262 results about "Mathematical Graph" patented technology

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

The invention discloses an intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network, and belongs to the technical field of block chain security, and the system comprises a contract analysis module, a multilayer graph construction module, a heterogeneous graph neural network module, a vulnerability feature library, a vulnerability recognition engine, an automatic repair module and a visual interface. After the source code of the intelligent contract is input, code analysis and standardization are completed by a contract analysis module; the multi-layer graph construction module constructs a contract internal heterogeneous graph, an inter-contract interaction graph and an ecosystem relation graph based on a graph theory; the heterogeneous graph neural network module learns a vulnerability feature mode; the vulnerability recognition engine combines the vulnerability feature library to realize vulnerability classification and risk assessment; the automatic repairing module generates a repairing scheme; and the visual interface realizes detection progress monitoring, result display and encrypted report export. The intelligent contract vulnerability detection and restoration system based on the heterogeneous graph neural network provided by the invention provides technical support for block chain digital asset security and ecological stability.

The invention provides a zero-sum game-based distributed elastic consensus optimal control method under denial of service attack of a multi-agent system, and the method specifically comprises the steps: firstly, constructing a multi-agent formation model in which a leader and a plurality of followers cooperatively move through graph theory knowledge and a multi-agent second-order state equation; secondly, in order to reduce the influence of denial of service attack on communication topology, a time-varying weight distributed elastic observer is provided to estimate the state of a leader, and the attacked condition of the leader is considered; then, by constructing an augmentation system, a distributed consistency tracking problem with a leader is converted into a local tracking problem between each follower and a virtual leader thereof; and finally, in order to solve the zero-sum game problem, introducing a Hamiltonian-Jacobian-Ansaxophone equation to realize optimal control input under maximum external disturbance, and realizing algorithm design by using single-evaluation reinforcement learning with experience playback and combining a gradient descent method.

The invention relates to a river network grading system and method based on a graph theory and confluence cumulant, and belongs to the technical field of water conservancy projects and geographic information. Comprising the following steps: acquiring original DEM data and water system data, and converting the water system data to a preset projection coordinate system; extracting a primary river network based on the DEM data, and screening the primary river network in combination with the length of the river network and the area of the drainage basin to obtain a simplified river network; river network flow calculation: carrying out flow assignment on each river network element in the simplified river network based on the confluence cumulant; graph theory modeling: abstracting the simplified river network into a directed graph model; and river network grading: reverse tracing is carried out by taking the water outlet as a starting point, and the hierarchical relationship between the trunk and the branch is determined by combining the in-degree / out-degree and the edge weight of the node, so that river network grading is realized. According to the method, river network grading is upgraded from morphological description to hydrological function description, and the method is suitable for high-precision river network refined analysis, intelligent water conservancy emergency management and other scenes.

The invention provides an unmanned aerial vehicle-unmanned vehicle combined formation cooperative control method and system, and relates to the technical field of vehicle-vehicle cooperation, and the method comprises the steps: taking a virtual unmanned aerial vehicle as a leader, taking the unmanned aerial vehicle and the unmanned vehicle as followers in a unified manner, setting a fixed formation offset for each follower, and defining a communication topological relation based on a graph theory; the method comprises the following steps: acquiring an actual measurement state vector of a sensor in real time, processing a current estimation state through a constructed radial basis function neural network observer, outputting a disturbance estimation value in combination with a weight matrix, updating the estimation state according to a core equation, and judging whether to update the weight matrix or not according to an observation error; calculating the expected state of the follower according to the reference trajectory of the leader and the fixed formation offset, calculating the formation error, constructing an event trigger communication condition, judging whether the condition is met or not, enabling the follower to interact the state and the error according to the communication topology only when the condition is met, or else, continuing to use the previous trigger data; and finally, current control input of the follower is calculated through a distributed control law.

The invention provides a park integrated energy system low-carbon scheduling method combining demand response and carbon emission flow, and belongs to the technical field of energy system low-carbon scheduling. Establishing a directed weighted carbon flow network model based on a graph theory, tracking a carbon emission transmission path by adopting a maximum flow minimum cut theorem and a proportional allocation principle, constructing a space-time coupled dynamic carbon flow state space model, and performing state estimation by adopting a Kalman filtering algorithm; the carbon emission responsibilities are distributed based on a Shapley value method, a stepped carbon transaction cost function is established, an optimal scheduling strategy is solved through a double-layer iterative optimization framework, and the technical problem that the carbon emission responsibilities are difficult to distribute reasonably due to the fact that a park integrated energy system cannot accurately track a carbon emission transmission path when electric heat gas multi-energy flow coupling is considered is solved.

The invention provides a method for constructing an arch-shaped large-span shell steel roof, and belongs to the technical field of arch-shaped large-span shell steel roof construction. A temporary supporting system is built, and distributed optical fiber sensors are arranged to monitor the lifting process of a main arch; a cable force distribution scheme of a cable supporting system is calculated by adopting a double-layer game optimization model to realize cooperative control of stress uniformity and form precision, tensioning force is applied by using an intelligent tensioning system in stages, and a crane moving path is planned in combination with a path optimization algorithm based on a graph theory to realize graded unloading of a temporary supporting system. The technical problem that it is difficult to achieve structural stress uniform distribution and form accurate control of an arched large-span shell steel roof at the same time in the tensioning process of a cable supporting system is solved.

The invention discloses a cooperative control and disturbance suppression method for a multi-axis servo system, and relates to the technical field of cooperative and disturbance control of servo systems, position and speed signals of each servo axis are acquired in real time, a cooperative error vector is calculated based on a graph theory model, the vector reflects the deviation between the state of each axis and an expected cooperative trajectory, and the cooperative control and disturbance suppression of the multi-axis servo system are realized. And synchronous movement is ensured. The collaborative error is used for generating a preliminary control signal, and a proportional, differential and integral term combination strategy is adopted to quickly correct the error and eliminate the steady-state error. Meanwhile, disturbance vectors are estimated through an observer, and external interference or internal changes are compensated. And after the preliminary control signal and the disturbance compensation are added, amplitude limiting processing is carried out to prevent saturation of the driver. The control parameters are adjusted through an off-line optimization algorithm to minimize errors and disturbance influences, so that the tracking precision, the anti-interference capability and the adaptability of the multi-axis servo system are improved, and the method is suitable for the fields of high-precision manufacturing, robot control and the like.

The invention discloses a control valve drawing part intelligent identification system, and belongs to the technical field of intelligent manufacturing and drawing identification, the system obtains a direction control valve drawing sample, combines a QATM algorithm, a YOLO model, EAST and TesseractOCR, and U-Net and a graph theory algorithm to carry out multi-modal feature extraction and joint identification, constructs a three-level classification matching system, and outputs an initial identification result; through label data conversion, manual sampling auditing, initial recognition standard construction, extended label data generation and comprehensive precision label data fusion, multiple times of iterative optimization of an initial model is achieved, and finally a high-precision detection model is output. Automatic and intelligent recognition of part structures, parameters and topological relations in control valve drawings is achieved; the drawing recognition efficiency and accuracy are remarkably improved, and the method is suitable for industrial drawing digitization and intelligent assembly application.

The invention provides a power distribution network fault self-healing control method based on artificial intelligence, and relates to the field of power system intelligent control, in particular to a power distribution network fault self-healing technology, which comprises the steps of acquiring real-time operation state data of a power distribution network, extracting features to establish a fault prediction rule base, monitoring faults in real time and determining positions, and constructing a topological structure based on a graph theory algorithm. And calculating the load transfer capacity of the selectable reconstruction path, solving the multi-objective optimization function by adopting an improved particle swarm algorithm to obtain an optimal reconstruction scheme, generating a control instruction sequence to isolate a fault area, and recovering power supply of a non-fault area. The intelligent level of power distribution network fault processing is improved, the fault recovery time is shortened, and the power supply reliability is improved.

The invention relates to a GPU scheduling optimization method for task and node bidirectional modeling, which comprises the following steps: step 1, task modeling and classification, step 2, node feature modeling and scoring mechanism, step 3, priority scheduling of strong dependency tasks, and step 4, optimal matching scheduling of weak dependency tasks. And constructing an adaptive scoring matrix between the task and the node, and realizing optimal matching between the task and the node by means of a graph theory matching model. The method comprises the following steps: constructing a multi-dimensional feature modeling system, and abstractly expressing resource demand features of a computing task and computing power features of a computing node; and then, based on a multi-index comprehensive weighting mechanism, evaluating a matching relationship between the task and the node, fusing factors such as a task dependence structure and a task emergency degree, and executing fine distribution by adopting a hierarchical scheduling mechanism, so that efficient utilization of GPU resources and interpretable optimization of a scheduling result are realized.

The invention relates to the technical field of electronic design automation, and discloses a standard cell layout legalization method and system based on a graph theory constraint graph, and the method comprises the steps: building a bidirectional directed constraint graph based on an existing layout and a design rule, and building a directed edge between adjacent graph edges according to the design rule; traversing the constraint graph, detecting whether a directed edge which does not meet a constraint condition exists, and if so, identifying the directed edge as a constraint violation edge; for two end points of each constraint violation edge, searching a key path based on a depth-first search algorithm, and determining a maximum movable distance in combination with a greedy strategy; moving the endpoint to a legal position according to the critical path and the maximum movable distance, and updating the position of a related node in the constraint graph; and mapping the position changes of the nodes in the constraint graph back to the corresponding graph edges in the layout, and outputting the legalized layout, so that efficient and automatic repair of design rule violation is realized through the method, and the layout compliance rate and the area optimization effect are remarkably improved.

The invention relates to a motorcade consistency formation control method and device based on a dynamic event triggering mechanism, electronic equipment and a storage medium. The method comprises the steps of generating a communication topological graph of a multi-agent system composed of a preset number of unmanned vehicles according to the theoretical basis of a graph theory, and building a mathematical model of each unmanned vehicle; based on the mathematical model, designing a combined measurement function, a measurement error and a consistency controller of the multi-agent system; distributed event triggering conditions and self-triggering conditions are designed based on Lyapunov parameters; according to a dynamic event triggering condition and a self-triggering condition, a triggering time interval expression of the intelligent agent is obtained through calculation; by constructing a multi-agent system consistency controller and combining distributed event triggering conditions and self-triggering conditions, multi-vehicle formation consistency control is completed. According to the fleet consistency formation control method disclosed by the invention, accurate and efficient control of a fleet formed by multiple unmanned vehicles can be realized.

The invention discloses a power system carbon emission dynamic traceability and evaluation method, system and device based on life cycle and topological entropy fusion and a medium, and belongs to the technical field of power system low-carbon operation and carbon emission evaluation, and the method comprises the steps: collecting carbon emission data of different stages; establishing a stage set, calculating the emission of each stage, and solving a life cycle carbon entropy; abstracting a power system into a weighted directed graph, calculating a branch carbon flow rate based on the weighted directed graph, and calculating a graph theory carbon entropy based on node inflow; introducing a weight parameter, fusing the life cycle carbon entropy and the graph theory carbon entropy, and defining a fusion entropy; under a multi-period operation condition, calculating a time evolution sequence based on the fusion entropy, and defining a sensitivity coefficient; and generating a visual carbon flow map containing node carbon potential distribution, a branch carbon flow rate, a carbon entropy evolution curve and a responsibility allocation matrix. According to the method, dynamic conduction and node-level tracking of carbon emission along the electric power flow are realized, and the complexity and balance of carbon emission space distribution are quantified through an entropy theory.

The invention provides an intelligent proportion control method and system for an antistatic agent synthesis process, and the method comprises the steps: fuzzifying real-time parameters through real-time and historical process parameters by using an asymmetric membership function constructed based on data distribution skewness and kurtosis; historical data samples are mapped into graph theory nodes, communities are divided through a community discovery algorithm to generate fuzzy rules, initial weights are set, and an initial rule base is constructed; using a recursive least square method to identify rule consequent parameters, combining redundancy rules according to cosine similarity, and combining a particle swarm optimization algorithm to optimize antecedent parameters; and inputting the fuzzification real-time parameters into the optimized fuzzy neural network, calculating the activation intensity of the rule, adjusting the weighted average weight based on the information entropy of the current activation intensity, and obtaining the proportion control quantity of each component after defuzzification.

The invention provides a fault diagnosis method and system for a 500kV bus protection device, and the method comprises the following steps: obtaining the operation data of the 500kV bus protection device, and carrying out the preprocessing of the obtained operation data; according to the method, fault feature extraction is carried out by adopting the convolutional neural network in combination with an attention mechanism, fault information can be accurately captured, fault diagnosis and classification are carried out in combination with a support vector machine and a fuzzy logic theory, and the diagnosis accuracy of complex hidden faults is effectively improved; the fault location can be quickly and accurately determined through a fault location algorithm based on the graph theory, a fault isolation strategy is automatically generated, equipment action is controlled, fault expansion is avoided, and the reliability and stability of a power system are improved; by establishing the prediction model, the future operation state of the protection device can be predicted, early warning and prevention of faults are realized, a scientific and reasonable basis is provided for maintenance and repair of equipment, and the maintenance cost and power failure loss of the equipment are reduced.

The invention discloses a topology state monitoring-based high-voltage distribution network self-healing model construction method and system, equipment and a medium. The method comprises the steps of obtaining main network topology structure data, equipment operation state data and typical wiring mode data; based on topological structure data and typical wiring mode data, a main network self-healing model including bus self-healing, buscouple self-healing and total station self-healing types is constructed by adopting a graph theory adjacency matrix, and the charging and discharging states of the model are periodically polled and judged according to equipment operation state data. And the linkage analysis of the topology power-off state and the bus voltage abnormity is fused before and after the self-healing process is started, and the self-healing action logic is locked through multiple conditions, so that the problem that the traditional technology is lack of standardized modeling, dynamic state evaluation and linkage analysis is solved, the strategy reusability and the fault power recovery efficiency are improved, the misoperation risk is reduced, and the reliability of the system is improved. And the safety and reliability of the operation of the main network are ensured.

The invention belongs to the technical field of data mining and artificial intelligence, and discloses a high-dimensional data clustering and feature structure analysis method, which comprises the steps of 1, preprocessing high-dimensional data and outputting standardized data to obtain a preprocessed standardized data set, 2, constructing a Gaussian graph mixture model to perform unsupervised clustering, and 3, obtaining a feature structure of the Gaussian graph mixture model; the method comprises the following steps: step 1, carrying out classification on the data sub-groups, and outputting data sub-groups containing class labels, step 3, independently constructing a feature association network for each class of data sub-groups, and outputting a topological graph of the feature association network of each sub-group, and step 4, carrying out multi-dimensional graph theory index calculation and statistical test on the feature association network of each sub-group, and outputting a final analysis result. According to the method, the problem that high-dimensional complex manifold distribution data is difficult to process is solved, core features behind different categories and a dynamic interaction mechanism thereof can be visually displayed, spanning from sample division to mechanism revealing is realized, and the method can be widely applied to the fields of medical subtype discovery, financial risk conduction analysis, industrial fault diagnosis and the like.

The invention discloses a legal contract risk monitoring and early warning method, and particularly relates to the technical field of risk early warning. The method comprises the following steps: constructing a multi-contract risk propagation network by identifying transmissible terms in a plurality of legal contract texts, and generating a multi-contract propagation path topology network model by adopting a graph theory algorithm; identifying a cascade trigger mechanism of risk propagation terms between contracts by using time sequence causal analysis, and outputting a cascade risk propagation link; performing sensitivity grading on each risk propagation path to obtain propagation path sensitivity grading data; integrating the cascaded risk propagation link and propagation path sensitivity grading data, constructing a contract network risk propagation comprehensive evaluation model, and outputting a comprehensive risk evaluation result; comparing the comprehensive risk assessment result with a preset risk early warning threshold rule to generate corresponding risk prompt information; according to the method, the propagation trend and the diffusion path of the legal contract risk can be effectively predicted, and the timeliness and the accuracy of contract risk management and control are improved.

The invention provides an online dynamic water quality fingerprint rapid acquisition and preprocessing device and method. The method comprises the steps that intelligent flow path selection is achieved through graph theory modeling, a water quality monitoring system is modeled into a directed weighted graph, bilateral connected component decomposition is carried out to recognize system vulnerabilities, and a redundant flow path is configured to improve the fault-tolerant capability; adopting an optimal transmission theory to optimize dilution control, modeling a dilution problem as an optimal transmission problem of spectral distribution, and calculating an optimal dilution ratio through a linear space super-gradient algorithm; a matrix flipping graph search algorithm is applied to accelerate spectrum calculation, a spectrum data matrix is abstracted into a flipping graph model, and an optimal matrix flipping operation sequence is found through heuristic search. According to the method, the efficiency, precision and robustness of water quality monitoring are remarkably improved, the monitoring efficiency is improved by 50%, the measurement error is reduced by 70%, and the calculation speed is improved by 5-10 times.

The invention discloses a crop germplasm resource data analysis and integration system, which relates to the technical field of germplasm resource management and comprises a data acquisition module and a data processing module, the mapping module is used for building a crop germplasm correlation graph based on a graph theory algorithm, and the integrated retrieval module is used for providing multi-dimensional retrieval for the correlation graph. According to the method, germplasm resources are mapped into nodes by utilizing a graph theory method, and correlation map view angles oriented by different functions such as genotype approximation, phenotype shape approximation or environmental adaptability approximation can be constructed as required by adjusting the proportions of genotype, phenotype characteristics and environmental adaptability vectors in the correlation map in edge weights; and a basis is provided for diversified analysis and retrieval.

The invention relates to the technical field of big data mining and analysis, and discloses a charging pile intelligent site selection method and system based on big data analysis, and the method comprises the steps: obtaining traffic space-time big data, and generating a road network topological structure; determining a shortest path matrix based on the road network topological structure, generating an importance score in combination with a graph theory algorithm, and comparing the importance score with a preset score threshold; carrying out simulation evolution based on a comparison result, generating a propagation coefficient and an aggregation coefficient of the node, and obtaining a transmission influence value; acquiring spatial load data, and outputting a site selection scheme set in combination with a genetic algorithm; obtaining road network structure data of the scheme set, and determining a preliminary ranking; acquiring data of the ranking scheme to determine a to-be-optimized scheme, and updating by using the prediction model; and calculating a time expectation value based on the updated data, and outputting a final site selection scheme by combining a preset time threshold value for judgment. According to the method, the road network integrity and the dynamic association effect are quantitatively evaluated through big data analysis, and global optimization of charging pile site selection is realized.

The invention provides a double-layer optimization scheduling method for source network load storage collaborative loss reduction, and belongs to the technical field of source network load storage collaborative scheduling. A double-layer optimization framework comprising an upper layer planning model and a lower layer operation model is constructed, and a key node set is screened by using a node importance comprehensive scoring method based on a graph theory; the improved wolf pack algorithm is adopted to solve a double-layer coupling problem, a lower-layer operation effect is fed back to an upper layer to serve as a fitness evaluation basis, and in cooperation with population aggregation degree monitoring, a wandering wolf random jumping mechanism and a reverse learning strategy, deep collaboration and global optimal solution of a planning layer and an operation layer are achieved. The technical problem of poor network loss optimization effect caused by lack of an effective double-layer coupling solution mechanism for collaborative optimization planning and operation scheduling of the distributed photovoltaic and energy storage system in the power distribution network is solved.

The invention provides a discrete manufacturing intelligent scheduling method based on a graph theory. The discrete manufacturing intelligent scheduling method comprises the steps of 1, constructing a heterogeneous graph model of scheduling elements; step 2, static scheduling optimization based on a critical path method; step 3, resource allocation optimization based on a multi-resource bipartite graph matching method; and 4, dynamic response and rescheduling are carried out. Based on a graph theory method, a complex'process-resource-constraint 'relationship is converted into a visual heterogeneous graph model, and a systematic scheduling solution based on the graph theory is constructed, so that the scheduling solution is suitable for a discrete manufacturing scene with multi-process, multi-equipment, multi-constraint and dynamic disturbance characteristics; the method is used for realizing static planning of production plan scheduling, resource optimization distribution and dynamic adjustment full-process optimization.

The invention discloses an MBSE model version pedigree iteration method and system based on a graph theory and a knowledge graph. The method comprises the following steps: constructing an MBSE model of an aerospace craft and mapping the MBSE model into an attribute graph; a directed acyclic graph is adopted to record a version iteration process, management is carried out through a version number system comprising a main version number, a combined version number and a revised version number, and a differential storage strategy is adopted; and performing change detection and merging based on a baseline, accurately identifying changes through a model comparison algorithm including internal attributes, internal relationships, overall relationships and difference analysis, and processing conflicts in collaborative design. According to the method, atomic-scale version control is realized, the problems of missing change traceability, low verification efficiency and multi-baseline conflict are solved, and the efficiency and quality of aerospace craft full-life-cycle model management are improved.

The invention provides an embedded part group precision control method for wind tunnel construction, and belongs to the technical field of wind tunnel construction, and the method comprises the steps: building an initial control network, dividing embedded part groups in a segmented manner, and determining the position of an optimal control point through an iteration experiment method and a graph theory network model; establishing an error propagation mathematical model to describe a measurement error propagation rule, performing control point position optimization calculation by applying a double-layer game optimization algorithm and a cross subwolf optimization algorithm, calculating three-dimensional coordinates of the embedded part by combining a resection method and a forward intersection method, and implementing real-time monitoring and dynamic adjustment of the laser tracker; and finally, overall precision verification is carried out through the total station and the laser scanner, and the technical problem that accumulated error propagation is difficult to effectively control in the installation process of the wind tunnel embedded part group is solved.

The invention provides an expressway multi-bottleneck layered ramp coordination control method based on deep reinforcement learning so as to improve the overall traffic efficiency of a system. According to the method, an expressway comprising a plurality of bottleneck road sections is taken as a whole, and a ramp coordination control method is constructed by adopting a deep reinforcement learning algorithm according to a macroscopic fundamental diagram theory. According to the method, a hierarchical control framework is adopted, wherein upper-layer control is combined with an expressway macroscopic fundamental diagram theory and a deep reinforcement learning algorithm, and expected ramp total flow entering a main line is output; and a refined local control strategy is adopted in lower-layer control, the total flow of the ramps is distributed to all the ramps, and the expected flow of all the ramps is obtained. The control method provided by the invention not only can play the advantages of low cost and high robustness based on macroscopic fundamental diagram control, but also has the advantages of being independent of an accurate model and capable of adapting to an uncertain environment based on deep reinforcement learning control, so that the requirement of efficient ramp coordination control of a large-range expressway can be effectively met.

The invention relates to the technical field of data processing, and provides a feature selection-based multi-modal data classification method, which comprises the following steps of: obtaining original data of at least two heterogeneous modals and corresponding initial feature sets; performing intra-modal selection on each modal, constructing a feature association graph based on a graph theory, and screening a core feature subset meeting a threshold requirement through mutual information; constructing a cross-modal feature incidence matrix, realizing inter-modal fusion based on a weighted graph model and condition mutual information, and screening a cross-modal key feature set; and inputting the key features into a classification model to train a multi-modal classifier, and repeating a feature selection process on to-be-classified data to complete classification prediction. According to the method, the optimal threshold value is adaptively determined through innovative fusion of double-stage feature selection, the graph theory and the information theory, the classification accuracy and the data processing efficiency are remarkably improved, and the method can be widely applied to the fields of medical image and pathological report combined diagnosis, automatic driving data fusion, internet multimedia understanding and the like.

The invention relates to the technical field of power system safety measures, and particularly provides a method for constructing a safety measure generation model based on a graph theory and a large language model, and the method comprises the following steps: constructing a power grid knowledge graph according to a power grid topological graph, and a sequential rule and a restrictive rule of equipment operation; inputting the structured text instruction of the power grid knowledge graph into the large language model, and generating an initial work ticket through the large language model; generating a training sample according to topological features extracted from the power grid topological graph and the initial work ticket, and training the large language model to obtain a topological perception enhancement model; and according to the multi-modal auxiliary data and the topological features, generating a multi-modal feature vector, and re-training the topological perception enhancement model to obtain a security measure generation model, so as to solve the technical problems that the security measure generation model cannot process image data and the generated security measure is easy to violate equipment operation security requirements.

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.