74 results about "Residual graph" patented technology

The invention discloses a crop growth analysis system based on machine vision, and relates to the technical field of agricultural intelligent monitoring and analysis, the system comprises a multi-source acquisition module, a disturbance identification module, a growth behavior modeling module, an environmental adaptability correction module and a feedback prediction module; wherein the multi-source acquisition module can synchronously acquire visible light, near-infrared and depth images, and generates a joint index in combination with environmental data; the disturbance identification module identifies abnormal areas such as wind disturbance and shielding based on the residual image and the image structure network; the growth behavior modeling module is used for extracting internode change, leaf surface tension and bifurcation angle characteristics by using a three-dimensional structure time sequence alignment and hidden change rate coding network; the environment adaptability correction module constructs a crop-environment double-domain mapping relation; the feedback prediction module outputs personalized intervention suggestions in combination with deviation analysis and crop variety embedding vectors; the system has the advantages of high modeling precision, strong adaptability, fast feedback response and the like, and is suitable for intelligent planting management of various crops.

The invention relates to the technical field of fault detection, and particularly discloses a prediction reconstruction framework causal perception space-time network for explaining anomaly monitoring in a complex industrial process, comprising the following steps: S01, constructing graph data E (V) and a causal graph; automatically adjusting the fusion proportion of the time-frequency characteristics according to the data characteristics so as to ensure that the model can comprehensively capture the information of the data in the time domain and the frequency domain; secondly, introducing a residual image attention network (RGAT), and converting the image data E (V) into image structure data G (S (V), E (V)); and S03, reconstructing a prediction error by adopting a variational automatic encoder (VAE), learning an error mode of normal data, providing an anomaly judgment AD (V) for anomaly detection, analyzing a causal relationship between data in combination with a causal graph, and positioning an anomaly reason according to an anomaly score, so as to form a prediction result. The network solves the problem that a traditional monitoring network is high in false alarm rate.

The invention provides an scRNA-seq data clustering method, system and device based on ZINB distribution and graph attention. The scRNA-seq data clustering system mainly comprises three core modules: a ZINB auto-encoder, which is used for modeling scRNA-seq data based on zero-expansion negative binomial distribution, generating robust potential representation through a denoising auto-encoder, and accurately capturing sparsity, excessive discreteness and shedding events of gene expression; the residual image attention auto-encoder is used for constructing a cell relation graph by using a Pearson correlation coefficient, dynamically learning a neighborhood weight in combination with a multi-head attention mechanism, retaining original features through residual connection, and relieving the excessive smoothness problem of image convolution; and a deep clustering model is self-optimized: target distribution and soft label distribution are minimized through KL divergence, and end-to-end joint optimization of embedded learning and clustering is realized.

The invention discloses a cross-border e-commerce abnormal transaction behavior risk prediction method based on a graph neural network, and the method comprises the steps: synchronously collecting multi-source data of a cross-border e-commerce platform, and writing the multi-source data into a data buffer region according to a unified event timestamp; mapping the multi-source data into a heterogeneous dynamic transaction graph; in the current time window, performing structure comparison on the historical transaction graph and the heterogeneous dynamic transaction graph to generate a structure difference sub-graph, and performing normalization processing to obtain a residual graph; splicing the heterogeneous dynamic transaction graph and the residual graph, inputting the spliced graph into a time sequence graph neural network, obtaining a risk embedding vector of a target transaction sub-graph, and calculating an abnormal score through a mapping function; and comparing the abnormal score with a first threshold value, outputting a risk level of the corresponding transaction and triggering early warning. The cross-border e-commerce abnormal transaction behavior risk can be quickly and accurately identified and predicted, the active identification and real-time early warning of the abnormal transaction risk can be realized, and the real-time performance and accuracy of the risk management of the cross-border e-commerce platform can be greatly improved.

The invention relates to the technical field of water quality parameter monitoring. The invention provides a water quality multi-parameter prediction method and model based on a space-time diagram convolutional network. On the time dimension, a mask code transformer module is designed, a pre-training task is self-supervised through a random mask to enable the model to extract trend features from long-period data, and the problem of response lag of a traditional model to a sudden change event is solved in combination with an accumulative effect of capturing water quality features through expansion causal convolution. And in the spatial dimension, constructing a dynamic graph structure learning module, and fusing the predefined adjacency matrix based on the station physical distance and the dynamic residual graph to generate a dynamic graph structure. Experimental results show that compared with other water quality space-time prediction models, the model has better prediction precision, and prediction R2 of all water quality indexes can reach 93% or above.

The invention provides a network information tracing method and device based on a graph neural network, and belongs to the technical field of information data processing, and the method comprises the steps: obtaining original infectious disease data; performing first linear transformation on the original infectious disease data, performing node embedding according to a first linear transformation result and the adjacent matrix, and performing time dynamic embedding according to a node embedding result to obtain embedded data; inputting the embedded data into the residual image convolutional network to obtain features of the embedded data; and inputting the characteristics of the embedded data into a bidirectional gating loop unit to obtain a first result, and taking the first result as a network information traceability result. According to the method, only the network structure of the original infectious disease data and the infection data at some moments are needed, the node state at each moment does not need to be traced, and the traceability accuracy is improved.

The invention discloses a hydro-generator abnormal state identification method and device based on machine learning, and a medium. The method comprises the following steps: constructing a time synchronization multi-source heterogeneous monitoring signal diagram data structure; generating a real-time dynamic graph data set; configuring a multi-scale residual image convolutional network model, and obtaining an initial model parameter set and an initial deep feature representation set; constructing a squirrel search algorithm adaptive optimization model, and forming an optimized multi-scale residual image convolutional network model; inputting a multi-source heterogeneous monitoring signal set acquired on line into a graph construction module, generating a current real-time dynamic graph data instance, and uploading the current real-time dynamic graph data instance to a cloud end; and reasoning the current real-time dynamic graph data instance in the cloud by using the optimized multi-scale residual graph convolutional network model, and outputting an asymmetric fault recognition result set. The intelligent level and the reliability level of intelligent operation and maintenance of the hydraulic generator are improved.

The invention discloses a new energy charging SaaS operation management method based on artificial intelligence. The method comprises the steps of collecting new energy charging facility data and generating a structured data set; constructing a charging state map to form a map structure sequence; inputting an improved space-time residual image convolutional network, and outputting an embedded vector; a price strategy is generated by combining a multi-source information input condition variational auto-encoder; inputting the equipment state embedded vector and the operation sequence into an improved Transform model, identifying the health state of the equipment and generating a maintenance suggestion; and packaging a final result into a service module, deploying the service module to an SaaS platform, and providing multi-end calling. The new energy charging SaaS operation management method is realized by fusing an improved space-time residual image convolutional network and introducing a Transform structure of a gating fusion mechanism.

The invention provides a power system unit combination method and system. The method comprises the following steps: training a pre-constructed mixed integer programming model through a pre-constructed graph data sample set and a residual graph convolutional neural network to obtain a trained graph neural network, and forming a mapping relationship between input data features of the graph neural network and data labels of graph data samples in the graph data sample set; explaining the trained graph neural network through the mapping relation and a graph neural network interpreter based on mask learning to obtain an interpretable graph neural network; converting the mixed integer programming model into a linear programming model through a unit start-stop strategy output by the interpretable graph neural network; and according to a solving result obtained by solving the linear programming model, selecting and outputting an optimal solution of the linear programming model or outputting an optimal solution of the mixed integer programming model as a unit commitment scheduling result. According to the invention, the credibility of the unit commitment scheduling decision is improved.

The invention relates to an autism classification method based on a multi-scale residual image neural network, and aims to cope with the challenge of crowd autism classification in multi-modal medical data. The method comprises the following steps: firstly, providing a new function connection feature construction method, extracting second-order function connection features by using tangent Pearson embedding to capture a high-order interaction relationship between brain intervals, and then adopting a maximum independent domain to adaptively minimize statistical dependence between the features and acquisition sites, and combining F-score to screen the features with the most discriminative ability, so as to obtain the feature with the most discriminative ability. And redundancy is effectively removed. Secondly, a multi-modal edge weight calculation method fusing imaging information and non-imaging information is provided, so that noise interference is effectively suppressed while a key discriminant relation is reserved. And finally, expanding a node receptive field layer by layer by stacking multiple layers of Chebyshev convolutions with residual errors on the subject graph so as to capture multi-level relation characteristics, and performing weighted modeling and adaptive fusion on convolution output of each layer by using a multi-head self-attention mechanism, so that effective integration of multi-scale information is realized, and accurate classification of autism is realized. The method has excellent performance in the aspect of autism classification, and an innovative, feasible and effective solution is provided for solving the autism classification task in the multi-modal medical data.

The invention discloses a water quality prediction method based on fine tuning large language model knowledge driving, and belongs to the crossing field of artificial intelligence technology and environmental discipline. According to the method, a large language model is finely adjusted according to a predefined prompt template through professional knowledge in the water environment field, a knowledge extraction model suitable for the water environment field is formed, and a quintuple is extracted from a text to construct a water environment knowledge graph. Performing graph embedding learning on entities in the knowledge graph by adopting a GloVe model and a residual image attention network, and calculating correlation among water quality indexes through cosine similarity to obtain a knowledge domain correlation weight matrix; meanwhile, the Pearson's correlation coefficient, the weighted grey correlation degree analysis and the dynamic time warping correlation similarity coefficient are used for respectively calculating linear and nonlinear correlation among the water quality indexes, and a data field correlation weight matrix is obtained; and finally, constructing a KG-DW-CLA water quality prediction model jointly driven by knowledge and data to perform water quality prediction. According to the method, the problems that a traditional model is insufficient in domain knowledge utilization and single in data correlation modeling are solved, the accuracy and scientificity of water quality prediction are improved, and the method has high generalization ability and application value.

The invention discloses a geometric topology fusion-based part assembly method, which comprises the following steps of: acquiring boundary representation models of at least two to-be-assembled CAD parts, and converting the boundary representation model of each CAD part into a structured heterogeneous geometric topology graph; performing feature coding on each node in the heterogeneous geometric topological graph to form node feature representation; inputting the node feature representation into a graph attention reasoning module, and obtaining a node embedding representation representing a geometrical relationship and a topological relationship in the CAD part through multi-layer residual graph structure information propagation and feature aggregation; based on the node embedding representations of the different parts, calculating association scores of node pairs among the different parts; and performing supervised training on the feature coding module and the graph attention reasoning module by using the labeled assembly constraint sample data. According to the method, fine geometric features can be extracted from a boundary representation B-Rep model, and a complex topological dependency relationship is inferred, so that robust assembly constraint inference is realized.

The invention discloses a training method and a fault diagnosis method of a solid oxide fuel cell system multi-fault diagnosis model based on a graph neural network, and the method comprises the steps: obtaining multi-source time sequence operation data of an SOFC system in different operation states, and obtaining a data set; the operation state comprises a normal state, a single fault state and a composite fault state, and the data set is marked with an operation state label; preprocessing the sample data in the data set, generating a node feature matrix and a corresponding adjacent matrix according to each piece of sample data, and forming a graph structure data set; constructing a graph neural network model which sequentially comprises a multi-head graph attention layer, a residual graph convolution layer and a global pooling classification layer, then taking graph structure data in the graph structure data set as input, taking a corresponding operation state as output, and training the graph neural network model to obtain a trained multi-fault diagnosis model; the method can be used for multi-fault decoupling diagnosis of the SOFC system, and diagnosis accuracy and practicability are improved.

The application discloses an electroencephalogram epilepsy detection method and system based on a node adaptive graph neural network, relates to a computer system based on a biological model, and is proposed in view of problems such as fixed graph structure in the prior art. The method comprises the following steps: an electroencephalogram signal acquisition and preprocessing step; mixed EEG graph construction; adaptive residual graph optimization; node-specific diffusion convolution; time sequence feature modeling; and classification output. The system comprises a data acquisition module, a mixed graph construction module, an adaptive graph construction module, a node-specific convolution module, a time sequence modeling module, and a classification output module; when the system is running, the method steps are executed, so that the electroencephalogram epilepsy detection function based on the node adaptive graph neural network is realized. Technical advantages include: (1) graph structure self-learning; (2) brain region personalized modeling, and strengthened regional feature expression; and (3) combined space-time dependence modeling, and complete description of the epilepsy dynamic process.

The embodiment of the invention provides an unmanned aerial vehicle group network self-healing method and device based on graph learning, and relates to the field of unmanned system communication and control. The method is used for solving the problems that an existing unmanned aerial vehicle group self-healing method is large in power adjustment limitation, a moving strategy has defects, and a graph learning method faces topological imbalance and is insufficient in response and generalization ability. According to the method, through a hierarchical residual graph topology reconstruction strategy, the problems of message passing unevaluation and excessive node aggregation occurring in the unmanned aerial vehicle relocation process are effectively relieved, it is ensured that the recovered network topology structure is more balanced and stable, and the overall elasticity and survivability of the network are improved; by introducing task semantic embedding, dynamic diffusion intensity and implicit multi-branch adaptive aggregation, the model can dynamically perceive and adaptively respond to damage scenes distributed by different nodes, and the response efficiency and cross-scene applicability of a self-healing algorithm are improved. By designing a differentiable joint loss function, reasonable constraint and stable gradient guidance are provided for optimization of a graph learning algorithm.

The application relates to the technical field of semiconductor manufacturing, and discloses a wafer defect detection method, a wafer defect detection device and a medium. The method comprises the following steps: performing image processing on a target wafer image of a wafer object to be detected to obtain a plurality of local spectrum regions; performing complex filtering processing on the local spectrum regions in a plurality of scales and a plurality of directions to obtain response information of each local spectrum region under different scales and different directions; determining a phase consistency residual graph, a frequency band leakage residual graph and a direction offset residual graph based on phase responses and energy responses in the response information, a pre-constructed spectrum phase reference field, a main frequency band and a main direction; performing multi-scale residual back projection processing on the target wafer image to obtain a multi-scale residual back projection graph; generating a defect saliency graph based on any one or several of the phase consistency residual graph, the frequency band leakage residual graph, the direction offset residual graph and the multi-scale residual back projection graph; and performing defect detection based on the defect saliency graph to obtain a target detection result.

The present application discloses a training method, a road condition prediction method and a device for a road condition prediction model. By performing data transformation on a historical road condition training set, a plurality of target graph data are obtained, and a preset residual graph convolutional model is used to extract features from the plurality of target graph data to obtain a plurality of residual graphs, so as to capture data features with directional correlation by using the spatial dependence relationship of the graph data, in order to combine the time correlation features of the historical data in a plurality of time periods; the plurality of residual graphs are fused and fully connected to output a visual road condition prediction value, and according to the visual road condition prediction value, a loss function of the residual graph convolutional model is calculated, and the model parameters of the residual graph convolutional model are updated based on the loss function until the residual graph convolutional model reaches a preset convergence condition, thereby obtaining a road condition prediction model, so as to make full use of global features such as time and space, construct a prediction model with visual road condition features, enrich the model prediction results and improve the model prediction accuracy.

The application discloses an annual cloud coverage prediction method and device based on a graph neural network and monthly features, and belongs to the technical field of remote sensing information intelligent processing, atmospheric science and deep learning. The method comprises the following steps: acquiring historical cloud coverage data of a geographical sampling point, and constructing a 12-dimensional monthly feature vector as an input feature for each sampling point; constructing an adjacency matrix representing the topological relationship of a space according to the geographical coordinates of the sampling point and based on a spherical distance and a K nearest neighbor algorithm; inputting the input feature and the adjacency matrix into a pre-trained spatio-temporal graph neural network model, and outputting a daily cloud coverage grade prediction result of a target year; and the model is composed of a cascaded residual graph convolution network module, a bidirectional long short-term memory network module, a multi-head attention module and a plurality of independent monthly classifiers. The application realizes annual scale cloud coverage prediction only by relying on historical data, and has significant precision advantages and generalization capabilities in complex terrain areas and scenes with insufficient historical data.

The application provides a direction of arrival determination method, device, electronic equipment and storage medium, wherein the direction of arrival determination method comprises: obtaining a vector covariance matrix of a snapshot data of a to-be-tested signal based on the snapshot data; obtaining a noise residual graph based on the vector covariance matrix; obtaining a de-noised covariance feature based on the noise residual graph and the vector covariance matrix; and determining the direction of arrival of the to-be-tested signal based on the de-noised covariance feature. The application can improve the accuracy of direction of arrival estimation based on a noise residual graph.

The invention discloses an image compression method oriented to key point detection optimization, and aims to solve the problem that downstream task performance cannot be ensured at a low code rate in traditional compression. The method comprises the following steps: generating a track main code stream and a skeleton diagram from an original image frame through attitude token extraction and track parameterization; applying a reversible skeleton to render the track main code stream and the skeleton graph so as to synthesize a basic reconstruction image and a skeleton rendering state; calculating a residual image between the original image frame and the basic reconstruction image; coding the residual image based on task hard constraints derived from a skeleton rendering state and a key point similarity threshold, and generating an auxiliary residual code stream and a coding sequence strategy; and outputting the track main code stream and the auxiliary residual code stream. According to the method, the motion structure and the detail residual error are separated, and the task hard constraint is introduced to carry out code rate allocation, so that the performance of the key point detection task can reach the preset threshold value under the condition of low bit rate.

Embodiments of the present application provide an image tampering detection method and device. The method comprises: performing noise extraction processing on a target image to obtain a noise residual graph. The noise residual graph is divided into N reference subgraphs. For any reference subgraph, the tampering score maximum subgraph is determined by a tampering score model; the tampering score maximum subgraph is one of the reference subgraph and the changed subgraph of the reference subgraph; the changed subgraph is a subgraph obtained by expanding or reducing the area of the reference subgraph in the noise residual graph. The tampering region of the target image is determined by the N tampering score maximum subgraphs corresponding to the N reference subgraphs. The area of the changed subgraph is continuously changed adaptively by the score of the tampering score model. If there is a tampering trace in the subgraphs obtained in this way, the relative proportion of small size tampering traces in the subgraph is the highest, which can effectively avoid the omission of small size tampering traces. In this way, the recognition rate of small size tampering traces is improved.

The application discloses a kind of multi-task cell analysis method and system based on residual graph neural network, comprising: normalizing single-cell transcriptome data, selecting the top 2000 genes with the highest transcription level from the normalized data, obtaining new single-cell transcriptome data;According to the new single-cell transcriptome data, construct and train denoising auto-encoder, reduce the dimension of original single-cell transcriptome data, obtain the feature representation after dimension reduction;Using the feature representation after dimension reduction constructs adjacency matrix, constructs residual graph neural network model;Connecting the graph neural network model with the denoising auto-encoder, construct double self-supervised model and train;According to the double self-supervised model, output the clustering result, interpolation result and low-dimensional representation of single-cell transcriptome data.The method and system provided by the application greatly improve the feature discrimination of network extraction, improve the performance of each single-cell analysis task.

The invention relates to the technical field of hybrid electric vehicles, in particular to a hybrid electric vehicle type generator power supply system and method, and the system comprises a multi-source data processing module, a driving condition prediction module, an efficiency solving module and a database. The multi-source data processing module collects working condition, energy state and environment data, and obtains clean data through a variational mode decomposition and wavelet threshold denoising combined algorithm; the driving condition prediction module constructs a dynamic graph based on a residual image neural network, integrates adjacent vehicle motion and gradient influence prediction working conditions, constructs a first optimization function in combination with hybrid electric vehicle performance requirements, and generates target power instructions of an engine and a generator set through an optimization algorithm. And the efficiency solving module is used for solving a second optimization function taking the maximum total efficiency of the system as a target and outputting the optimal torque and the rotating speed set value of the engine in real time. The efficiency and working condition adaptability of the power generation system can be effectively improved, the fuel economy is optimized, and the service life of the battery is prolonged.

The application discloses a kind of based on graph neural network's solid oxide fuel cell system multi-fault diagnosis model training method and fault diagnosis method, obtains the multi-source time series operation data of SOFC system under different operating conditions, obtains data set;Operating condition includes normal state, single fault state and compound fault state, and data set is labeled with operating condition label;Sample data in data set is preprocessed, node feature matrix and corresponding adjacency matrix are generated according to each sample data, and graph structure data set is formed;Graph neural network model including multi-head graph attention layer, residual graph convolution layer and global pooling classification layer in turn is constructed, then the graph structure data in graph structure data set is as input, corresponding operating condition is as output, graph neural network model is trained, i.e. the multi-fault diagnosis model trained is obtained, and can be used for the multi-fault decoupling diagnosis of SOFC system, improves diagnostic accuracy and practicality.

This application relates to a spatial geographic data processing method and apparatus. The method includes: acquiring spatial geographic data corresponding to dynamic geographic entities, the spatial geographic data including location change sequences, attribute label sequences, and external event records; performing cross-domain nested encoding processing on the spatial geographic data to obtain a multi-domain entity relationship encoding set with multi-dimensional unstructured temporal features and heterogeneous semantic relationship dimensions; in the multi-domain entity relationship encoding set, performing residual graph generation processing on the temporal nodes of geographic entities exhibiting label drift and state jumps to obtain a set of residual graphs spanning temporal periods; and performing semantic transition compression processing on unstructured deterministic regions based on the residual graph set to obtain a semantic chain set, which is used to characterize the spatiotemporal semantic reconstruction expression model of dynamic geographic entities in a multi-stage evolution process. This method can compress and model the state mutation patterns of dynamic geographic entities in unstructured deterministic regions.

The invention discloses a disaster recovery redundant link optimization method and device for a smart power grid dispatching communication network, and the method comprises the steps: firstly obtaining the topology and link attributes of the dispatching communication network, mapping the time delay, rental or bandwidth and other indexes into weights, calculating a shortest main path from a source dispatching center to a target substation on an original graph, and carrying out the calculation of the shortest main path from the source dispatching center to the target substation; constructing a split residual image based on the path, and reserving a cost bit of a shared link in the split residual image; a candidate standby path with the shared link number not exceeding a threshold value is obtained through a limited shortest path algorithm, a final redundant path pair is generated in combination with the main path, the total weight of the final redundant path pair is calculated according to a union set, and the weight of the shared link is metered only once; and finally, issuing the obtained main and standby paths to electric power communication network equipment to realize rapid switching when the link fails. According to the method, the construction and operation and maintenance cost is reduced while the reliability is ensured, the resource utilization rate is improved, and good deployment feasibility and near-linear time complexity performance are achieved.

This invention discloses a real-time early warning method and system for road maintenance facilities, belonging to the field of intelligent transportation technology. The method includes: constructing a facility graph model based on dual rules of physical proximity and functional dependency; introducing a dynamic weight correction mechanism to generate a dynamic weighted adjacency matrix; real-time acquisition of multi-source sensor time-series data, which, after preprocessing, is input into a time-series graph neural network; extracting spatiotemporal features of nodes through spatial feature aggregation and multi-scale temporal evolution modeling; mapping the final hidden state to a future anomaly probability sequence; dynamically adjusting the early warning threshold based on the number of historical false alarms, and comparing it with the predicted probability to trigger tiered early warnings. This invention solves the problems of difficult identification of clustered anomalies and delayed early warning response in complex environments by quantifying the implicit functional coupling relationships between facilities and fusing residual graph convolution and multi-scale dilated convolution into a TGNN architecture, achieving high-precision, adaptive proactive early warning of road facility anomalies.

The application claims a herbal medicine recommendation method based on self-supervised contrast learning and semantic enhancement. First, the Word2vec word embedding algorithm is used to combine the semantic information of symptoms and herbal medicines to train the feature representation of symptoms and herbal medicines. Then, the symptom-herbal medicine bipartite graph, symptom-symptom collaborative graph and herbal medicine-herbal medicine collaborative graph structure are constructed to capture different types of relationships between symptoms and herbal medicines. The residual graph convolutional neural network is used to aggregate the features of neighboring nodes to update the representation of the current node, and the enhanced subgraph is generated by randomly discarding nodes or edges of the collaborative graph and the bipartite graph. After encoding these subgraphs, the self-supervised contrast learning method is used to calculate the contrast loss between nodes. Then, the information from different graphs is fused to calculate the treatment score of herbal medicines on the symptom set. Finally, the adaptive optimization algorithm is used to adjust the gradient of the model by combining the cross-entropy loss, Topk loss and contrast loss, which improves the accuracy and reliability of herbal medicine recommendation.

The application discloses a method for evaluating the quality of UHD images and videos based on graph convolution and space-time correlation, which comprises the following steps: dividing an input ultra-high-definition image according to a preset grid, and obtaining node features with uniform dimensions through linear mapping. Based on the spatial distance between the normalized central coordinates, the node features and a normalized adjacency matrix are input into a multi-layer residual graph convolution network for feature propagation to obtain node representation. The node representation is subjected to attention weighted pooling to obtain a standardized quality prediction value. The quality prediction value is subjected to learnable affine calibration, and is subjected to inverse standardization according to statistical parameters of quality scores of a training set to obtain a final image quality score. The method can adapt to the high-dimensional input characteristics of ultra-high-definition images, and can combine the time sequence correlation between video frames to realize a high-precision and high-time-efficiency method for evaluating the quality of ultra-high-definition images and videos without reference.

The invention relates to an infantile autism classification method based on a graph attention mechanism and an adaptive multi-modal graph convolutional network, which utilizes image data and non-image data to explore early diagnosis of infantile autism spectrum disorders, and identifies biomarkers related to infantile autism. The method comprises the following steps: firstly, constructing a self-adaptive crowd graph which takes image features as nodes, dynamically optimizing an edge weight based on the correlation between non-image features by utilizing a pairwise correlation encoder, and then, carrying out sparse processing on a graph structure by adopting an edge random discarding strategy; and then refining the multi-modal features layer by layer by using a residual image convolution network, introducing an image attention mechanism to carry out weighted fusion on the complementary multi-modal features, and finally outputting a final classification result through a multi-layer perceptron. The method has excellent performance in the aspect of autism diagnosis and classification, and meanwhile, biomarkers related to autism classification can be recognized.