630 results about "Undirected graph" patented technology

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 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 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 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 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 relates to the field of heliostat calibration, and discloses a scheduling method for calibrating heliostats through cooperation of artificial light sources and cameras in a large-scale heliostat field, and the method comprises the following steps: determining the heliostat range covered by each camera, determining the heliostat range covered by each artificial light source, and determining the heliostat range covered by each camera; the method comprises the following steps: determining the center and size of a light spot in an image shot by a camera when the heliostats are calibrated, determining the heliostats which can be calibrated in the coverage range of each artificial light source-camera combination, regarding each effective calibration combination as a node to construct an undirected graph, dividing the nodes into a plurality of groups through a graph grouping algorithm, and ensuring that the nodes in the same group are not connected with each other, therefore, all combinations in the same group can execute calibration in parallel. According to the method disclosed by the invention, the problem of affiliation misjudgment caused by light spot interference between heliostats is solved, possible shadow and shielding problems are avoided, dynamic scheduling of the artificial light source and the camera is realized, and invalid operation time is shortened.

The invention relates to a stable database query processing method and device based on predicate transfer, and the method comprises the steps: obtaining a target query statement; analyzing the target query statement to generate a target undirected graph; wherein the target undirected graph takes a base table corresponding to the target query statement as a node, and takes equality connection corresponding to the target query statement as an edge; converting the target undirected graph into a target directed connection graph; adopting a Bloom filter to pre-filter nodes in the target directed acyclic connection graph; and performing data query based on the pre-filtered target directed acyclic connection graph to obtain a query result. Through the method and the device, the redundant data in all the base tables in the target query statement is pre-filtered, and the method can be practically applied.

The invention discloses a GCN-LSTM-based tailing dam multi-point settlement deformation prediction method. The method comprises the steps of collecting and preprocessing original settlement monitoring data of monitoring points in a research area; constructing a graph structure for settlement data between all monitoring point pairs, setting a threshold value, connecting node pairs with significant correlation coefficient relationships by using edges, constructing a weighted undirected graph, and converting the weighted undirected graph into a normalized adjacent matrix as the input of a graph convolutional network GCN; extracting the spatial topology of each monitoring point by using a GCN; capturing long-term time dependence in the settlement process by using a gating mechanism of the LSTM network; and fusing the GCN and the LSTM network, and finally outputting a predicted value through a full connection layer. According to the method, the spatial topological graph among the monitoring points of the tailing dam is constructed, and the GCN and LSTM networks are fused, so that the spatial correlation among the monitoring points and the time dynamic characteristics of the settlement data are effectively captured, and high-precision tailing dam multipoint settlement prediction is realized.

The invention provides a parking space navigation method and system for an underground parking lot, and the method comprises the steps: collecting magnetic field signals in the underground parking lot, RSSI signals of a cellular base station, and motion sensor data of each user during walking through a plurality of users, forming multi-source data, and recognizing the positions of all potential anchor points through a sliding window and a trend analysis algorithm; obtaining a virtual anchor point to construct an undirected graph; performing path reconstruction by using the multi-source data to obtain a walking path of each user and an undirected graph to construct a virtual map so as to determine a parking position of a target vehicle and a current position of a target user; according to the parking position and the current position of the target user, an optimal path from the current position of the target user to the parking position is planned by using an anchor point connection relation in the virtual map, and navigation guidance is generated; by fusing various signal sources, the dependence on fixed infrastructures is reduced, and meanwhile, the positioning precision and reliability are improved, so that a user can accurately navigate to a parking spot in an underground parking lot with signal congestion.

The invention discloses a traffic scheduling method and device for a time-sensitive network and electronic equipment, and relates to the technical field of computer networks and communication. The method comprises the following steps: acquiring a network topology of a time-sensitive network, and abstractly modeling the network topology into an undirected graph which is used for representing all network nodes and all communication links in the network topology; obtaining a traffic demand in the undirected graph; converting the traffic demand and the network topology into mathematical constraints in a satisfiability model theory; based on mathematical constraints, determining a target scheduling scheme on the basis of maximizing the priority satisfaction rate; and performing traffic scheduling according to the target scheduling scheme. According to the invention, the target scheduling scheme is determined by maximizing the priority satisfaction rate, more accurate and efficient network traffic scheduling is realized, and the technical problem that an existing scheduling scheme has obvious insufficiency in the aspect of priority satisfaction of traffic demands in the aspect of coping with complex demands of upper-layer services is solved.

The invention relates to a capacity expansion optimization method, system and equipment based on network performance marginal gain, and belongs to the technical field of network communication. The method comprises the following steps: constructing an undirected graph of communication network topology; defining a service requirement for each pair of nodes in the undirected graph, endowing each edge with a capacity value, and constructing to obtain an MCFP model; on the basis of the Lagrange duality theory, a duality model of the MCFP model is constructed, and a column generation algorithm is adopted to carry out double-model synchronous solution to obtain a maximum concurrency factor, optimal path flow and capacity constraint optimal duality variable; performing network key bottleneck identification according to the capacity constraint optimal dual variable and generating a plurality of capacity expansion candidate schemes; and comprehensively evaluating the capacity expansion candidate scheme by combining the network performance marginal gain, the influence degree of the link capacity change and the implementation cost, and selecting the optimal capacity expansion scheme for implementation. According to the invention, accurate load evaluation, bottleneck quantitative identification and efficient capacity expansion optimization of the communication network can be realized.

The invention relates to a distribution network topology analysis method and system, and the method comprises the steps: carrying out the metadata analysis of a distribution line SVG file, and constructing an undirected graph, a subgraph and a connected subgraph; traversing the sub-graph by using a breadth-first search algorithm with a specified root node as a starting point, and marking a hierarchy for each node according to the distance from the root node to the current node; carrying out edge pruning based on the marked hierarchy, and converting the undirected graph into a directed graph according to a hierarchical relationship; on this basis, distribution transformer-switch relation analysis is carried out to form a basic information table of a switch to which a distribution transformer belongs, and connection line analysis is carried out to obtain a simple line connection path. According to the method, it is ensured that the sub-graphs conform to the radial structure of the power distribution network through hierarchical pruning, connection of the same hierarchy is removed, formation of an annular structure is prevented, detailed line connection paths and simple line connection paths are formed, and contact points between lines can be checked quickly and conveniently.

The invention provides a flower bud and flower recognition and positioning method for a picking robot to perform zoned picking, and belongs to the technical field of picking robots. Inputting the segmented mask into a multi-scale feature fusion semantic recognition model based on hierarchical attention aggregation to perform preliminary classification of foreground flowers and background vegetation, performing a graph cut energy minimization segmentation algorithm on the preliminary segmentation mask to construct an undirected graph, and calculating a minimum cut set to obtain an accurate flower segmentation mask; key point heat map prediction is carried out on separated flower individual masks to extract flower center point coordinates and petal tip coordinates, depth image channel information is combined to calculate three-dimensional space position coordinates of each flower, and a flower three-dimensional positioning database is established for picking robot path planning; the problem of insufficient segmentation precision caused by serious overlapping of foreground flowers and background vegetation in a color space in a flower picking scene is solved.

The invention relates to the technical field of data processing, and discloses a video monitoring intelligent analysis method and system based on edge computing, and the method comprises the steps: generating a sub-region set for each camera in a monitoring region, a time sequence people flow feature vector of each camera, and a people flow abnormal sign of the current real-time people flow; a dynamic weighted undirected graph representing a topological relation of a plurality of sub-regions in a monitoring region is constructed, and then a feature matrix fusing time sequence people flow feature vectors and people flow abnormal marks is input into a sub-region key level probability prediction model based on a space-time diagram convolutional network and the dynamic weighted undirected graph. And the final key level of each sub-region is obtained after prediction and correction, so that edge equipment is controlled to execute frame extraction and video picture analysis of a corresponding frame rate. Therefore, the video analysis precision and the computing power load of edge equipment can be balanced by acquiring the real-time and historical people flow characteristics and the abnormal marks and fusing the physical distance and the topological connectivity between the sub-regions.

The invention provides a node clustering method based on communication network connectivity analysis, and belongs to the technical field of network analysis. The method comprises the following steps: constructing a graph model according to input nodes and a connection relationship thereof, wherein the construction of a graph supports a single network or multi-network relationship; traversing each node in the graph by using a depth-first search algorithm, recording an access path, and classifying the nodes with a communication relationship into the same communication component; and outputting a set of all connected components. According to the method, when the graph is constructed, topology analysis of various network conditions in the undirected graph is supported by dynamically judging the nodes and the neighbor relation of the nodes. The method has the advantages of low calculation complexity and high expansibility, is suitable for the fields of network communication, equipment connectivity check, complex command and control system information transmission modeling and the like, and provides an efficient and accurate solution for connectivity analysis.