248 results about "Laplacian matrix" patented technology

The invention discloses a vehicle navigation positioning method and system when a navigation signal is lost, and relates to the technical field of vehicle autonomous localization, and the method comprises the steps: carrying out the tight coupling matching based on a geomagnetic feature map, obtaining the geomagnetic sequence similarity through a sliding window dynamic time warping algorithm, and generating a fusion positioning track; extracting a road network topology connection relation from the high-precision map, constructing a road connection graph Laplacian matrix with a weight, and obtaining a topology constraint correction coefficient through a graph convolutional network in combination with the fusion positioning track; and constructing a joint optimization function based on the topological constraint correction coefficient, and solving an optimal positioning coordinate by adopting an asymmetric Gaussian-Newton iterative algorithm. According to the method, the defects of topology modeling stiffness and non-linear optimization instability of a traditional method are overcome, so that high-precision and high-robustness vehicle continuous positioning is realized in a GNSS signal loss scene.

The invention provides a network traffic data security assessment method and system based on deep learning. The method comprises the following steps: converting original network traffic data into a graph structure data set comprising a topological structure, node attributes and time sequence behaviors; in the process, the time-space fusion input tensor is formed through the association strength between adjacent matrix and Laplacian matrix coding network entities and the fusion of time sequence characteristics extracted by time window slices. Compared with traditional flow analysis which only pays attention to a single protocol or a rate threshold value, the method achieves global relevance expression of network behaviors through graph structure modeling. Through graph structure modeling, multi-dimensional feature fusion and probabilistic risk assessment, the method can adapt to dynamic change of network topology and continuous evolution of an attack mode, so that a final assessment result is more accurate.

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 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 relates to the technical field of multi-view clustering, in particular to a multi-view clustering method and device, and can solve the problem that the overall effect of an existing method in a large-scale clustering task is limited due to the fact that the existing method has problems in the aspects of calculation efficiency, robustness and multi-view information integration to a certain extent. The method comprises the following steps: dynamically learning an anchor matrix and a projection matrix for each view, and constructing a bipartite graph to generate a similarity matrix; calculating a graph Laplacian matrix based on the similarity matrix of each view, and extracting spectrum embedding; the spectrums of multiple views are embedded and stacked into a third-order tensor, and cross-view shared information is extracted by using a low-rank tensor constraint; multi-view atlas embedding is aligned through a spectrum rotation technology, and a discrete clustering indication matrix is directly output.

The invention relates to the technical field of industrial process measurement, and discloses a high-precision liquid level measurement anti-interference calibration method, which comprises the following steps of: 1, synchronously acquiring a liquid level signal, a mechanical vibration signal, an electromagnetic field intensity signal and a temperature distribution signal through a multi-mode sensor array to form multi-physical field data; 2, constructing a hypergraph adjacency tensor based on the coupling relationship of the multi-physics field data, and generating a Laplacian matrix constraint; step 3, performing CP tensor decomposition with hypergraph constraint on an original signal tensor formed by the multi-physical field data, and separating a liquid level signal kernel from an interference kernel; and step 4, inputting the decomposed signal kernel characteristics and the sensor impedance parameters into a depth deterministic strategy gradient algorithm to realize hardware acceleration of a processing flow. A multi-physics field coupling relation is constructed through a hypergraph adjacency tensor and a Laplacian matrix, and the limitation of a traditional binary correlation model is broken through.

The invention relates to the technical field of temperature control, in particular to a heating and ventilation heating power constant-temperature variable-flow control method, and provides the following scheme that circulating pump frequency instructions and water temperature response data of multiple historical regulation and control periods are collected, and a dynamic track of the current water temperature in a temperature inertia field is constructed; calculating an attraction potential energy state between the current water temperature and a target constant temperature value in combination with the disturbance residual matrix; and outputting the frequency bandwidth interval of the circulating pump based on the state to realize dynamic adjustment. An adjacency matrix and a Laplacian matrix are constructed by introducing a hydraulic communication relation, spatial convolution and weighted updating are performed on disturbance residues in combination with a topological diffusion core, and accurate propagation and suppression of disturbance are realized. The method can effectively solve the problems of nonlinear response, delay effect and disturbance coupling of the water temperature to the pump frequency, and the temperature stability and the energy utilization efficiency of a heating system are improved.

The invention discloses a short-term wind speed prediction method for multiple offshore wind power plants, relates to the technical field of power system intellectualization, and constructs a dynamic graph structure fusing the correlation between geographic distance and wind speed according to the correlation between the geographic position of a target area and the wind speed, namely a multi-wind-plant connected graph, and represents the spatial topological relation of a wind power plant group. A wind power plant group is mapped into a node network by constructing a dynamic graph structure fusing geographic distance and wind speed correlation, spatial dependence intensity between nodes is quantized by using a weighted adjacent matrix, spectral domain convolution operation is carried out by a graph convolution network based on a normalized Laplacian matrix, and the spatial dependence intensity between nodes is quantized by using a normalized Laplacian matrix. Efficient neighborhood feature aggregation is achieved through Chebyshev polynomial approximation, complex spatial association caused by geographic position difference and meteorological condition interaction can be accurately captured, the defect of non-Euclidean spatial relationship modeling in a traditional method is overcome, and the representation capacity of the spatial dependency relationship in the multi-wind-power-plant environment is remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle cluster control, in particular to an unmanned aerial vehicle cluster distributed optimal formation control method considering multiplicative noise and a computer readable medium, and the method comprises the following steps: constructing a communication topological graph of an unmanned aerial vehicle cluster, each edge represents a communication relationship between two adjacent unmanned aerial vehicles; constructing a Laplacian matrix of the communication topological graph; establishing an unmanned aerial vehicle kinetic equation considering the multiplicative noise influence; defining an objective function needing to be optimized and constraint conditions; constructing a Hamilton function of the target function; and designing a distributed optimal control strategy by applying a random optimal control theory, setting operation time, system parameters and initial state information of the unmanned aerial vehicle cluster, and obtaining a formation operation track and a final formation state of the unmanned aerial vehicle cluster under optimal control. According to the invention, the global optimality and robustness of the unmanned aerial vehicle cluster control algorithm are ensured.

The invention belongs to the field of energy storage monitoring, and provides an energy storage facility safety early warning protection method and system, and the method comprises the steps: obtaining the temperature, heating rate and equivalent impedance data of each monitoring unit of a battery cluster; constructing a physical adjacency matrix based on the physical space arrangement relation of the monitoring units; fusing the physical adjacency matrix and the electrical adjacency matrix according to a preset weight; constructing a graph structure based on the comprehensive adjacent matrix, and calculating a Laplacian matrix of the graph; calculating a node neighborhood residual error for the temperature and the heating rate based on a Laplacian matrix to obtain a temperature consistency residual error; calculating a node neighborhood residual error for the equivalent impedance to obtain an electrical consistency residual error; calculating a temperature prediction residual error under the time sequence prediction model with graph regularization constraint; and constructing an early abnormal score of the node, adaptively setting a quantile threshold according to the healthy operation data distribution, and carrying out graded early warning judgment on the early abnormal score. The accuracy of safety early warning of the energy storage facility can be improved.

The invention relates to a multi-agent system distributed consistency optimization method considering an unbalanced directed communication graph, and belongs to the field of multi-agent system coordination control. The method comprises the following steps: S1, establishing a first-order dynamic model of a multi-agent system; s2, constructing an unbalanced directed communication topology network of the multi-agent system, and describing a communication relationship between agents through an adjacent matrix; s3, defining a global optimization target to minimize the sum of local cost functions of all agents; s4, constructing a distance-based adaptive precise penalty function, and converting a set constraint optimization problem into a set-free constraint optimization problem; s5, designing an adaptive coupling gain to solve a distributed optimization problem, and designing an auxiliary variable to estimate a left eigenvector corresponding to a zero eigenvalue of the Laplacian matrix; and then updating the state variable and the auxiliary variable of the intelligent agent according to the estimated value, and solving the problem of consistency optimization of the multi-intelligent-agent system of which the cost function cannot be differentiated.

The invention relates to the technical field of natural language processing, and discloses a generated text quality processing method and system based on a large language model, and the method comprises the steps: constructing a logic topological graph and a Laplacian matrix of an original generated text, and extracting a feature value sequence; recognizing a logic bearing wall based on the attention gradient and generating an anti-fact contrast text; calculating a logic collapse index by combining the difference between the map characteristic values of the original text and the contrast text and the logic polarity overturning condition; and judging the quality of the generated text according to the logic collapse index, blocking the text which does not pass the judgment, and shaping and resampling the output probability value of the error position by utilizing the spectrum difference information to generate a new text. The method does not need to depend on an external knowledge base, quantifies the stability of text logic through anti-fact interference and spectrum analysis, and identifies a high-risk illusion text; and a wrong logic path is automatically corrected through Logits shaping, so that the logic self-consistency of the generated content is improved on the premise of ensuring the semantic smoothness of the text language.

The invention relates to the technical field of boxing path planning, and discloses a deep learning-based boxing path planning method and system, and the method comprises the steps: collecting the data of an object to be boxed, and constructing a three-dimensional coordinate parameter; performing spatial topology modeling by using the graph convolutional network to generate a geometric topological graph, and calculating a path density coefficient; carrying out graph Laplacian matrix spectrum decomposition on the path density coefficient to extract a spatial principal component vector, and generating a path planning weight through a genetic algorithm; and dynamically updating the reference path node based on the weight to generate an optimized path, and controlling the motion trail of the mechanical arm. The system comprises a three-dimensional data acquisition module, a topology modeling module, a density coefficient calculation module, a spectral decomposition module, a weight optimization module, a path generation module and a motion control module. According to the method, through combination of deep learning and an intelligent algorithm, complex geometric feature modeling and path optimization are realized, the boxing efficiency, accuracy and system adaptability are improved, and the method is suitable for boxing scenes such as logistics and warehousing.

The invention relates to the technical field of data processing, in particular to a multi-modal sensor data real-time fusion processing method for a smart community. According to the method, a sensor network topological graph is constructed, a connection weight is optimized, distributed clock synchronization is realized by using a graph Laplacian matrix, and clock drift prediction and compensation are performed in combination with a fractional Brownian motion model; performing wavelet transform decomposition on the sensor data after time sequence alignment, calculating each scale Hurst index, predicting a load trend through a fractal prediction model, and outputting an optimal resource allocation scheme through hybrid evolution calculation; the method comprises the following steps: constructing multi-modal sensor data into a graph structure, extracting node features by using a graph convolutional neural network, obtaining global feature representation by using a self-attention mechanism, and performing anomaly detection classification in combination with a resource utilization rate and a prediction error; an anomaly detection feedback mechanism is established, and Laplacian matrix eigenvalues and weight parameters are dynamically adjusted; the real-time performance, the accuracy and the robustness of data fusion processing are improved.

The invention discloses a space-ground integrated micro-grid cluster topology distributed optimization method and system for network risks, and the method comprises the steps: constructing a micro-grid cluster model based on the topological structure information of a micro-grid cluster, and constructing a communication topological optimization model and constraint conditions based on the communication network topological information, the communication topology optimization model is solved, an adjacent matrix and a Laplacian matrix are obtained, a virtual signal is generated according to the reference signal of each distributed power generation unit, a smooth reference trajectory is generated based on the virtual signal, and a decentralized controller model for constructing the micro-grid cluster is determined based on the smooth reference trajectory; a solution result based on the topology optimization model and the decentralized controller model are applied to the microgrid cluster model, so that the communication network topology structure of the microgrid cluster is optimized, the coping capacity of the microgrid cluster to network risk behaviors is improved, the stability of network communication is greatly improved, and the network communication efficiency is improved. And the problem of communication delay caused by a space-ground integrated network is effectively solved.

The invention discloses a graph encoder recommendation method based on guide diffusion model enhancement, and the method comprises the specific steps: S1, initializing a user-article interaction adjacency matrix, carrying out the normalization processing, and generating a Laplacian matrix; s2, adaptively calculating a mask probability for each click behavior, generating a partially visible mask bipartite graph, taking the mask bipartite graph as a mask graph encoder input in a model training process, and enhancing a supervision signal from a structure level; and S3, generating representations of the user and the article by using a mask graph encoder, performing de-noising processing by adding directed noise and introducing a diffusion model guided by a cooperative signal, and enhancing a supervision signal from a semantic level. And S4, generating user and article representations by using a graph encoder, optimizing semantic information of the representations, and finally predicting articles which may be clicked by each user. According to the method, by combining a structure and semantics dual enhancement strategy, the accuracy and recall rate of a recommendation system are remarkably improved, and meanwhile, the robustness of the system to noise information is improved.

The invention discloses a dual-channel label-guided multi-label feature selection method and system, and belongs to a feature engineering technology. The method mainly comprises the steps of obtaining a feature matrix and a positive label matrix of a multi-label data set, performing logic negation on the positive label matrix to generate a mirror image negative label matrix, and constructing a graph Laplacian matrix based on the feature matrix; constructing a multi-label model based on the preprocessed data, wherein an objective function of the multi-label model at least comprises a positive label regression loss item, a negative label regression loss item, a label alignment constraint item, a graph regularization item and a sparse constraint item; constructing an optimization function through relaxation processing constraint and in combination with a Lagrangian multiplier method, iteratively solving the objective function according to a KKT condition, and evaluating feature importance based on a projection matrix for associating features and positive tags after iterative convergence; according to the method, the requirement of multi-label learning for accurate and efficient feature screening is met, label information can be comprehensively utilized, the anti-interference capability is enhanced, and the efficiency is considered.

The invention discloses a dual robust principal component analysis image denoising method based on a graph, and belongs to the technical field of image processing. The method comprises the following steps: flattening an image to be processed to construct a data matrix in a column vector form; constructing a graph structure by using a K nearest neighbor method, and generating a graph Laplacian matrix based on the graph structure; jointly considering an image reconstruction error, a sparse noise item, a linear mapping error item and a graph structure regular item, and constructing an optimization model; and carrying out variable alternating optimization by adopting an augmented Lagrange multiplier method and an alternating direction solution method, and obtaining an image denoising result according to the low-rank principal component. According to the method, image structure information and a double constraint mechanism are introduced into a robust principal component analysis framework, so that the detail retention capability and the structure consistency of the image are effectively enhanced, the robustness and the visual quality of image denoising are improved, and the method is suitable for application scenes such as image processing, video monitoring and target detection under complex backgrounds.

The invention provides a network traffic prediction method and system, computer equipment and a storage medium. The method comprises the following steps: on the basis of a preset multi-scale time domain period, preprocessing to-be-processed network traffic time sequence data of a current prediction period obtained according to a preset sampling duration interval to obtain to-be-analyzed traffic time sequence data of a plurality of time scales and a graph Laplacian matrix; a directed graph adjacency matrix is generated based on a multi-scale fusion graph Laplacian matrix obtained by performing weighted fusion on graph Laplacian matrixes of different time scales by taking the total variation of a minimized graph as an optimization target, and then the flow time sequence data to be analyzed of a preset prediction time scale are analyzed according to the directed graph adjacency matrix and the flow time sequence data to be analyzed of the preset prediction time scale. And obtaining a network traffic prediction value of the current prediction period based on a preset traffic prediction model. According to the method, the real internal relevance of the traffic data can be adaptively learned in a complex dynamic environment based on the time domain periodicity of the traffic, so that the reliability and accuracy of real-time prediction of the network traffic are effectively improved.

The invention discloses a data privacy protection method based on multi-party security computing and a block chain, and the method comprises the following steps: 1, creating a multi-party security computing task request, and distributing the multi-party security computing task request to each participant node; 2, performing Kronecker perturbation coding on local original data, and performing Hash processing through an SHA-256 Hash algorithm to generate a perturbation Hash value; 3, recording access behaviors, and generating an access behavior track sequence; step 4, calculating a Soft-DTW distance between the access behavior track sequence and the historical access track sequence; 5, constructing an adjacent matrix and a Laplacian matrix, and carrying out eigenvalue decomposition; step 6, calculating the Euclidean distance between the access embedding vector and the legal access embedding vector; and 7, generating a result abstract and writing the result abstract into a task recording module. According to the method, the Kronecker perturbation coding and the Hash algorithm are combined, so that the data privacy protection, the calculation transparency and the compliance verification capability are improved.

The invention discloses a transformer area distribution network data sensing method and system based on a space-time diagram neural network, and relates to the field of transformer area distribution networks, and the method comprises the steps: constructing an undirected graph according to an electrical network of a transformer area distribution network, generating an adjacent matrix, and obtaining a Laplacian matrix through normalization processing; constructing a time sequence diagram neural network model based on a Laplacian matrix; the method comprises the following steps: recording input characteristics and state estimation data of transformer area distribution network nodes, constructing label training data, and storing the label training data into a historical database for model training; training a time sequence diagram neural network model based on historical data, and performing distribution network state estimation by using the trained model; the system monitors the state of the power grid in real time through the cloud server and performs supplementary training on the model; the space-time diagram neural network provided by the invention combines the advantages of a diagram neural network and a sequence model, considers the topological structure of the power grid and the association between the nodes, and improves the accuracy of node sensing data.

The invention provides a flexible interconnection device site selection method and system based on distributed resource clustering, and the method comprises the steps: constructing a photovoltaic output feature map Laplacian matrix and a load feature map Laplacian matrix according to the photovoltaic output and load data of a planning region, decomposing the constructed matrixes through DGRNMF, and obtaining a distributed resource cluster; solving a photovoltaic clustering center curve and a load clustering center curve according to a decomposition result through a k-means clustering algorithm, and dividing the photovoltaic clustering center curve and the load clustering center curve into an urban region and a rural region; constructing constraint conditions of the power supply grid segmentation model to obtain the power supply grid segmentation model; and solving the power supply grid segmentation model to obtain an optimal power grid partitioning scheme, and determining a candidate installation position of the flexible interconnection device according to the optimal power grid partitioning scheme. According to the invention, the precision and reliability of the flexible interconnection device site selection planning decision are improved.

The invention provides a non-stationary industrial process monitoring method and system. The method comprises an offline training stage: calculating a time Laplacian matrix and a space Laplacian matrix based on a historical data matrix; constructing an objective function of the stationary subspace analysis method, and adding a time constraint term of a time Laplacian matrix and a space constraint term of a space Laplacian matrix into the objective function; solving the objective function to obtain a stable projection matrix; calculating a stationary component and a monitoring index of each sample in the data matrix X in sequence; determining a control limit by using a kernel density estimation method; an online monitoring stage: based on the real-time operation data x, calculating a stationary component of the real-time operation data x and a corresponding real-time monitoring index according to the stationary projection matrix, and if the real-time monitoring index is greater than a control limit, judging that the operation of the non-stationary process has a fault; the monitoring accuracy can be improved.

The invention discloses a distributed photovoltaic cluster elastic grid division method based on space-time dynamic association. The method comprises the following steps: constructing a space-time high-dimensional feature tensor for describing the running state of a power distribution network; a comprehensive space-time incidence matrix is obtained; a Laplacian matrix reflecting source-load interaction characteristics is constructed; performing characteristic decomposition and dimension reduction mapping on the Laplacian matrix to obtain initial sub-grids of a plurality of distributed photovoltaic clusters; verifying whether the frequency change rate caused by the maximum power shortage in the isolated island operation mode is out of limit or not; calculating the critical clearing time of the system through time domain simulation, and taking the critical clearing time as a quantitative index of transient stability; and calculating a migration priority index of the boundary node, carrying out iterative updating until the whole network meets the multi-dimensional constraint, and outputting a final division scheme. The method can adapt to the spatial-temporal fluctuation characteristics of high-proportion distributed photovoltaic, effectively guarantees the frequency safety and transient stability of the power distribution network, and achieves the dynamic balance of physical topology, spatial-temporal association and safe operation.

The invention relates to the field of computer software, and discloses an open source software code security and compliance management and control method which comprises the following steps: acquiring security vulnerabilities, licenses, version information and dependency relationship data of an open source component, constructing a component-characteristic matrix based on the data and performing dimension reduction processing, predicting a security vulnerability risk and a compliance risk of the component; key components and potential vulnerability propagation paths are identified by constructing a dependency graph and calculating a Laplacian matrix in combination with eigenvalue analysis; and finally, generating a security vulnerability repair suggestion and a compliance review report, helping a development team to preferentially process high-risk problems, and ensuring that the project meets the requirements of related licenses. According to the method, the bug repairing process can be optimized, the safety and compliance of open source software are improved, and the maintainability of the system is improved.

The invention discloses a brain network quantitative analysis method based on near-infrared signal time sequence dynamic graph Fourier transform, which comprises the following steps: constructing a brain network based on fNIRS signals, optimizing time sequence alignment by using a dynamic time warping algorithm, and dividing dynamic network time periods by combining adaptive K-Means clustering with an elbow rule; a neighbor topology overlapping coefficient and feature vector centrality analysis are introduced, and a time sequence dynamic graph is constructed to quantify the connection stability between node layers; a time sequence dynamic graph Fourier transform method is provided, brain function signals are mapped to a time-space-frequency three-dimensional joint domain through spectral decomposition of a time-varying graph Laplacian matrix, and a dynamic spectrogram is generated to analyze a multi-scale spatio-temporal evolution mode; a filter is designed to separate low-frequency (global coordination) and high-frequency (local mutation) components, and a function-structure dynamic constraint model is established. According to the method, an efficient and accurate quantification tool is provided for revealing a brain network dynamic recombination mechanism and neural adaptability changes.

The invention discloses an impedance mismatch design method for a gas turbine combustion chamber multi-flame-tube cross-flame system, which comprises the following steps: forming a combustion chamber by 12 flame tubes in a gas turbine through cross-flame tubes on the basis of a graph theory, modeling as a cyclic graph, and constructing an adjacent matrix and a Laplacian matrix; constructing a system matrix based on the angular frequency of the flame tube and the coupling strength of the cross flame tube; calculating a characteristic value and a characteristic vector of a system matrix, determining an oscillation frequency and a vibration mode, and identifying a degenerate mode; by modifying the coupling strength of at least one cross flame tube and recalculating the eigenvalue and eigenvector of the system matrix, the degenerate frequency is split, and the symmetry is broken; and according to the frequency and the mode after splitting, parameters of the combustion chamber are adjusted to optimize the combustion stability. Modeling of a complex system is simplified based on a graph theory method, the calculation cost is reduced, the symmetry of the system is broken by modifying the coupling strength of the cross flame tubes, the degenerate frequency is split, and combustion instability is relieved.

The invention provides a high-order multi-agent event triggering predefined time consensus control method under a spoofing attack, which comprises the following steps: modeling the spoofing attack as an unknown bounded nonlinear disturbance signal, and non-linearly coupling the nonlinear disturbance signal with a system state through a time-varying coefficient; through a distributed predefined time state observer, the real state of the agent masked by the attack is estimated in real time through neighborhood information interaction, and an attack signal is separated; dynamically adjusting control parameters through an adaptive law based on observer output and post-attack state difference so as to compensate state deviation caused by the attack; each agent is provided with a local control law, the input of the local control law is an observer estimation state of the agent and a neighbor, an acceleration item is introduced into the control law, and a preset convergence time upper bound is realized through preset parameters; parameters of the control law are updated based on local adjacency information of a Laplacian matrix, and distributed consensus tracking is completed only depending on states of adjacent agents.

The invention relates to the technical field of remote sensing image processing, in particular to a multi-temporal remote sensing image change detection method and system based on a space-time diagram neural network, and the method comprises the steps: carrying out the geometric registration, radiation correction and superpixel segmentation of an image, and outputting a segmented superpixel region set; taking the superpixel of each time phase as a node, constructing a space-time diagram structure fusing the spatial adjacency relation and the multi-step time association, and generating a normalized space-time Laplacian matrix; spatial structure features and multi-temporal evolution features are extracted through a double-branch graph neural network, spatial branch and time branch output are fused based on a gating mechanism, and node embedding is generated; constructing positive and negative sample pairs based on node embedding, and optimizing a feature space by comparing a loss function; and calculating the Euclidean distance of node embedding at adjacent moments, and outputting a change detection result in combination with a dynamic threshold. The method effectively reduces the false alarm rate and omission rate, greatly improves the reasoning speed, and is suitable for the scenes of urban expansion monitoring, dynamic disaster evaluation and the like.

The invention provides a hypergraph-based weighted gene co-expression hypernetwork module identification method and system, and the method comprises the steps: obtaining a gene expression profile matrix, and integrating an effective gene set of each sample to construct a weighted hypergraph; calculating a Laplacian matrix of the weighted hypergraph, and obtaining a topological overlapping matrix based on the Laplacian matrix; and based on the topological overlapping matrix, performing module identification on the gene by adopting a hierarchical clustering method, and outputting a gene co-expression module. According to the method, the calculation efficiency is effectively improved by optimizing hypergraph construction and utilizing the hypergraph Laplacian matrix to carry out network analysis. Compared with a traditional WGCNA method, the WGCHNA shows higher calculation speed and processing capacity when processing a large-scale gene data set, so that the gene co-expression network can be analyzed more quickly, and the requirements of multiple samples and complex networks are met.