784 results about "Undirected graph" patented technology

The embodiment of the invention provides a welding path planning method and device, and the method comprises the steps: carrying out the three-dimensional scanning of a to-be-processed object through a machine vision algorithm, obtaining three-dimensional point cloud data, carrying out the data analysis of the three-dimensional point cloud data through a segmentation algorithm based on deep learning, and carrying out the recognition to obtain geometric features; the geometric features are constructed into an undirected graph, an optimal communication path of a weld joint center line is searched on the graph through a minimum spanning tree algorithm, and a fine three-dimensional model of the machined object is constructed through the optimal communication path; according to the three-dimensional coordinates of the welding seam locating point, the current welding gun posture and the target welding seam length, the optimal locating height, locating depth, inclination angle and offset of a welding gun are determined, and a corresponding welding path plan is generated through a welding process simulation algorithm based on the locating height, the locating depth, the inclination angle and the offset; the precision and efficiency of welding path planning can be effectively improved.

Disclosed in the present invention is a graph theory-based method for locating an instability fault source in a direct-current microgrid. The method includes: collecting key electrical feature quantities through an instability fault feature collection and extraction link to accurately capture an instability oscillation phenomenon, and utilizing wavelet packet decomposition to construct fault feature vectors; then, according to the structure of the direct-current microgrid, constructing an undirected graph model G=(V, E, X), which lays a foundation for analysis; applying a graph convolutional neural network for deep learning and training to further improve the accuracy and efficiency of diagnosis; deploying the trained model in an actual system to effectively achieve accurate fault diagnosis and location of an instability source. Also provided in the present invention is a device for locating an instability fault source in a direct-current microgrid. The method provided by the present invention can significantly improve a maintenance level of the direct-current distribution network, and greatly improve a detection and adjustment ability of system operation stability through advanced automatic diagnosis technology.

The invention relates to the technical field of knowledge maps, and discloses a knowledge map searching method and system based on local semantics and global communitization, and the method comprises the steps: extracting entities and relationships thereof from a knowledge map to construct an undirected graph; the undirected graph is divided into multiple levels of community information, and a community report is obtained by generating a community information table; selecting an instance triple containing data source information from the knowledge graph, constructing an instance triple information source table, extracting IDs, names and attributes of entities, and generating an entity attribute table to obtain an entity attribute vector table; key entities in a to-be-queried question are extracted and subjected to vector representation, the similarity between the key entities and an entity attribute vector table is calculated, first N related entities are selected to generate contexts (including an entity context, a relation context, a data source context and a community context), and finally a search result of the to-be-queried question is obtained. According to the method, the information related to the to-be-queried question can be accurately retrieved from the knowledge graph.

The invention relates to the field of deep learning and emotion recognition, in particular to an EEG emotion recognition system and method based on multi-scale space-time diagram convolution and comparative learning, and the method comprises the steps: carrying out the preprocessing of an EEG signal; constructing positive and negative sample pairs based on the emotion categories and the identities of the subjects; extracting hidden space representation of the electroencephalogram signal by adopting a stack auto-encoder, and dividing the electroencephalogram signal into a plurality of functional brain regions to obtain a brain source signal after dimension reduction; constructing an undirected graph structure of the brain source signal; constructing a multi-scale space-time diagram convolutional network as a feature extraction network, inputting an undirected graph structure, dynamically learning and updating an adjacent matrix, expressing features of brain source signals in the undirected graph structure, and extracting emotion related features; based on a multi-scale space-time diagram convolution and comparative learning framework, an EEG emotion recognition model is built and trained to eliminate individual differences. According to the method, the multi-dimensional information of the EEG signals is fully utilized, and the accuracy, robustness and generalization performance of emotion recognition are remarkably improved.

The invention discloses an automated guided vehicle scheduling optimization method and device, a medium and a terminal, and the method comprises the steps: generating fleet heterogeneous data based on parameter data of a plurality of automated guided vehicles in an intelligent warehouse, and fusing the fleet heterogeneous data into an undirected graph model obtained through undirected graph modeling based on storage environment topological data in advance, the method comprises the steps of obtaining an environment topological graph model, then carrying out graph topology and heterogeneous feature extraction on the environment topological graph model to obtain a multi-constraint path planning model, and dynamically adjusting a single-vehicle route of each automated guided vehicle by utilizing the planning model and real-time storage field state data. And then performing scheduling process simulation on the intelligent warehouse based on the dynamic routing table formed by adjustment and historical scheduling index data of the intelligent warehouse, so that a scheduling system model learns a mapping strategy from a state to a behavior and outputs an optimization decision strategy. The method is used for automatic guide vehicle scheduling, is suitable for storage environments of different scales and complexities, and improves the scheduling efficiency and accuracy.

The invention discloses an adaptive learning path recommendation method based on a hypergraph neural network and knowledge tracking, and relates to the field of learning path recommendation, and the method comprises the steps: determining an incidence relation between learning resources, and enabling the incidence relation to serve as an edge of a learning resource undirected graph; the features of the learning resources serve as embedded feature vectors of all nodes of the learning resource undirected graph; updating the embedded feature vector by using a graph neural network to obtain a resource embedded vector, and taking the resource embedded vector as a node feature of a learner hypergraph structure; performing iterative aggregation on the learner hypergraph structure by using a hypergraph neural network to obtain a dynamic resource embedding and learner behavior sequence, generating an initial recommendation list, further generating a candidate learning path set, and generating a Pareto frontier solution set by using a non-dominated sorting genetic algorithm II; and calculating a comprehensive score of each path in the solution set based on a dynamic weight distribution strategy and a comprehensive utility function, and determining an optimal learning path. According to the invention, the accuracy and effectiveness of learning path recommendation are improved.

The invention discloses a high-proportion new energy power grid island division method considering frequency constraint, and belongs to the technical field of power system stability analysis and control, and the method comprises the steps: calculating a coherence coupling matrix of a generator and a new energy station through point voltage phase angle information; constructing a homology information undirected graph and calculating a normalized Laplacian matrix; performing characteristic value analysis, and determining the number of coherent generator groups and the coordinates of the generator and the new energy station; determining the composition of a coherent generator group through inverse mapping, and obtaining a multi-type power supply grouping result; establishing a coherence grouping constraint and a frequency security constraint according to a multi-type power supply grouping result; and constructing an island division optimal section search model and solving to obtain an optimal splitting section. By the adoption of the method, coherence grouping of multiple types of power supplies is achieved, and the optimal island division scheme is obtained by constructing and solving the island division optimal section search model considering the new energy supporting capacity and the frequency constraint.

The invention provides a dam safety monitoring method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of collected multi-source monitoring time series data, inputting the data into a trained anomaly detection model, obtaining the anomaly probability distribution of each monitoring node, and obtaining the anomaly probability distribution of each monitoring node; and abnormal information is displayed through a dam safety monitoring visualization system. According to the method, the undirected graph is constructed through the propagation model of each monitoring type and the spatial features and the time features of the monitoring nodes, the undirected graph is input into the graph neural network for coding to obtain the second feature vector representing the state information of the dam structure, and the environment information is input into the first deep neural network for coding to obtain the first feature vector; and the first feature vector and the second feature vector are fused and then input to the second deep neural network to obtain the anomaly probability distribution, so that the accuracy and precision of dam anomaly detection are greatly improved.

The invention discloses a multi-modal abnormal data detection and restoration method and system oriented to a power business scene. The method comprises the following steps: collecting multi-source heterogeneous power data, abstracting a power system into a weighted undirected graph, uniformly mapping the multi-source heterogeneous data into a graph signal, and preprocessing the collected data; extracting spatial features of nodes in a topological structure by adopting a graph convolutional network, and capturing a time dependency relationship in combination with a time sequence encoder; identifying various types of data abnormal points through an abnormal scoring function fusing the time sequence prediction error and the neighborhood consistency; a prediction-reconstruction combined repair strategy is adopted, time sequence prediction and neighborhood diffusion estimation are fused, and a preliminary repair value is generated; a lightweight parameter adapter is introduced, a scene feature vector is used as input, a repair weight and a regularization coefficient are dynamically generated, and a repair strategy is automatically adjusted; and performing physical consistency verification on a data result, wherein the physical consistency verification comprises power injection conservation constraint, voltage amplitude range constraint and time sequence continuity constraint.

The invention discloses a networking charging data cross-platform fusion and transmission method based on an intelligent scheduling algorithm. The method comprises the following steps: S1, collecting and preprocessing networking platform charging data; s2, feature similarity is calculated, and an undirected graph with samples as nodes and similarity as edge weight is constructed; s3, calculating a standardized Laplacian matrix, extracting feature vectors, and clustering to generate feature cluster tags; s4, constructing a modal mapping matrix, mapping the feature vectors, and inputting the mapped feature vectors into a first convolution restricted Boltzmann machine to extract fusion features; s5, inputting the fusion features into a second convolution restricted Boltzmann machine, and combining graph structure sorting and constructing a weighted directed graph; s6, initializing a virtual scheduling factor in the graph, and searching an optimal transmission path by an ant colony algorithm; and S7, distributing fusion features according to paths, and realizing cross-platform efficient transmission of charging data. According to the method, spectral clustering, modal mapping, convolution extraction and intelligent scheduling are fused, and the multi-platform charging data fusion accuracy and transmission efficiency are improved.

The invention discloses an intelligent dialogue system for time sequence memory management and task distribution, which is based on CrewAI multi-agent collaboration, combines time sequence memory updating and multi-agent collaboration work, introduces an independent memory unit in order to solve the problem of low memory efficiency management, and uses an undirected graph structure to store learned information. Storing each piece of information as a node in the graph; meanwhile, memory is increased, modified and forgotten according to a time sequence, so that the problem that important information is forgotten is relieved to a certain extent; in order to solve the problem of insufficient task interaction, a multi-agent cooperation mechanism is introduced, user demand understanding, task execution and task verification are respectively submitted to three different agents for independent work, and through independent work and complete information exchange among the three agents, information synchronization and state sharing among modules are realized, information islands are reduced, and the task interaction efficiency is improved. And decision accuracy is improved.

The invention discloses a computing power aware routing distribution method and system based on SRV6, and the method comprises the steps: constructing a network topology undirected graph based on a target distributed cluster; in response to the service request, obtaining real-time resource state data of each node in the target distributed cluster, and analyzing the service request to obtain resource request data; matching each node in the network topology undirected graph with the resource request data to obtain a target node; calculating an optimal path from the source node to the target node in the network topology undirected graph by using an improved Di jkstra algorithm; and according to the optimal path, configuring a corresponding routing rule in the target distributed cluster through an SRV6 network programming technology. According to the method provided by the invention, the problem of insufficient computing power resource routing distribution flexibility is solved.

The invention provides a drug target binding affinity prediction method based on multi-modal data fusion. The method comprises the following steps: firstly, extracting sequence feature information of drug SMILES and target FASTA, then constructing an affinity graph, modeling drug molecules and target protein molecules into an undirected graph, and extracting molecular-level features of atoms, bonds, residues and contact. And fusing the hierarchical graph structure information of the affinity graph and the molecular graph to obtain the graph structure feature representation of the drug-target spot. The sequence feature information and the graph structure feature representation are further fused by using intramolecular and intermolecular attention fusion mechanisms. And finally, performing affinity prediction by using the fused features, and outputting a drug-target binding affinity score. According to the method, sequence and structural information are effectively fused, the accuracy of drug target affinity prediction is improved, and the problems of insufficient information fusion and insufficient structural information utilization in an existing method are solved.

The invention provides a method for automatically centering and assembling a bearing on a long-axis part, and belongs to the technical field of bearing assembly.The method comprises the steps that position information of the long-axis part and the bearing is collected in real time through a binocular camera and a laser distance measuring sensor, a central line equation is calculated through the least square method, and position deviation is determined; a centering adjustment vector is generated to control a multi-degree-of-freedom precise attitude adjustment platform to perform preliminary adjustment, a mechanical arm is matched with high-precision laser ranging to perform precise centering, the centering process is modeled into a weighted undirected graph, an optimal adjustment path is solved, optimal press fitting parameters are calculated based on a bearing press fitting physical mechanics equation, and press fitting is performed. Meanwhile, the assembling process is monitored through a force sensor and a displacement sensor, an assembling quality evaluation matrix is constructed, finally, the assembling result is detected and evaluated, and high-precision automatic assembling of the long-axis part and the bearing is achieved.

The invention discloses a dynamic identification system and method for fault nodes of a heat pump measurement and control network, and aims to solve the problems of one-sided weight distribution, lack of directional modeling and insufficient dynamic adaptability in the prior art. The system comprises an energy transfer topology network model building module which is used for respectively quantifying the physical connection strength and the directional energy transfer path of the heat pump system by building an undirected graph and directed graph bimodal model; the multi-dimensional weight distribution module adopts a subjective and objective fusion strategy and combines a complex network theory, an entropy weight method and an analytic hierarchy process to distribute comprehensive weights for nodes and explicit modeling directivity dependence; the dynamic robustness analysis module is used for simulating and positioning structural hub nodes through static attacks, simulating and tracking cascade failure and topology reconstruction processes through dynamic attacks, and comprehensively evaluating the robustness of the system; and the fault simulation verification module verifies weight rationality and method effectiveness from four dimensions of objectivity, center matching degree, interpretability and robustness based on a fuzzy comprehensive evaluation framework. According to the method, through fusion topology modeling, dynamic weight distribution and dynamic and static combination robustness analysis, key fault nodes are accurately recognized, the system vulnerability is revealed, a data driving basis is provided for redundancy design, intelligent operation and maintenance and reliability improvement of the heat pump system, and the operation and maintenance cost is remarkably reduced.

The invention belongs to the technical field of parametric modeling, and discloses a dynamic parametric modeling method for a tunnel operation area. Comprising the steps of collecting actual measurement data of a tunnel operation area; based on the collected actual measurement data, an initial model of the tunnel operation area is established; dividing the initial model of the tunnel operation area into a plurality of model areas, and constructing an operation area undirected graph by taking the model areas as nodes; colors are distributed to nodes in the operation area undirected graph; for each model area, determining a corresponding parameter group according to the allocated color, and updating based on the initial model of the tunnel operation area and the parameter group corresponding to each model area to obtain an overall parameterized model; the dynamic parametric modeling result of the whole tunnel operation area provides a scientific decision basis for design optimization, construction scheme formulation, risk assessment, operation management and the like of tunnel engineering.

The invention discloses an electromagnetic field simulation grid adaptive generation method based on a neural network. The method comprises the following steps: performing mesh generation on a semiconductor device simulation model to obtain original generation data, and constructing node features; an undirected graph is constructed, graph nodes of the undirected graph adopt node features of grid points, and connecting edges adopt Euclidean distances between the grid points and adjacent grid points; inputting the undirected graph into a double-branch neural network, and outputting a predicted node position; and correcting each node of the original subdivision data by using the predicted node position obtained by the double-branch neural network to form new subdivision data for electromagnetic field simulation. According to the method, physical field information can be fused to efficiently adjust the node density, and the geometric boundary and topological structure characteristics of the device can be strictly kept.

The invention relates to an unmanned aerial vehicle control system and method for formation flight in a wind environment. The method comprises the following steps: generating a wind disturbance environment with a specified map scale and containing random obstacles; acquiring external image data and inertial measurement data of the unmanned aerial vehicle, and generating a grid map through point cloud extraction and conversion operation; the method comprises the following steps: modeling a spatial topological structure of an unmanned aerial vehicle cluster by adopting an undirected graph mode, and solving and generating a collision-free initial trajectory by adopting an unconstrained discrete optimization mode; optimizing the initial trajectory by adopting a task redistribution strategy, and re-planning a global target and a local target to obtain a re-planned optimized trajectory; and based on the optimized trajectory, solving by adopting a model prediction control mode to obtain an optimal control quantity for unmanned aerial vehicle bottom layer control so as to correspondingly control the motion state of each unmanned aerial vehicle and realize formation flight. Compared with the prior art, the unmanned aerial vehicle cluster can efficiently and accurately realize formation flight in an obstacle environment with wind influence.

The invention discloses a ground-to-ground heterogeneous cluster formation control method and system based on an adjustable funnel algorithm, and relates to the technical field of distributed formation maneuvering control, and the method comprises the steps: building a dynamic model of a ground-to-ground heterogeneous cluster, and building an undirected graph representing an interaction topological structure of the ground-to-ground heterogeneous cluster; modeling a distributed safety critical formation maneuvering control problem of the air-ground heterogeneous cluster; a fully distributed dynamic compensator facing each follower and a formation maneuvering controller based on an adjustable funnel algorithm are designed, and then a safety-critical control framework based on a disturbance observer is adopted to construct a composite controller; and acquiring a virtual navigator signal and inputting the virtual navigator signal to the constructed various controllers to obtain an actual formation control signal of each follower, and executing distributed safety-critical formation maneuvering control on the followers. Through a distributed safety critical formation maneuvering strategy based on an adjustable funnel method, collision / obstacle avoidance and disturbance suppression of an air-ground heterogeneous cluster can be realized.

The invention discloses a traffic flow prediction method based on a time-space synchronization embedded graph Transform model. The method comprises the following steps: acquiring historical traffic flow sequence data of a target area and preprocessing the historical traffic flow sequence data; defining a traffic network of a target area as an undirected graph, calculating a static adjacent matrix based on a graph structure, and obtaining spatial coding features through a full connection layer; extracting original traffic features through a full connection layer based on the preprocessed data; embedding the characteristic representations of the week cycle, the day cycle and the timestamp through a full connection layer to obtain week cycle, day cycle and timestamp characteristics; based on a space-time adaptive embedding mechanism, obtaining adaptive space-time features; hidden space-time representation is obtained through feature splicing; extracting space-time dependence characteristics of traffic flow along a time dimension and a space dimension through a double-channel attention mechanism to obtain enhanced space-time characteristic representation; and generating a traffic flow predicted value of each node in a future time period through a full-connection output layer. According to the invention, the traffic flow prediction precision is improved.

The invention discloses an air-ground integrated network spectrum power joint distribution method combining hypergraph and meta reinforcement learning. The method comprises the following steps: step 1, establishing a multi-unmanned aerial vehicle communication network model; 2, constructing an undirected graph and hypergraph structure; 3, introducing a graph attention network to realize bidirectional aggregation of node features; 4, the aggregated node features are input into a Meta-DQN strategy network, so that the strategy has rapid adaptive capacity when facing environment changes and new tasks; according to the air-ground integrated network spectrum power joint allocation method combining the hypergraph and meta reinforcement learning, node feature representation with global perception capability can be acquired by each unmanned aerial vehicle agent on the premise of only depending on local observation, and rapid adaptation is realized in the face of dynamic environment change, so that efficient migration of a resource allocation strategy is realized, and the resource allocation efficiency is improved. The spectrum utilization rate and the communication success rate of the system are improved, and stable operation of the air-ground converged communication system in a complex scene is effectively guaranteed.

The invention provides a power distribution network fault intelligent positioning and self-healing recombination method and system. The method comprises the steps that S1, operation data of a power distribution network are acquired and preprocessed; s2, modeling the power distribution network topology into a weighted undirected graph, and constructing transient characteristics including node characteristics and edge characteristics; s3, on the basis of the transient characteristics and the power distribution network topology, the fault type is judged in real time by adopting a graph convolutional network, and a fault section is determined in combination with a traveling wave distance measurement and impedance method; s4, when entering an isolation fault state, automatically generating a power supply reconstruction scheme meeting current, voltage and switch constraints, and selecting an optimal scheme through multi-objective optimization; and S5, issuing a switching instruction according to the optimal scheme to complete topology reconstruction, and updating a fault identification and recombination strategy according to an actual execution result to improve positioning precision and recovery efficiency. According to the invention, a novel fault processing scheme integrating multi-source measurement, an efficient algorithm and online learning can be adopted, so that the power failure time is shortened, the power failure range is reduced, and the toughness of the power distribution network is improved.

The invention discloses a medical data center network risk assessment method based on a random walk model. The method comprises the following steps of 1, obtaining topological structure information and a node set of a medical data center network; 2, constructing a weighted undirected graph according to the topological structure and the data traffic information between the nodes; 3, in the weighted undirected graph, performing simulation based on a random walk model to obtain a transition probability matrix between nodes; 4, calculating a node risk score of each node according to the transition probability matrix; 5, performing aggregation processing on the node risk scores of the nodes to obtain an overall network risk score; and step 6, generating an assessment report according to the overall network risk score. According to the method, the risk conduction probability between the nodes is quantified by constructing the combination of the weighted undirected graph and the random walk model, the limitation of a traditional static assessment method is broken through, and a cross-node risk propagation path caused by data flow can be identified.

The invention discloses a construction method and device of a metal material performance prediction model, equipment and a medium. The method comprises the following steps: constructing an initial graph neural network; acquiring a three-dimensional volume element structure of the specified metal material and mechanical property data corresponding to the three-dimensional volume element structure; describing the three-dimensional volume element structure as an undirected graph structure; constructing a target data set by using the undirected graph structure and the mechanical property data; and performing iterative training on the initial graph neural network through the target data set to obtain a metal material performance prediction model. Therefore, the undirected graph structure of the three-dimensional volume element structure is used as a bridge, the metal material performance prediction model between the complex three-dimensional volume element structure and the mechanical performance data is established through the graph neural network, the model can rapidly and precisely predict the macroscopic performance of the metal material, the cost is low, and the method is easy to implement. And the requirements of rapid iteration and real-time feedback of the macroscopic performance of the material in engineering design can be met.

The invention relates to a multi-task-point cruise path planning method and device for an unmanned aerial vehicle. The method comprises the following steps: acquiring flight data of the unmanned aerial vehicle in multiple flight stages; the flight data comprises corner data when the flight stage is a corner stage; constructing a flight energy consumption model according to the undirected graph, the flight data and the rotation angle data; the energy consumption of each task point in the cruising process of the unmanned aerial vehicle is obtained; performing iterative optimization on the energy consumption of the plurality of task points based on a wild dog optimization algorithm to obtain a path planning result, and controlling the unmanned aerial vehicle to fly according to the path planning result; energy consumption of different flight stages is quantified by constructing a flight energy consumption model, including energy consumption of rotation angle data in a rotation angle stage, so that the complex environment adaptability is improved, iterative optimization is performed on a path in combination with a wild dog optimization algorithm, the problems of inaccurate energy consumption modeling and easy falling into local optimum are solved, and the energy consumption modeling efficiency is improved. The method has the advantages that the energy consumption optimization efficiency and accuracy of path planning are improved, and the local optimum problem is avoided.

The invention relates to the technical field of consumer behavior analysis, and discloses a network user shopping behavior causal analysis method and system, and the method comprises the following specific steps: collecting multi-dimensional time series data affecting a shopping decision; constructing an observation variable set V with time lag; initializing a completely undirected graph structure G; a tensor rank-based conditional independence test method is adopted to obtain a skeleton graph structure G'and a potential variable candidate pair set Lc after conditional independence judgment pruning; potential variables are grouped, and a graph structure GL containing the potential variables is obtained; and performing causal direction judgment operation on the GL and performing direction control on the potential variable nodes to generate a directed acyclic graph structure Gfinal. According to the method, the problem of difficulty in causal structure analysis in the prior art is solved, and the method has the characteristics of high interpretability and robustness.

The invention discloses an image processing method and system based on an artificial intelligence large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining an original image, segmenting the original image into overlapped blocks, and generating an image block feature set through a fusion strategy; constructing an undirected graph structure based on the image block feature set, calculating a node similarity weight and generating a global topology descriptor; generating an importance weight according to the dimension importance of the global topology descriptor, generating a noise vector in combination with noise intensity and structural constraint, and generating an enhancement vector and an offset matrix through a UNet encoder-decoder; and performing feature distillation on the enhanced vector by using a student network and a teacher network, and generating a final distillation feature through cosine similarity matching and an attention mechanism. According to the method, a dynamic noise intensity calibration mechanism is introduced in a knowledge distillation link, and accurate constraint of a noise direction is realized through variance parameter linkage control, so that generalization errors are reduced.

The invention relates to the technical field of network security monitoring, in particular to a multipath network communication security guarantee method based on an adaptive encryption algorithm, comprising: acquiring a network undirected graph corresponding to a target network, the network undirected graph comprising a first node set, a second node set and an undirected edge set, the first node is used for collecting and managing data, the second node is used for monitoring the safety state of the first node, and the undirected edge is used for connecting the first node and the second node. According to the method, the behavior of the second node in the network is subjected to feature extraction, the trust level of each node can be dynamically evaluated and adjusted in combination with the pre-trained trust classification model, and the self-adaptive feature can effectively adjust the communication security level according to the actual situation, so that the security of the network is improved, and the user experience is improved. And potential security threats or malicious nodes can be identified by monitoring and analyzing the behavior of the second node.

The embodiment of the invention provides an infrastructure system security assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an infrastructure undirected graph which is generated by a logic connection relation between a plurality of infrastructures in an infrastructure system; generating a vulnerability curve corresponding to each node in the infrastructure undirected graph; based on the vulnerability curve of each node, the simulation fault probability of each node is obtained; node division constraints of the infrastructure system are obtained, a similarity matrix is generated based on the simulation fault probability of each node, clustering division is carried out on the infrastructure system based on the similarity matrix and the node division constraints, multiple division subsystems are obtained, and each division subsystem at most comprises one fragile node; and carrying out security assessment on each divided subsystem to obtain a security assessment result of the infrastructure system, thereby greatly improving the accuracy and foresight of the security assessment result of the key information infrastructure cascade.

The invention belongs to the technical field of protein structure prediction, and relates to a protein structure prediction method, system and device based on a light quantum computer, and the method comprises the steps: 1) obtaining a multi-sequence alignment MSA matrix of a target sequence based on the target sequence alignment of a to-be-predicted protein; 2) encoding the obtained multi-sequence alignment MSA matrix into a {0, 1} matrix; 3) converting the amino acid interaction of the protein into an undirected graph model of {0, 1} state nodes, and training by adopting a Boltzmann machine training mechanism and using a light quantum computer to obtain a weight coefficient of a connecting edge between the nodes in the undirected graph model; and 4) obtaining an interaction coefficient between different amino acids of the protein based on the weight coefficient. According to the method, the calculation efficiency and the solving result of protein structure prediction are improved, and the problems that complex scheduling problems are difficult to calculate and accurate solutions cannot be obtained in the prior art are solved.