1790 results about "Adjacency matrix" patented technology

A method for locating a high-impedance ground fault of a smart distribution network with topology change adaptation includes: acquiring a fault traveling wave sample within a specified time window after a fault occurs, and performing continuous wavelet transform on the fault traveling wave sample to obtain traveling wave full waveform feature information; establishing a graph structure of a power distribution network, obtaining a corresponding adjacency matrix, and obtaining node position and structure encoding information in the graph structure through graph random walk and graph Laplace transform; concatenating the node position, the structure encoding information and the traveling wave full waveform feature information to obtain a node feature, and inputting the node feature and an edge feature into the graph structure to establish a graph sample data set; constructing and training a Graph Transformer model; and calling the trained Graph Transformer model to locate a fault in to-be-detected sample data.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The invention provides a dam safety perception fusion association method based on a multi-modal space-time diagram neural network. The method comprises the following steps: dividing a dam into a plurality of structural units, and mapping various data into a three-dimensional coordinate system; a heterogeneous graph structure is defined, and a dynamic adjacency matrix is calculated based on the real-time stress gradient so as to reflect physical connection, mechanical conduction and geological association relationships among nodes; carrying out fusion modeling on multi-source data in the heterogeneous graph structure by utilizing a multi-modal space-time diagram neural network, constructing a causal inference engine based on an output result of the multi-modal space-time diagram neural network, and updating a three-level modeling system through structural equation modeling, anti-factual inference and dynamic weight to obtain the heterogeneous graph structure. According to the method, the dynamic coupling rule among the dam structure, geology and material states is excavated, cross-modal space-time fusion of manual inspection and sensor monitoring data can be realized, the early recognition capability and early warning accuracy of dam potential safety hazards are improved, and the problems of data islands and insufficient relevance in a traditional monitoring method are effectively solved.

The invention relates to the technical field of respiratory system risk prediction, and provides a respiratory system risk prediction method and system based on a graph neural network, and the method comprises the steps: collecting the multi-modal medical data of a patient, and constructing a multilayer heterogeneous graph based on the multi-modal medical data; constructing a weighted adjacency matrix and a node feature vector through the multi-layer heterogeneous graph; matrix product operation and convolution operation are carried out based on the weighted adjacent matrix and the node feature vector, splicing combination with historical moment state information is carried out, graph state representation is obtained, weighted aggregation of time dimensions is carried out, and time sequence attention features are obtained; performing coding processing based on the clinical examination data to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a risk classifier for classification calculation to obtain a respiratory system risk level prediction result, generating a risk assessment report, and outputting respiratory risk early warning information. The accuracy and clinical practicability of respiratory system risk prediction are improved.

The invention relates to the technical field of electroencephalogram signal processing, and discloses an electroencephalogram emotion recognition method and system based on a deep neural network. The method comprises the following steps: collecting and preprocessing a multi-channel EEG signal; constructing a graph data structure, extracting multi-domain features by taking electroencephalogram channels as nodes, and constructing a self-adaptive dynamic adjacency matrix; constructing a graph convolution long and short-term memory network, learning spatial features by GNN, and extracting time dependence by LSTM; enhancing emotion capture by using a multi-scale time-frequency feature fusion method in combination with STF and CWT; constructing global topological information of an FCN brain extraction region in combination with brain network features; and outputting alertness and other emotion indexes by means of the classification model. According to the method, graph structure learning and time sequence modeling are combined, EEG signal emotion recognition is optimized, and personalized adaptation and emotion recognition accuracy is improved.

The invention discloses an end-to-end fault diagnosis and identification method based on multi-modal fusion, and the method comprises the steps: 1), collecting a vibration signal and an acoustic signal, carrying out the preprocessing, and constructing a training sample set; 2) performing feature extraction to obtain a high-dimensional modal feature vector; 3) generating a sparse adjacency matrix through an end-to-end deep learning graph generation module, and establishing a graph generation structure relation; 4) constructing a multi-receptive field Chebyshev graph convolutional network, and extracting node-level features in a graph generation structure; 5) inputting the structure sensing features into a full-connection layer for mapping, and completing prediction and discrimination of a fault category to which an input sample belongs; performing model supervision training, and optimizing model parameters in an end-to-end mode; and 6) carrying out prediction output on the fault identification model on the test set, and carrying out quantitative evaluation on the fault identification result to obtain the fault identification device.The method belongs to the technical field of equipment operation state monitoring and fault diagnosis, and realizes accurate fault diagnosis of the rotating equipment.

The invention discloses an earth and rockfill dam seepage-deformation early warning method and system based on time-space combined anomaly, and belongs to the field of dam body safety data research. The method comprises the following steps: constructing a spatio-temporal topological graph based on an engineering coordinate system, integrating multi-dimensional data by nodes, and constructing a dynamic adjacency matrix according to spatial distance and seepage relevance; extracting features by using a space-time diagram convolutional network, a self-loop mechanism and cross-layer attention; and executing dual-drive early warning through standard threshold preliminary screening, multi-scale LSTM prediction and a time decay evidence theory. The system comprises a sensor network and an intelligent computing module, and the intelligent computing module has adaptive modeling and visualization functions. According to the scheme, seepage-deformation space-time correlation quantitative analysis is achieved, the hysteresis effect is captured, the threshold value is dynamically corrected, multi-source evidences are fused, the early warning timeliness and accuracy are improved, and the risk of false alarm and missing alarm is reduced.

The invention provides an equipment fault diagnosis method based on multi-scale image convolution, and relates to the technical field of industrial equipment intelligent fault diagnosis, and the method comprises the steps: extracting the time domain, frequency spectrum domain and time-frequency features of a vibration signal through a multi-scale input layer, and generating a 32-dimensional feature vector through the fusion of a cross-scale feature coupling module; and constructing an inter-equipment relation perception graph convolution model, generating a dynamic adjacency matrix in combination with a physical distance and a real-time working condition, and extracting space-time fusion features through space-time convolution. Transient and periodic features are enhanced through an accidental fault sensitive time sequence module, and time sequence features are output in combination with dual-channel fusion and a self-attention mechanism. And finally, fault identification and positioning are realized by adopting a double-threshold detection and equipment comparison enhancement strategy, an interpretable diagnosis evidence chain containing multi-scale feature contribution is generated, and the weak fault detection rate and the diagnosis credibility are improved.

The invention discloses a micro-grid dynamic scheduling method based on deep learning, and the method comprises the steps: fusing industrial Internet of Things collection and GIS positioning, and constructing a multivariable original spatio-temporal data set covering multiple nodes; extracting multi-scale features through multi-resolution wavelets and Fourier transform, combining the multi-scale features with a dynamic adjacency matrix, and realizing feature adaptive distribution and nonlinear dynamic modeling by using multi-scale attention gating, graph convolution and a time sequence neural network model; the micro-grid load and state prediction accuracy, the system generalization ability and the abnormal response level can be effectively improved, and powerful support is provided for intelligent scheduling and abnormal analysis.

The invention belongs to the technical field of artificial intelligence and engineering, and particularly discloses a tunnel deformation prediction method and system based on causal and space-time mixture graph attention, and the method comprises the steps: receiving monitoring data of a tunnel section, carrying out the data preprocessing of the monitoring data, and obtaining a time sequence; fusing the spatial adjacency relation of the monitoring points and the causal analysis result of the time sequence, generating a graph structure containing physical association and causal dependence, and constructing a weighted adjacency matrix in combination with the geological similarity of the monitoring points; inputting the weighted adjacent matrix and the time sequence into the hybrid network model, extracting spatial features and time sequence features, splicing the spatial features and the time features, inputting the spliced features into a full connection layer, and outputting a prediction result; and carrying out interpretability analysis on a prediction result, dynamically adjusting an early warning threshold value based on statistical distribution of prediction errors, and triggering a graded early warning signal for prompting. According to the invention, the prediction precision of tunnel deformation can be improved.

The invention relates to an unmanned aerial vehicle group collaboration and task allocation optimization method and system based on edge computing, in particular to the field of communication, efficient task allocation and threat early warning are achieved through dynamic modeling of a multi-modal sequence prediction model and a heterogeneous relation graph, firstly, real-time environment and historical task data are fused, and the real-time environment and historical task data are fused; generating space threat probability distribution and an environment dynamic coefficient; then, a dynamic adjacency matrix is used for adjusting a subgraph embedding vector, a threat-driven topological structure is reconstructed in real time, a decision-making layer outputs a task instruction and value evaluation based on a hierarchical decision-making network, task acceptance, task abandoning and path selection are intelligently optimized, and task conflicts are solved through a federal consensus mechanism; according to the method, the cooperation efficiency and the task execution accuracy of the unmanned aerial vehicle group in a complex environment are effectively improved, and task allocation and resource use are optimized.

The invention discloses a multi-modal offshore wind power ultra-short-term prediction method in the field of offshore wind power plant cluster power prediction, and aims to solve the technical problems of spatial-temporal feature splitting and insufficient dynamic dependency relationship modeling. The method comprises the steps of performing anomaly detection and restoration on fan data, and generating a corrected wind power cluster data set; extracting a mean value, a standard deviation and a latest value of core operation data of each fan through a dynamic time window, and constructing a multi-dimensional node feature; a static geographic similarity matrix is generated based on geographic coordinates, a basic wake effect matrix is generated in combination with real-time wind direction data, correction is carried out through the maximum mutual information quantization time-delay effect, and then a dynamic adjacency matrix is obtained through self-adaptive fusion; and integrating the multi-dimensional node features and the dynamic adjacency matrix into a space-time diagram sequence data architecture, inputting the space-time diagram sequence data architecture into a multi-scale wake flow perception diagram space-time prediction model, and outputting a multi-fan power prediction value. According to the invention, high-precision multi-fan power prediction can be realized.

The invention discloses a river flow missing data reconstruction method, which comprises the following steps of S1, constructing a river network topological structure, and quantifying hydraulic correlation; s2, spatio-temporal feature fusion and multi-source information extraction; s3, performing multi-task cooperative flow reconstruction and confidence coefficient prediction; s4, dynamic weighting and result correction of meteorological factors; and S5, performing anomaly detection and model dynamic updating. According to the method, a river flow missing data reconstruction method is set, the steps are mutually fused and used, and topology-aware space-time diagram convolution is performed: a river network topology structure is encoded into a weighted adjacency matrix for the first time, space correlation features are extracted through a diagram convolution network, and the problem that a traditional method ignores hydraulic connection is solved; a multi-task collaborative learning mechanism: synchronously outputting a flow reconstruction value and confidence, combining topological smooth constraints, and realizing reconstruction reliability quantification while ensuring precision; and meteorological dynamic weighted correction: dynamically adjusting the node weight based on the real-time rainfall intensity, and accurately adapting to the nonlinear response of the water flow in the heavy rainfall period.

The invention discloses an artificial intelligence abnormity early warning diagnosis method for industrial equipment operation state monitoring, and belongs to the technical field of industrial equipment intelligent monitoring, and the method comprises the steps: collecting a multi-source heterogeneous signal of industrial equipment, and carrying out the time-frequency dual-domain feature extraction; constructing a multi-scale time window based on the time-frequency features, and executing self-supervised contrast learning by injecting a preset abnormal mode to obtain cross-scale contrast feature representation; constructing a dynamic adjacency matrix according to the comparison features, extracting space-time correlation features through a graph attention network, and determining an abnormal score based on joint evaluation; according to the method, a deep coupling closed-loop cooperative system is formed, multi-dimensional state characterization, adaptive anomaly detection and root cause diagnosis are realized, and the problems of single data source and lack of fault analysis capability in the prior art are effectively solved.

The invention provides an electricity load equipment identification method based on spatial-temporal feature fusion and an attention mechanism, and the method comprises the steps: carrying out the dual preprocessing of the data through the real-time collection of the electrical time sequence data, operation state labels, spatial layout information and environment parameters of target equipment, comprising the steps of dynamically aligning a time sequence curve by time warping and adaptively adjusting data distribution in a standardized manner. A spatial correlation graph is constructed by using a graph neural network, and space-time proximity is calculated to generate a weighted adjacency matrix. A multi-head attention mechanism is adopted to fuse electrical time sequence features and spatial topology features, and bidirectional asymmetric attention modulation network optimization feature fusion is constructed. Time and space dimension features are extracted through a time flow module and a space flow module respectively, the features are dynamically weighted and fused through a gating attention unit, and the identification model is input to output the equipment type and the working state. According to the method, the accuracy and robustness of electricity load equipment identification can be improved, and the method has good adaptability and expansibility.

The invention discloses an intelligent automobile interpretable abnormity diagnosis method and system, and relates to the technical field of intelligent traffic. The method comprises the steps of collecting multi-dimensional sensor data based on an intelligent automobile test platform, and constructing a directed causal graph and a causal adjacency matrix which are used for describing a causal relationship between the sensor data; designing a causal constrained graph attention mechanism based on the causal adjacency matrix, and constructing a causal constraint enhanced graph attention anomaly diagnosis model; and based on the directed causal graph and the graph attention anomaly diagnosis model, constructing a hierarchical anomaly diagnosis strategy integrating a feature reconstruction error, a variable causal relationship and a graph attention network weight, positioning an anomaly root cause and identifying a propagation path of the anomaly in the sensor network. According to the invention, the problems of false correlation and lack of exception explanation ability of graph attention network learning in the prior art can be overcome, and reliable exception detection and root cause diagnosis of intelligent automobile multi-sensor data are realized.

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 electric digital data processing, and discloses a multi-dimensional data logic processing method based on an artificial intelligence algorithm, which comprises the following steps: receiving a target discrete data packet, extracting metadata, and calculating to generate a dimension entropy feature vector representing data logic complexity; inputting the vector into a preset topological mapping model, and outputting an initial adjacent matrix; calling a feedback suppression mask matrix generated based on a historical operator utility state, and executing bitwise logic AND operation with the initial adjacent matrix to generate a corrected effective topological matrix; the matrix is analyzed, a logic operator function pointer is dynamically indexed in an instruction cache, and a directed acyclic execution linked list is constructed; according to the method, redundant logic nodes in AI prediction are definitely eliminated through a bit operation mask mechanism based on historical feedback, and deterministic convergence of processing delay and optimal matching of computing power resources are achieved.

The invention discloses a multi-modal large model training data acquisition method and system, and relates to the technical field of data acquisition optimization, and the method comprises the steps: building a causal graph adjacency matrix based on a cleaning alignment data set, carrying out the anti-fact intervention after recognizing a prejudice variable, and generating an anti-fact sample set; combining the anti-fact sample set and the cleaning alignment data set into an enhanced data set; performing cross-modal anti-long-tail compensation on the enhanced data set to obtain a balanced data set; based on the image data and the text data in the balance data set, depth separable convolution feature extraction and BERT semantic coding are carried out respectively, cross-modal fusion is carried out, and fusion features are output; through the steps of depth separable convolution, BERT coding, quantum latent variable evolution and cross-modal semantic verification, the generalization ability, robustness and social adaptability of the multi-modal large model in a complex and real scene are significantly improved.

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 belongs to the technical field of deep learning and time sequence analysis, and particularly relates to a multivariable time sequence prediction method and device based on spatial-temporal feature fusion. The method comprises the following steps: acquiring multivariable traffic time series data, and processing the traffic time series data; dividing the processed traffic time sequence data into overlappable patches, and generating a patch embedding sequence through linear mapping; applying bimodal time attention to the patch embedding sequence to obtain fused attention features; based on the learnable node embedding matrix, generating a time-varying adjacency matrix through dynamic graph construction, executing graph convolution to obtain a time domain graph propagation result, executing fast Fourier transform, multiplying by a learnable scaling factor and then performing inverse transformation to obtain an inverse transformation time frequency result; adding the time domain graph propagation result and the inverse transformation time frequency result to obtain a final spatio-temporal characteristic; and flattening the final spatio-temporal characteristics, and generating predicted values of the variables in a future prediction window through a linear prediction head.

The invention provides a fault diagnosis method and system for a water electrolysis hydrogen production system based on a graph neural network, and belongs to the technical field of water electrolysis hydrogen production, and the method comprises the steps: obtaining key operation parameters of the water electrolysis hydrogen production system; constructing a time-varying dynamic adjacency matrix based on the key operation parameters, wherein the time-varying dynamic adjacency matrix is obtained by weighting and fusing physical topological structure data and multivariate time sequence data through arbitration parameters; inputting the multivariate time sequence data into a time feature extraction module, and extracting time dependent features; inputting the dynamic adjacency matrix into spatial feature extraction module spatial dependency features based on the time dependency features; fusing the time dependency feature and the spatial dependency feature by adopting a time-space gating fusion mechanism to obtain a fused spatial-temporal feature; and a fault classification module is adopted to identify whether the water electrolysis hydrogen production system has a fault and a fault type based on the fusion features. The diagnosis method is high in accuracy and fast in response.

The invention discloses a non-paired single cell multi-omics data gene regulatory network inference method, which comprises the following steps: collecting single cell sequencing non-paired data, and preprocessing the data; inputting the pre-processed single cell sequencing non-paired data into a multi-layer variational auto-encoder structure to generate advanced potential variables and reconstructed single cell sequencing non-paired data; training a layered GAN by adopting a layered adversarial alignment mechanism according to the advanced potential variables and the reconstructed single cell sequencing non-paired data; according to the advanced potential variables, adopting a mutual nearest neighbor strategy to train a mutual nearest neighbor module; fusing the advanced potential variables of different modes by adopting a gating fusion mechanism, constructing a unified advanced potential variable, and completing adaptive feature integration of the multi-mode advanced potential variables; according to prior information, an initial adjacency matrix is established, a gene regulation network is constructed in combination with unified advanced potential variables, and the gene regulation network with biological rationality is directly deduced from non-pairing input.

The invention discloses a blocked pipe section identification method of a drainage pipe network system based on dynamic characteristics, and relates to the technical field of drainage pipe network monitoring. Real-time hydraulic association between pipe network nodes is quantified through a dynamic adjacency matrix to generate a spatial topology matrix, and a time sequence is divided based on a sliding window; a space-time fusion model based on GCN and Transform is constructed to carry out multi-scale dynamic coding, and then a decoder is utilized to reconstruct normal working condition data of a pipe network. By calculating the deviation degree of the pipe section level reconstruction error and the threshold value, the blocked pipe section is accurately recognized, end-to-end modeling from'pipe network topology-drainage time sequence data-external rainfall 'multi-source data to blocked pipe section recognition is achieved, and the technical difficulties of a traditional method in the aspects of dynamic topology modeling, long time sequence dependence capture and multi-modal feature fusion are solved.

The invention discloses a network security data analysis system and method based on artificial intelligence, and relates to the technical field of network security, and the method comprises the steps: constructing a structured triple, mapping the structured triple into graph nodes and edges, and storing the graph nodes and edges in a graph database; extracting graph data from the graph database, and generating a node feature matrix, an adjacent matrix and an edge feature matrix; utilizing a graph attention mechanism to train a node representation vector, and constructing a semantic propagation matrix; obtaining a predicted attack path model; generating a candidate attack path set; constructing a credible scoring model, training and optimizing, and screening high-credibility paths with scores exceeding a threshold value; constructing a multi-layer perceptron model to obtain an attack source prediction model; according to the method, real-time atlas data is obtained, the nodes exceeding the threshold value are marked as risk nodes, the risk nodes are uploaded to a protection system to trigger alarm and check, and the automation and real-time performance of attack source recognition are improved.

The invention provides a tunnel full-section deformation prediction method and system based on structural constraints, and relates to the technical field of tunnel deformation prediction, and the method comprises the steps: obtaining monitoring data of tunnel full-section monitoring nodes; based on the correlation between the geometric distribution of the monitoring nodes and the monitoring data, determining a weight relationship between the monitoring nodes, and constructing an adjacent matrix; considering the stress continuity and symmetry characteristics of the tunnel, setting a structural constraint, introducing the structural constraint into the adjacent matrix, and generating a graph structure with the structural constraint; a graph structure with structural constraint is input into a tunnel full-section prediction model, node features of each time step are extracted through a ChebNet module to obtain a spatial feature sequence, then the spatial feature sequence serves as input of a GRU module, the node features of each time step are traversed step by step to obtain spatial-temporal features, the spatial-temporal features are mapped to a final output layer, and a tunnel full-section prediction model is obtained. And predicting and outputting the tunnel displacement value of each node in the future time step to obtain a tunnel full-section deformation result.

The invention discloses a multi-source heterogeneous engineering monitoring data fusion method based on deep learning, and relates to the technical field of engineering monitoring and artificial intelligence crossing. Collecting multi-source heterogeneous time series data; inputting the multi-source heterogeneous time sequence data into a dynamic space-time alignment module, performing time sequence alignment to obtain alignment data, and extracting time sequence characteristics of the alignment data through a sequence-to-sequence model; constructing a sensor topological graph through an adjacent matrix based on the sensor data, determining node features of the multi-source heterogeneous time series data through a statistical feature method, and inputting the sensor topological graph and the node features into a graph convolutional network for processing to obtain spatial features; inputting the structural crack image sequence data into the residual convolutional network to extract visual semantic features; and dynamically fusing the time sequence features, the spatial features and the visual semantic features through a space-time cross attention mechanism to obtain fused features. According to the method, the compatibility of engineering monitoring data fusion can be improved.

The invention belongs to the technical field of charging pile power distribution, and discloses a charging pile power distribution method and system based on a depth-first algorithm, and the method comprises the steps: building a host power topology adjacency matrix as an initial adjacency matrix; obtaining a host fault state, and optimizing the initial adjacency matrix to obtain a real-time adjacency matrix; when a host fault state or charging gun power demand change is detected, each charging gun is taken as a starting point, the real-time adjacency matrix calculates each charging path and path efficiency through a depth-first algorithm, the charging paths with the path efficiency lower than a preset value are eliminated, weighting priorities of the remaining paths are calculated, and the charging paths are calculated according to the weighting priorities; selecting the charging path with the highest weighting priority as a target path; and performing cross verification on the target path and the parallel contactor path, if verification succeeds, outputting the target path, otherwise, outputting the path successfully verified last time, so that power distribution can be dynamically and flexibly adjusted according to the charging power demand changing in real time.

The invention provides a machine tool thermal error prediction compensation method, device and equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of temperature data and thermal deformation data of different machine tools, and dividing the data into a source domain data set and a target domain data set; a dynamic time warping algorithm is adopted to construct an adjacent matrix between temperature sensors, a time-space diagram neural network model is constructed based on the adjacent matrix, and a source domain data set and a target domain data set are input into the model for cross-domain training to obtain a temperature data completion model; and missing temperature sensor data is input into the completion model for data completion, and thermal error prediction is performed based on the data after completion, so that high-precision thermal error prediction under the condition of temperature sensor data missing is realized. According to the method, the space-time diagram neural network is combined with a cross-domain training technology, so that accurate complementation of sensor data, cross-machine-tool-environment model adaptability and stable thermal error prediction precision are realized.