analyte sensor electronics circuitry includes a connectivity interface. The analyte sensor electronics circuitry is configured to process touch input data received via the at least one input sensor to identify a first touch input pattern, and transition the connectivity interface from a low power state to an operational state based on identifying the first touch input pattern. The analyte sensor electronics circuitry is further configured to transmit the first analyte data to the display device upon transitioning the connectivity interface to the operating state, and transition the connectivity interface from the operating state to the low power state after transmitting the first analyte data to the display device.