434 results about "Information aggregation" patented technology

The invention provides a consensus decision question answering system based on multi-AI agent game, and relates to the technical field of artificial intelligence. The system comprises a multi-domain information aggregation module, an interaction effect deduction module, a strategy fusion calibration unit, a distributed behavior adaptive mechanism and an aggregation strategy discrimination module. The multi-domain information gathering module is used for unifying multi-source strategy information and environment situation data, the interaction effect deduction module is used for analyzing and quantifying the mutual influence relation of strategies between intelligent agents, and the strategy fusion calibration unit generates correction suggestions based on a game deduction and optimization method. The distributed behavior self-adaptive mechanism is used for locally and progressively executing a correction path in an intelligent agent; and the aggregation strategy judgment module dynamically evaluates the overall strategy state. The multi-agent consensus decision-making question-answering method realizes consensus decision-making question-answering of multiple agents in a complex environment, can effectively identify and correct non-collaborative strategy deviation, and improves the coordination, stability and immunity of a system.

The invention provides an interpretable node classification prediction method based on adversarial causal graph learning. The method comprises the steps that a constructed prediction model comprises a redundancy filtering module and an adversarial causal graph learning module; a redundancy filtering module and an adversarial causal graph learning module realize a graph information bottleneck mechanism; the redundancy filtering module adopts a two-layer graph attention network GAT structure to carry out information aggregation, and node embedding is obtained; the confrontation causal graph learning module adopts a learnable sub-graph sampler based on an attention mechanism to generate a causal interpretation sub-graph for node embedding, performs gradient disturbance optimization on interpretation sub-graph embedding based on a PGD confrontation training strategy of a causal enhancement mechanism, generates confrontation embedding, and obtains final disturbance interpretation sub-graph embedding through multiple rounds of disturbance iteration; performing end-to-end prediction model training through multi-target loss joint optimization; and after training is completed, embedding of the nodes is input into a classifier, and a prediction result is output. According to the method, the structural transparency and interpretability of the model are remarkably improved.

The invention discloses a multilayer dynamic aggregation big language model driven letter text analysis method, which relates to the technical field of natural language processing, and comprises the following steps of: acquiring multi-format letter data and converting the multi-format letter data into a text sequence with a timestamp, performing semantic analysis and structure processing on the text sequence, generating a text block set, and storing the text block set into a text database; performing multi-task training and topic fusion on the text block set to generate a comprehensive semantic vector, constructing a dynamic bridging graph based on the comprehensive semantic vector, activating associated text blocks by the bridging graph through semantic and time sequence relevance, responding to user query, splicing the activated text block content into a context Prompt, and inputting the context Prompt into a large language model; obtaining a one-time generated answer; the method has the advantages that semantic information and time sequence information are associated through the dynamic bridging graph, accurate response of the large language model is achieved based on the context Prompt, and the method has the advantages that complex semantic association is efficiently captured, context understanding coherence is improved, and dynamic information aggregation is achieved.

The invention discloses a building outer wall defect detection method based on mixed feature enhancement and attention optimization, and relates to the technical field of image data processing, and the method comprises the steps: collecting an original image of a building outer wall, carrying out the slicing processing, obtaining a plurality of slice sub-images, and carrying out the screening, and obtaining a mixed data set; constructing a feature pyramid sampling model of the mixed data set image, and performing feature extraction, feature enhancement and information aggregation on the mixed data set image at multiple scales; generating a small target defect feature map of the building outer wall based on the extended feature pyramid sampling model; and positioning defect positions in the slice sub-images based on a defect feature map, and mapping the defect positions in the image to obtain defect positions and types in the building outer wall image. According to the invention, the technical problem of low accuracy caused by too small target of building outer wall defect detection in the prior art is solved, and the accuracy of irregular small target defect detection and classification is improved.

The invention discloses an intelligent financial risk early warning method based on multi-modal data fusion, particularly relates to the field of financial risk management and control, and is used for solving the problem of existing enterprise transaction authenticity risk prevention and control. According to the method, a cross-modal transaction behavior chain of supervision enterprises is constructed through merging of transaction data in continuous account periods and modal information aggregation; generating an initial transaction relation graph based on the behavior chain, identifying business associated enterprises, and screening pseudo independent enterprises with control path crossing; performing modal semantic consistency check on the transaction path of the pseudo independent enterprise, identifying a semantic mismatch node, tracing a fund return path by taking the semantic mismatch node as a starting point, identifying a transaction closed-loop structure, and constructing a pseudo compliance risk chain; finally, the control relation and modal data are integrated, a structured evidence chain is generated, risk early warning output of involved enterprises is rapidly completed in a high-risk marking mode, and the automation degree, interpretability and accuracy of abnormal financial risk discrimination are improved.

The invention relates to the technical field of recommendation algorithms, provides a graph collaborative filtering course recommendation method based on interactive attention and comparative learning, and aims to solve the problem that a traditional recommendation system is insufficient in modeling ability in a sparse interaction scene. The method comprises the following steps: firstly, constructing a user-course bipartite graph, and utilizing a dynamic attention mechanism guided by an interactive opposite-end node: carrying out vector dot product through original embedding of the opposite-end node (for example, course embedding is used during user aggregation) and current embedding of a neighbor node, and generating an attention coefficient in combination with temperature parameter normalization; and multi-level structure information aggregation is realized. Afterwards, local context features are fused through a multilayer graph convolutional network, random noise disturbance is introduced to generate a multi-view comparison sample, the consistency of positive samples is maximized in combination with an InfoNCE loss function, and the robustness of the model to noise and sparse data is enhanced; and finally, optimizing user-course embedding in combination with Bayesian personalized sorting loss and comparison loss, and generating a personalized recommendation list. According to the method, the key interaction relationship is screened through guided attention, the representation discrimination is improved in combination with comparative learning, and the recommendation precision in cold start and data sparse scenes can be improved.

The invention relates to the technical field of artificial intelligence and biomedicine, and provides a multi-agent diagnosis planning device and method based on a consultation thinking process. The multi-agent diagnosis planning device comprises a diagnosis planning agent, a pathological section analysis agent, an in-hospital information aggregation agent, an information search agent, a knowledge base search agent, an evaluation agent, an arbitration agent and a risk assessment agent. Each agent realizes data interaction through a dynamic priority message bus, and the evaluation agent and the arbitration agent form a progressive verification closed loop: after the evaluation agent outputs a question evidence chain, the arbitration agent triggers diagnosis correction only when question items are greater than 3 items, and otherwise, final diagnosis is output based on a preset rule. According to the invention, the thinking process of multidisciplinary expert collaboration in clinical consultation is simulated by constructing a multi-agent collaborative diagnosis framework, and the whole process intelligence from medical data acquisition and analysis to diagnosis decision is realized.

The invention discloses an AI-based auxiliary system for clinical teaching of cerebrovascular diseases, particularly relates to the field of disease management, and comprises a multi-modal information summarization module, a feature extraction module, a cross-modal deduction module, a dynamic incremental learning module, a virtual case generation module and a teaching evaluation feedback generation module. According to the AI-based auxiliary system for clinical teaching of cerebrovascular diseases, millisecond-level time domain alignment is carried out through a multi-modal information gathering module, a mapping relation table of image space coordinates and physiological signal timestamps is established, the problem of time sequence deviation caused by asynchronous processing of sub-channels is solved, and time-space alignment of multi-source data is achieved; feature decoupling of images and physiological signals is achieved through a feature extraction module, and relevance between cross-modal features is enhanced; dynamic information of blood flow velocity and blood pressure fluctuation of blood vessels is fused into attention scores through a cross-modal deduction module, and a causal relationship between variables is established, so that time-space consistency of clinical logic during deduction of complex cases is improved.

The invention discloses an attention perception path reasoning method for a knowledge graph, and the method comprises the steps: firstly, generating an entity representation containing local semantics through employing a graph attention network coding entity and an adjacency relation, and synchronously obtaining an attention weight representing the association intensity between entities; secondly, innovatively providing a target-guided biased random walk path sampling strategy, and adaptively exploring a high-quality multi-hop semantic path related to a target task by taking the attention weight as a bias; then, information aggregation is carried out on the sampled semantic paths through a path encoder, and global path representation is obtained; and finally, carrying out deep fusion on the local entity representation and the global path representation, and jointly inputting the local entity representation and the global path representation into a prediction layer to carry out knowledge graph link prediction. According to the method, local structure perception and global path reasoning are cooperatively optimized through an attention mechanism, so that the link prediction precision and interpretability of the knowledge graph are remarkably improved.

The invention discloses a knowledge graph inference method and system combining reinforcement learning and an attention mechanism, and the method comprises the steps: inputting a knowledge graph into a knowledge graph inference model, building the environment of the knowledge graph, representing an entity in the knowledge graph as a state, taking a relation-entity pair as an action, constructing a Markov decision process, and obtaining the knowledge graph inference model. The agent performs an action in the environment to find a target entity; through mutual cooperation of a path history coding module, a neighbor entity information extraction module, a strategy decision-making module and a reward shaping module, an intelligent agent is guided to perform multi-hop path reasoning on entities and relationships thereof in a knowledge graph; and completing a knowledge graph reasoning task based on a reasoning result of multi-hop path reasoning. The method can effectively improve the accuracy and efficiency of the knowledge graph reasoning task, solves the problems of path selection difficulty, information aggregation insufficiency and reward sparseness in a traditional reasoning method, and has good practicability and popularization value.

The invention relates to the technical field of material testing, and particularly discloses an intelligent quality detection method and system for a micro-mineral bio-organic fertilizer, local thermal excitation is applied to a fertilizer sample through a micro-area thermal pulse excitation device, a gas release kinetic curve and a spectrum change track are synchronously collected, and a gas spectrum coupling data cube is constructed; performing differential transformation and modal decomposition on the time sequence feature set, extracting transient response feature vectors and generating an activity response distribution diagram; establishing component-activity correlation analysis, and decoupling biological metabolic activity and matrix background interference through feature separation and a comparative learning strategy to obtain a dynamic metabolic fingerprint; constructing a multi-dimensional feature space by using the dynamic metabolic fingerprints and the apparent characteristic parameters, and performing information aggregation and feature reconstruction by using a graph convolutional network to generate a comprehensive quality index; and finally, realizing quality grade judgment based on a quality characteristic pyramid structure, and establishing a dynamic early warning mechanism by analyzing time sequence evolution characteristics of the activity response distribution diagram.

The invention discloses an unmanned autonomous cluster flight control method based on a bionic alert mechanism, which is applied to the field of unmanned autonomous cluster control, and is characterized in that a W-MSR algorithm and a dynamic weighted bionic alert mechanism are fused, the W-MSR filters and eliminates extreme values of neighbor individuals, the dynamic weighted bionic alert mechanism strengthens input from informed individuals, and the dynamic weighted bionic alert mechanism is used for improving the robustness of the unmanned autonomous cluster flight control. And meanwhile, the influence of suspicious individuals is inhibited, so that the local information aggregation degree is effectively improved, and group splitting can be prevented. Different from a scheme depending on explicit attacker recognition, the method can improve the motion accuracy and connectivity of the unmanned autonomous cluster in a confrontation environment by probabilistically suppressing the influence of suspicious individuals and amplifying a consistent signal, provides a new idea for improving the security of a swarm intelligence system, and has a wide application prospect. Damage of malicious individuals to the cluster is effectively defended, and robustness and recovery capability of the cluster in a complex environment are improved.

The invention relates to the technical field of petroleum drilling engineering, and particularly discloses an analysis and prediction system based on petroleum drilling and production data, which realizes virtual reconstruction of a physical drilling system by constructing a digital mapping body, and adopts a multi-source sensor network to collect drill string vortex frequency and wellbore temperature field gradient data. Forming a time sequence feature set through time sequence alignment and fusion processing; performing differential transformation and modal decomposition on the feature set to extract transient response feature vectors and generate a risk distribution diagram; establishing a parameter-risk correlation model, and decoupling mechanical vibration and thermal stress interference by solving a physical equation to obtain a dynamic risk index; the risk indexes and the process parameters are mapped to a three-dimensional grid to construct a multi-dimensional feature space, a feature fusion grid is adopted to achieve information aggregation and reconstruction, and comprehensive risk assessment indexes are generated; a dynamic early warning threshold value is established according to the evaluation indexes, and drilling parameters are optimized in real time through closed-loop control.

The invention discloses a lodging winter wheat spatial distribution extraction method based on bimodal feature fusion, and relates to the technical field of agricultural remote sensing monitoring, and the method comprises the steps: building a double-flow semantic segmentation network DMFCFNet, and training the network through employing a constructed sample data set, inputting the preprocessed remote sensing image and the DSM elevation image into a network in parallel to obtain semantic texture feature information of the remote sensing images with different scales and spatial structure feature information of the DSM elevation image; and performing feature correction and fusion on the obtained semantic texture feature information of the remote sensing image and the spatial structure feature information of the DSM elevation image, and performing segmentation prediction on the fused feature information to realize extraction of the spatial distribution information of the winter wheat lodging region. By correcting and fusing remote sensing image semantic texture features and DSM elevation features and combining multi-scale information aggregation processing, the winter wheat lodging region extraction precision is effectively improved, and the problems of small target missing detection and boundary blur are solved.

The invention discloses a machine learning-based low-voltage transformer area electric leakage risk intelligent identification method and system. The identification method comprises the following steps of 1, multi-dimensional electric leakage feature construction and transformer area information aggregation analysis; 2, dynamically evaluating the electric leakage risk and adaptively adjusting a threshold value; and step 3, an electric leakage type intelligent identification and confidence degree determination mechanism. The electric leakage risk identification and classification method has the beneficial effects that by introducing a graph nerve enhanced gradient boosting tree model (G-GTBoost) and an improved residual error convolution-time sequence neural network (Res-CNN-LSTM) model and cooperating with a multi-modal feature fusion and dynamic threshold adjustment mechanism, the performance is remarkably improved in electric leakage risk identification and classification.

The embodiment of the invention discloses a knowledge graph and multi-modal fusion government affair information recommendation method and equipment, belongs to the technical field of information recommendation, and solves the problem that the matching degree between a recommendation result and a demand is relatively low when a user obtains government affair information. Comprising the steps of generating a user preference vector based on a user historical retrieval behavior, and performing coarse-grained retrieval on a government affair knowledge graph to obtain candidate knowledge sub-graphs; fusing the candidate knowledge sub-graphs with multi-modal data uploaded by the user to obtain a multi-modal semantic vector; performing multi-hop information aggregation processing on the multi-modal semantic vector, and performing fine-grained retrieval based on an aggregated graph node vector and a user query vector; based on a reordering mechanism, reordering the retrieval results, and determining recommended government affair knowledge sub-graphs; and based on the multi-modal data and the recommended government affair knowledge subgraph, performing visual question and answer and visual common sense reasoning processing to obtain a multi-modal reasoning result, and based on user requirements, sending the multi-modal reasoning result as recommended content to the user.

The invention discloses a high development potential academic entity identification and prediction method based on a heterogeneous graph Transform, and the method comprises the following steps: firstly constructing a heterogeneous information graph, representing multiple types of entities in an academic network through nodes, and representing the relation between the entities through edges; fusing structure embedding and semantic embedding of the nodes to generate a multi-modal initial feature vector; iteratively aggregating multi-hop neighbor information through a multi-layer heterogeneous graph Transform layer, dynamically learning weight and updating node representation to realize global feature aggregation; fusing multiple types of neighbor information of a target node based on cross-layer attention weighting, generating refined context features, and completing local background information aggregation; and finally, splicing the global node representation and the local context feature, and outputting the probability that the target entity becomes a high-development potential entity through a classifier. According to the method, the complex isomerism of the academic network can be effectively processed, the prediction precision is improved, and support is provided for scientific research management and the like.

The invention relates to the technical field of artificial intelligence and data processing, and discloses a multi-mode identity relation inference system based on a graph neural network. The system comprises a multi-modal feature extraction module, a cross-modal alignment module, a graph structure construction module, a dynamic relation reasoning module and a decision output module. According to the method, the cross-modal alignment module is introduced to project the image features and the text features to a unified public semantic space, so that the nonlinear distribution difference of heterogeneous modals in an embedding space is effectively eliminated, and cross-modal alignment errors are avoided from the source; by integrating the attention mechanism of modal perception in the graph neural network, the system can dynamically learn the semantic association strength between the nodes in different modals, adaptively adjust the weight distribution in the neighborhood information aggregation process, and significantly improve the accuracy of node characterization.

The invention relates to the technical field of knowledge graphs, and particularly discloses a knowledge graph construction method for structural information aggregation, which comprises the steps of data collection, entity semantic understanding, structural context aggregation, weighted relation representation and fusion graph construction. According to the scheme, discrete, disordered and heterogeneous entity attribute information in a cross-source knowledge graph is converted into continuous, ordered and structured entity attribute vectors to serve as carriers of structural information aggregation; step-by-step aggregation from a local triple structure to a graph-level semantic structure is realized, structural semantic roles of entities in different knowledge graphs are reserved, selective modeling is performed on adjacency relations through an attention mechanism, relation semantic similarity is introduced, and consistency and discrimination of entity representation are enhanced; entity-level and structure-level closed-loop enhancement processes are realized, and unification of semantic space of the knowledge graph and improvement of knowledge density are realized through alternate execution of multiple complementation and alignment.

A single-target tracking method and device based on a state space model and attention and a medium are characterized in that firstly, hierarchical feature extraction is carried out, hierarchical modeling is carried out on a template and a search area through a mixed attention mechanism and the state space model, and a time sequence token is introduced into each frame at the final stage of hierarchical feature extraction to carry out target information aggregation; and aggregating the time sequence tokens by using a state space model to realize cross-frame information propagation, multiplying the obtained time sequence tokens by corresponding frame feature maps to serve as feature enhancement, and finally predicting a bounding box of a target object by a prediction head to realize target representation. According to the method, the state space model and the attention mechanism are combined for single-target tracking, especially target tracking under a large-resolution video, the state space model is introduced into the field of single-target tracking, the advantage of linear complexity of the state space model is brought into full play, meanwhile, the disadvantage of limited retrieval capacity of the state space model is made up, and high-resolution video tracking is achieved. And finally, the consumption of computing resources is reduced while accurate tracking is realized.

The invention provides a multi-level semantic map construction method based on multi-sensor fusion, and the method carries out the construction of an environment grid layer, and comprises the steps: constructing an environment grid map in real time through fusing perception data; scene semantic layer construction: extracting image scene semantic probability distribution by using a deep convolutional network, fusing time sequence observation through Bayesian filtering, and mapping a scene category to a grid unit by using an occupation probability model; constructing an object semantic layer, namely identifying an object by adopting a target detection network in which an information aggregation-distribution mechanism is introduced, extracting an object point cloud, and dynamically updating object semantic attributes of grid units through multi-source observation fusion; and scene atlas generation: constructing a hierarchical scene atlas which takes the marker object as a reference core and comprises a spatial topological relation. According to the method, the dynamic environment adaptability and the multi-modal data fusion precision of semantic mapping are improved, a more visual environment understanding mode is provided for the robot, and the practicability of the semantic map in robot positioning and navigation is improved.

The invention relates to the field of machine learning, particularly discloses an artificial intelligence-based power system fault analysis and diagnosis method, and effectively solves the problem of information loss caused by neglecting a key waveform form in a transient signal in the prior art through a local feature extraction and serialization module. An original signal is converted into a local feature sequence with more characterization significance. Aiming at the averaging bottleneck of an existing model in an information aggregation stage, a traditional feature compression method is abandoned, and a sequence information aggregation and decision-making mechanism is provided. According to the mechanism, a context sensing sequence is regarded as a probability event, and modeling is carried out on the sequence from three orthogonal dimensions of a content center, time sequence dispersion and distribution uncertainty by calculating feature expectation, time sequence variance and information entropy of the context sensing sequence. The method can deeply insight and quantify the essential difference of different events in the time sequence dynamic evolution mode, thereby fundamentally solving the problem of misjudgment caused by feature confusion.

The invention discloses a regional public opinion propagation mode mining method based on a heterogeneous graph neural network. The method comprises the following specific steps: S1, constructing a heterogeneous graph; s2, weight definition of edge semantics; s3, node feature propagation and updating are carried out based on the graph neural network, and node embedding representation is obtained; and S4, node embedding is clustered, and regional public opinion propagation mode recognition is carried out in an embedding space. According to the method, joint modeling of users, media and regional nodes in a multi-relation heterogeneous graph structure is realized, and cross-semantic-edge information aggregation is completed by using a multi-relation attention mechanism, so that node embedding representation capable of representing multi-level propagation characteristics is obtained; the expressions provide basic support for subsequent public opinion propagation link identification, diffusion range prediction and opinion leader mining.

The invention relates to a multivariable time sequence anomaly detection method fusing feature correlation and time sequence dependency, and the method comprises the following steps: obtaining multivariable time sequence data, and obtaining a time sequence feature; calculating a mutual information coefficient between any two time sequence characteristics at the current moment; according to historical data in the multivariable time sequence data, predicting a time sequence feature at the current moment to obtain a prediction feature; constructing a graph neural network, taking the prediction features as nodes of the graph neural network, obtaining a relation coefficient of adjacent nodes through mutual information coefficients, and carrying out information aggregation on the graph neural network to obtain a prediction value of the prediction features at the current moment; and calculating the deviation between the predicted value and the current time sequence characteristic, and carrying out anomaly scoring according to the deviation. According to the method, the mutual information coefficient (MIC) and the graph neural network (GNN) are combined, the nonlinear dependency relationship among multiple variables and the dynamic change rule of the time sequence can be captured at the same time, and the accuracy and robustness of anomaly detection are remarkably improved.

The invention relates to a Parkinson's disease treatment effect prediction method based on a multi-modal image model and a related device. The method comprises the following steps: acquiring a multi-modal vector of a Parkinson's disease patient; aligning the multi-modal vectors on a time axis, and constructing a time point data element sample sequence; deploying a double-flow cross attention encoder for a sample sequence in each time point data element, and outputting a fused multi-modal feature through the double-flow cross attention encoder; and connecting the fused multi-modal features with digital clinical treatment scheme vectors corresponding to corresponding time points to form time point comprehensive feature vectors, inputting the time point comprehensive feature vectors to a time sequence information aggregation gating circulation unit, outputting final time point aggregation features, and inputting the final time point aggregation features to a multi-task adaptive prediction head. A UPDRS total score or a specific sub-scale score for the patient at a future preset point in time is predicted. According to the method, time sequence modeling is carried out on multi-mode and multi-time-point data, so that the accuracy and interpretability of Parkinson's disease treatment effect prediction are improved.

The invention discloses a multi-mode autism diagnosis method and system based on resting state fMRI and phenotypic text information, a medium and a product. FMRI and phenotypic text information are processed through a pre-trained heterogeneous graph neural network model to output a diagnosis result; the resting state fMRI is utilized to construct a brain function connection graph, and the phenotypic text information is coded into a two-dimensional vector; respectively extracting brain connection feature embedding of the fMRI mode and phenotype information feature embedding of the text mode; fusing the feature embedding in the two modes through a gating fusion network; and constructing a heterogeneous group diagram based on the fusion features and phenotypic information similarity, and obtaining group diagram features on the heterogeneous group diagram by using dual-channel information aggregation and adaptive feature fusion to output a diagnosis result. The invention aims to improve the accuracy of autism diagnosis by using multi-modal information, and can be applied to the fields of neuroimaging analysis, intelligent medical treatment and the like.

The invention provides a landslide detection method based on Swin Transform and multi-scale feature fusion, and relates to the technical field of landslide image detection, and the method comprises the steps: employing a Swin Transform architecture as a backbone network of a detection system, and effectively capturing local and global context information in an input image through a hierarchical self-attention mechanism; a multi-scale feature fusion lateral connection module is introduced, so that cross-scale feature integration is realized, and the capture capability of the model on landslide feature details and the understanding of the model on wider context information are improved; and the local information aggregation module is adopted to enhance the processing precision of local information. Through the advantages of the Swin Transform architecture, the problems of insufficient local and global feature fusion, low multi-scale feature utilization efficiency, poor complex scene adaptability and the like in landslide detection are effectively solved, and high accuracy and high efficiency of landslide detection are realized; particularly, the method shows excellent performance in the aspects of long-distance global dependence mining and landslide and non-landslide area distinguishing, and shows high robustness to environment and topographic changes.

The invention discloses an equipment remaining service life prediction method and device based on isomorphic space-time fusion and causal expansion convolution. The method comprises the following steps: firstly, acquiring an original vibration signal of the whole life cycle of equipment, screening data with learning characteristics, and performing sliding time window segmentation and normalization preprocessing; the preprocessed sequential sequence is subjected to causal convolution processing and then input into a time channel and a space channel, the receptive field is expanded by using expansion convolution with the same expansion rate, and spatial-temporal feature cross fusion is realized through a gating activation unit and a weight cross fusion mechanism. And performing global information aggregation and key feature extraction by means of a residual connection and information aggregation module, and finally outputting the residual life of the equipment through linear regression. According to the method, the spatial-temporal feature extraction capability is improved, the long-term dependency relationship modeling effect is enhanced, and an efficient and accurate prediction means is provided for health management of industrial equipment.

The invention discloses a bearing monitoring method and system applied to a bearing test, and relates to the technical field of intelligent monitoring, and the method comprises the steps: inputting a collected space-time sensing data set into an improved convolutional neural network, carrying out the modal weight distribution of an attention convolution layer, carrying out the global information aggregation of a pyramid pooling layer, and outputting a bearing joint vector; inputting the bearing joint vector into a digital twin engine, driving the bearing to execute multi-working-condition response simulation, monitoring the thickness distribution of a lubricating film and the crack growth rate in real time, and generating a virtual simulation data set; and comparing the virtual simulation data set with the space-time sensing data set by applying a dynamic residual analysis algorithm to obtain a deviation matrix, and performing incremental updating on the improved convolutional neural network according to the deviation matrix. According to the method, the improved convolutional neural network and the digital twin engine are utilized, multi-modal data are effectively fused, meanwhile, optimization and dynamic adjustment of a control strategy are achieved, and it is ensured that efficient and stable operation can be kept under various working conditions.

The invention relates to the field of underwater sound signal processing, in particular to an underwater DOA (direction of arrival) estimation method based on graph nerves and a convolutional neural network, which comprises the following steps: 1, establishing a linear array, and enabling narrow-band signals to simultaneously reach an underwater sound array; 2, performing signal preprocessing to obtain a signal covariance matrix, and performing normalization processing; 3, extracting correlation between array elements and spatial features of array signals, and performing data supplementation on sparse linear array information; 4, forming a double-branch structure, enhancing the information aggregation capability, and extracting features from a space path and a time domain path; and 5, constructing an adjacent matrix, filling node features of damaged array elements, adopting a double-branch structure, extracting spatial features and time domain features, carrying out feature integration, and outputting a DOA estimation result. The spatial correlation between array elements is extracted and the array sparsity problem is processed by using the graph neural network, and the time domain features of the signals are extracted in combination with the convolutional neural network, so that more accurate and more robust DOA estimation can be realized under the conditions of low signal-to-noise ratio and array sparsity.