395 results about "Network analyser" 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 digital twin intelligent test run system based on a marine medium-speed diesel engine and a monitoring method, relates to the technical field of monitoring, and is used for solving the problems of degeneration identification lag and low emission early warning precision of an oil injector. The system comprises a multi-source heterogeneous data preprocessing module and a running state intelligent diagnosis module. The multi-dimensional data acquisition module is used for acquiring in-cylinder pressure, crankshaft torsional vibration and transient air-fuel ratio signals to construct multi-dimensional data vectors, and the multi-dimensional data acquisition module is used for analyzing combustion fluctuation characteristics based on an attention mechanism LSTM (Long Short Term Memory) network, identifying early deterioration of an oil injector and outputting an oil injection consistency coefficient and a fault type identifier. The method comprises the steps that according to an oil injection consistency coefficient, electromagnetic valve response delay and combustion parameter correlation are tracked, a degradation trend is predicted, and emission early warning is generated; and according to prediction and early warning results, fuel injection compensation parameters are dynamically adjusted in the digital twinborn model, a recursive least square method is adopted to identify combustion parameters and feed the combustion parameters back to an electric control unit, and self-adaptive optimization and closed-loop control in the test run stage are achieved.

The invention discloses an electrical safety test data analysis method and system for an electrical cabinet, and relates to the technical field of electrical measurement and test. The method is used for solving the problems of single test dimension, poor anti-interference capability and inaccurate fault positioning in the safety test of the electrical cabinet. Firstly, multi-channel electrical parameter synchronous acquisition is triggered based on a working condition event, real discharge and interference signals are distinguished through time-frequency analysis, and a time sequence incidence matrix of discharge pulses and load currents is established; then, analyzing phase distribution characteristics of partial discharge, calculating a correlation coefficient between a discharge repetition rate and a load current, and dynamically adjusting a weight to generate an insulation state comprehensive test index; further, insulation degradation characteristics are extracted through signal decomposition, and an insulation degradation trend and a fault probability are predicted in combination with energy entropy analysis and discharge mode recognition; and finally, the position of a fault component is analyzed and positioned based on the impedance network, and a maintenance priority sequence is generated according to the fault probability and the degradation rate.

The invention relates to the technical field of environmental monitoring of artificial intelligence, and particularly discloses a dangerous waste storage environment real-time monitoring and risk early warning system based on digital twinning, which collects multi-modal environmental data in real time through an intelligent sensor network deployed in a dangerous waste storage facility, and constructs a digital twinning model synchronized with a physical entity; a multi-modal data fusion and physical embedding technology is adopted, and virtual risk parameters of an area which is not actually measured are calculated based on an environment evolution rule; an abnormal association mode between the measured data and the virtual risk parameters is analyzed and identified through the dynamic association network, and risk level judgment and early warning signal triggering are achieved; a sensor monitoring strategy is dynamically adjusted according to an early warning result to form closed-loop management and control; according to the invention, the sensor limitation of the traditional monitoring system is broken through, the advanced accurate early warning of the hidden risk is realized, and the monitoring efficiency is obviously improved through adaptive resource configuration.

The invention discloses a computer network security protection method and system based on deep learning, and the method comprises the steps: inputting standard multi-modal data into a multi-channel collaborative deep detection model, and extracting a spatial local feature vector in network flow through a TCN time convolution network; analyzing a long-range time sequence dependency relationship in a user behavior sequence through a network combining a GRU gating loop unit and a self-attention mechanism, performing deep semantic analysis by adopting a pre-trained DeBERTa large language model, and extracting an abnormal semantic feature vector to obtain a multi-modal feature vector; fusing the multi-modal feature vectors through cross-modal attention fusion, inputting the fused multi-modal feature vectors into a full-connection network for fusion analysis, and outputting a threat scoring index; and generating a computer network security protection strategy according to the threat scoring index based on an Apriori association algorithm, and executing according to the protection strategy. And efficient and rapid guarantee is provided for network security.

The invention discloses a coarse and fine particle inclusion photoelastic sample preparation and contact force chain network analysis method, and relates to the field of particle material mechanical tests, and the method comprises the following steps: calculating target void ratios under different coarse particle contents through a discrete element method; generating a corresponding numerical calculation sample according to the target void ratio and the preset coarse grain content, and exporting related parameters to prepare a coarse and fine grain inclusion photoelastic sample with the same initial compactness; in combination with non-polarization and polarized photoelastic images of the photoelastic sample, extracting intergranular contact force and constructing a contact force network; based on the contact force network, a strong contact force network is extracted and microscomic analysis is carried out, and force transmission characteristic comparison results under different coarse grain content conditions are obtained. According to the method, preparation efficiency of samples with different coarse grain contents and controllability of an initial state are realized, an influence rule of the coarse grain contents on a force chain structure and force transmission characteristics thereof is disclosed based on analysis of a contact force chain network, and the method is of great significance in researching a force transmission mechanism of a coarse and fine grain inclusion system and microstructure characteristics thereof.

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 discloses an fNIRS adaptive feedback emotion cognition cooperative training device and method, and relates to the technical field of emotion disorder cognition training. The device comprises a data acquisition module used for acquiring an original dual-wavelength light intensity signal from a brain; the data processing module is used for performing multi-stage preprocessing on the original dual-wavelength light intensity signal; the neural feedback module is used for calculating a multi-dimensional evaluation index according to the hemoglobin concentration data and adjusting a difficulty level and a feedback threshold value of a training task by using a dynamic threshold value control algorithm; the training module is used for training participants based on the emotion stimulation task and the cognitive training task; and the evaluation module is used for performing training evaluation according to the comparison result of the comprehensive performance score and the feedback threshold. Brain function equipment is closely combined with cognitive training and emotion stimulation tasks, dynamic evaluation and real-time feedback are added for traditional tasks, and through the brain network analysis technology, accurate evaluation of the user training effect is achieved.

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 provides a power distribution network topological structure automatic identification and optimization reconstruction method and system, and relates to the technical field of power systems, and the method comprises the steps: obtaining power distribution network information, and generating an initial topological graph; setting heterogeneous sensing nodes to form a sensing domain, and identifying a topological association relationship by an edge computing unit; generating a global topological feature matrix; dynamic response characteristics and load characteristics are extracted, and association strength is calculated; and establishing a Bayesian network analysis causal relationship, calculating an intervention effect value and generating a candidate reconstruction scheme. Accurate identification and intelligent reconstruction of the topological structure of the power distribution network are realized, and the reliability and the operation efficiency of the system are improved.

The invention belongs to the technical field related to data processing, and provides a brain network analysis method and system based on multi-network collaborative topology analysis in order to solve the problem that an analysis result is unstable due to threshold selection subjectivity in existing Alzheimer disease brain network analysis. Extracting brain regions corresponding to the default mode network, the significance network and the execution control network, calculating correlation coefficients among different brain regions to construct a function connection matrix, and converting the function connection matrix into a distance matrix meeting complex construction requirements; constructing a Vietories-Rips complex based on the distance matrix, calculating a coherence group under each scale of the filtering sequence, and extracting topological features of a 0-dimensional Betti number and a 1-dimensional Betti number; according to the method, the Betti number is extracted, a curve that the Betti number changes along with the threshold value is drawn, the difference of the two groups in topological characteristics is compared, the specific topological biomarker related to the Alzheimer's disease is identified, and richer biomarker information is provided for early diagnosis of the Alzheimer's disease.

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 an intelligent lighting control method and system based on the Internet of Things, and the method comprises the steps: deploying a lightweight edge computing node at an Internet of Things terminal, and carrying out the real-time collection to obtain preprocessing data; transmitting the preprocessed data to a cloud platform, analyzing a user historical behavior sequence based on an LSTM (Long Short-Term Memory) network, modeling a spatial dependency relationship between environment parameters and user behaviors through a GCN graph convolutional network, and constructing a hybrid model; dynamically adjusting parameters of the hybrid model through a dual time difference optimization algorithm in combination with a multi-target reward function to obtain a target hybrid model; and inputting the preprocessed data into a target hybrid model for training, outputting a control strategy, and transmitting the control strategy to an Internet of Things terminal for real-time control. On-demand lighting can be achieved, energy waste of public areas and unmanned areas is avoided, and the energy-saving effect 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 land reclamation engineering, in particular to a land reclamation engineering progress monitoring system based on time sequence image analysis, which comprises a data storage module, an engineering progress image recognition engine, an engineering quality detection module, a meteorological condition compensation module and an early warning module. A Riemannian geometric model of a terrain curved surface is constructed, a project progress is analyzed and quantified through a geodesic network, a project progress image recognition engine comprises an image registration module, a Riemannian geometric terrain modeling module and a geodesic analysis module, and the Riemannian geometric terrain modeling module constructs a terrain surface model through manifold parameterization and measurement tensor calculation. The geodesic analysis module quantifies engineering progress indexes such as the earthwork completion rate, the road hardening rate and the greening coverage rate based on geodesic network changes, the system further integrates quality evaluation functions such as slope stability detection and drainage system integrity detection, and the reliability of a monitoring result is improved through meteorological condition compensation.

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 relates to the technical field of Internet of Vehicles information security, and discloses an Internet of Vehicles-oriented Beidou signal spoofing detection and mitigation intelligent system and method, and the system comprises a multi-dimensional data sensing and preprocessing module, a deep learning joint detection module, and a security mitigation and reputation management module. The method comprises the following steps: firstly, extracting a physical layer signal feature and a network flow stability feature and generating a multi-dimensional feature vector; secondly, performing feature compression and preliminary screening by using an auto-encoder, analyzing a time sequence evolution rule through a long-short-term memory network, and outputting a judgment probability; and finally, executing hierarchical defense according to the judgment probability, responding to local threats by dynamically adjusting a measurement noise covariance matrix or a hard isolation strategy, evaluating network node reputation based on a path damage index, and adding abnormal nodes into a dynamic blacklist. Through the multi-dimensional feature fusion and deep learning cascade architecture, the spoofing attack detection accuracy is improved, and the diffusion of false information in the Internet of Vehicles is effectively blocked.

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 relates to the technical field of mechanical equipment state monitoring, and discloses a multi-parameter monitoring method and device suitable for the abnormal state of mechanical equipment and a medium. The invention aims to solve the problems of high false alarm rate, difficulty in early abnormality recognition and difficulty in quantitative prediction of residual life caused by single monitoring parameter and poor static threshold adaptability under variable working conditions in the prior art. According to the invention, vibration, temperature and working condition data are synchronously obtained; performing layer-by-layer extraction and information fusion by using a depth auto-encoder to generate a fusion feature vector; constructing a dynamic health baseline associated with the working condition, and quantifying the health index by calculating the deviation degree of the feature vector and the baseline; and analyzing a health index evolution rule based on a long short-term memory network, and deducing a degradation track to predict the remaining service life. According to the invention, deep fusion of multi-source information and self-adaptive monitoring of working conditions are realized, the accuracy of early fault early warning is improved, and a quantitative service life prediction basis is provided.

The invention discloses an adversity high-photosynthetic-efficiency transcription factor screening method based on deep learning, and relates to the technical field of biological information analys.The method comprises the steps that rice multi-modal stress response data is obtained and preprocessed, and preprocessed gene expression data is obtained; carrying out differential expression gene screening and co-expression network analysis on the preprocessed gene expression data, extracting multi-modal features, and fusing the multi-modal features to generate a multi-modal input feature matrix; constructing a double-layer deep learning model, training the double-layer deep learning model by using the multi-modal input feature matrix, and respectively outputting a regulation and control relationship matrix of transcription factors and target genes and a regulation and control relationship matrix of transcription factors and target pathways; and according to an output result, calculating a comprehensive score of each transcription factor through a multi-dimensional scoring system, and screening out the stress high-photosynthetic-efficiency transcription factor according to a predetermined screening standard.

The invention relates to the technical field of gas detection, and discloses a methane gas leakage detection method based on multi-sensor data fusion. According to the method, an absorption intensity sequence and a wavelength drift distance are obtained through a plurality of tunable semiconductor laser absorption spectrum sensors. The method comprises the following steps: constructing a dynamic characteristic association network taking sensor positions as nodes, taking sensor data as node initial states, comparing adjacent node state vectors in a cross manner along a preset path, and calculating a similarity matrix of absorption spectral line characteristics; a sensor subset with a cooperative change pattern is identified, and a background absorption component and a potential leakage signal component are separated from the sensor subset. Performing space-time alignment and integration on the leakage signal to generate a regional fusion concentration distribution diagram, further calculating the methane concentration gradient and change rate, and judging leakage when the methane concentration gradient and change rate exceed a preset threshold value. According to the method, through dynamic network analysis and cooperative signal extraction, the accuracy and anti-interference capability of leakage detection in a complex environment are improved.

The invention relates to the technical field of photovoltaic power station digital twinning modeling, and discloses a photovoltaic power station digital twinning modeling method and system, and the method comprises the steps: obtaining the monitoring data of each node of a distributed photovoltaic power station, and generating a temperature-power feature vector; analyzing the temperature gradient influence by using a graph convolutional network, and generating a node specificity temperature compensation matrix; calculating a deviation coefficient based on a standard temperature reference, and generating a temperature normalization mapping function; correcting model parameters by using an environment normalization method, and generating a model update vector after environment correction; and performing federal aggregation on the model update vector after environment correction, and outputting a global digital twinborn model. According to the method, high-precision distributed photovoltaic power station digital twinborn modeling under the environment heterogeneous condition is realized, and the accuracy and robustness of cross-climate region data integration are improved.

The invention provides a brain function network causal analysis method based on phase-space reconstruction and unified GCA, and relates to the field of functional brain network analys.The method comprises the steps that fMRI data are collected and preprocessed, and a time sequence of interested nodes is extracted from the preprocessed fMRI data; for extracting time sequences X and Y of any two to-be-analyzed interested nodes, constructing a variable time delay unified Granger causal model based on phase space reconstruction; and traversing all to-be-analyzed node pairs of interest, calculating the causal direction and strength between each pair of nodes to construct a whole-brain directed causal connection matrix, and performing network metric attribute analysis. According to the method, phase-space reconstruction is taken as a core, a causal analysis framework is provided by unifying GCA, and end-to-end modeling is realized. The final target is to generate a high-fidelity fMRI data model, so that the causal connection relationship is closer to a brain real neural mechanism, and the reliability and the application value of functional brain network research are improved.

The invention discloses an attention reminding algorithm for intelligent driving based on multiple modes, and relates to the technical field of intelligent driving and active safety, and the algorithm comprises the steps: firstly, collecting data in real time through a multi-mode sensor group, carrying out the alignment, and then calculating the quantization features, such as the fixation deviation degree, the steering wheel disturbance entropy and the heart rate variability; then, mapping scores by adopting an S-type function, analyzing a historical sequence by utilizing a long short-term memory network to generate a dynamic weight, and performing adjustment and weighted calculation on a single-mode score in combination with an environment complexity weight to obtain a comprehensive attention score; and finally, according to the continuous driving duration, calculating a linear decreasing dynamic reminding threshold value, executing dual logic judgment in combination with the environmental risk level, and triggering graded feedback through sound and light or vibration. According to the method, the problems of poor adaptability and high false alarm rate of traditional single-mode monitoring in a dynamic environment are effectively solved, the robustness of the system is improved, and adaptive evaluation and accurate early warning of driver distraction behaviors in a complex scene are realized.

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 provides an intelligent monitoring method and system for an electronic detonator continuous production line. The method comprises the following steps: performing continuous temperature field scanning on the surface of the injection mold to obtain gradient change data; driving a temperature response unit array configured by the mold to execute subarea phase change triggering, absorbing heat of a local overheating area and generating a phase change completion degree quantization parameter; analyzing and positioning a heat-flow density abnormal region by using a thermal resistance network, and performing spatial topological correlation processing to generate a heat-flow density deviation distribution diagram; executing multi-objective optimization on a cooling channel airflow controller to generate a self-adaptive airflow distribution instruction set; and path planning is conducted on the air cooling execution mechanism, and a mold temperature production line regulation and control parameter set is generated. According to the technical scheme provided by the invention, through three-stage cooperation of zoning phase change temperature control, explosive chamber safety association and multi-target airflow optimization, the mold temperature balance control is realized while the thermal shock risk of the detonator charging area is avoided.

Techniques are described for providing a threat analysis platform capable of automating actions performed to analyze security-related threats affecting IT environments. Users or applications can submit objects (e.g., URLs, files, etc.) for analysis by the threat analysis platform. Once submitted, the threat analysis platform routes the objects to dedicated engines that can perform static and dynamic analysis processes to determine a likelihood that an object is associated with malicious activity such as phishing attacks, malware, or other types of security threats. The automated actions performed by the threat analysis platform can include, for example, navigating to submitted URLs and recording activity related to accessing the corresponding resource, analyzing files and documents by extracting text and metadata, extracting and emulating execution of embedded macro source code, performing optical character recognition (OCR) and other types of image analysis, submitting objects to third-party security services for analysis, among many other possible actions.

The invention discloses a dynamic brain network analysis method guided by neural heterogeneity, and is suitable for the technical field of brain image processing and recognition. The method comprises the steps that functional magnetic resonance imaging data are acquired and preprocessed, an overlapped sliding window is used for dividing the functional magnetic resonance imaging data to construct a dynamic functional brain network, and then the dynamic functional brain network is decoupled into a topological consistency network and a time trend network which conform to brain activities; capturing space and time heterogeneity weights of different brain regions in the brain based on a topological consistency network and a time trend network, and identifying key nodes for driving brain network recombination; further weighting the topology consistency network and the time trend network to obtain a heterogeneity dynamic function brain network; propagation of neural information in a time dimension is simulated based on time propagation graph convolution operation, and spatial-temporal features of brain images are extracted from a heterogeneous dynamic function brain network; and finally, inputting the obtained spatial-temporal characteristics of the brain image into a multi-layer perceptron to predict the data category of the brain image to be recognized, and analyzing the influence of the brain disease image characteristics on the spatial-temporal heterogeneity of the brain region to complete the dynamic brain network analysis guided by the neural heterogeneity.