170 results about "Structure learning" patented technology

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 industrial Internet of Things time sequence self-supervision anomaly detection method and a monitoring and early warning system, and relates to the field of industrial Internet of Things, and the method comprises the steps: S1, constructing an anomaly detection model, and S2, obtaining a training data set; s3, training and optimizing an anomaly detection model; s4, acquiring to-be-detected data in real time; s5, performing anomaly detection analysis on the to-be-detected data, and outputting an anomaly detection result; through a time sequence and relation learning module, a dynamic graph topological structure learning module and an enhancement module, internal characteristics of a time sequence in a time domain and a space domain are deeply mined. The time sequence and relation learning module comprehensively captures a multi-scale time pattern, and the dynamic graph topological structure learning module eliminates dependence on a predefined graph structure; the enhancement module enhances the invariant representation under noise, and improves the recognition capability of the model to a normal mode; through wide experiments, the advancement of the method in detection performance is verified, and reliable support is provided for intelligent manufacturing and infrastructure diagnosis.

The invention relates to the technical field of data anomaly detection, in particular to an intelligent treatment anomaly detection method and system based on dynamic space-time hypergraph evolution. Self-adaptive modal decoupling and multi-view embedding are carried out based on the obtained original observation data to obtain initial node representation, and the initial node representation comprises variational modal-based signal decoupling and multi-view space-time embedding coding; performing dynamic evolution hypergraph structure learning based on the obtained initial node representation to obtain a deep feature tensor, including dynamic hyperedge generation based on metric learning and space-time hypergraph convolution evolution; performing multi-scale time sequence prototype memory prediction based on the deep feature tensor, including multi-scale time sequence feature extraction, prototype memory reading and reconstruction and future prediction of reconstructed features; according to the method, the problems that sudden anomalies are difficult to pre-judge and the depth model lacks interpretability are solved.

The invention discloses a power plant secondary circuit fault tracing method based on causal reasoning. The method comprises the following steps: collecting and preprocessing multi-source operation data of a power plant secondary circuit system; based on the secondary circuit schematic diagram, the relay protection configuration table and the signal connection relation, establishing a topology constraint set, and generating an intervention node set; constructing an enhanced causal Bayesian network model based on the topology constraint set and the intervention node set; based on the topology constraint set, carrying out structure learning and parameter estimation on the enhanced causal Bayesian network model; inputting the standardized operation data set into the trained enhanced causal Bayesian network model, and executing reverse causal reasoning; and performing consistency verification on the fault root cause candidate set, the propagation path set, relay protection logic and interlocking rules, and generating a fault traceability report. According to the method, the enhanced causal Bayesian network is adopted, so that the power plant secondary circuit fault can be accurately traced.

The invention relates to an industrial internet multilayer causal motif abnormal propagation path identification method and system, and the method comprises the steps: firstly carrying out the construction and extraction of a multilayer high-order motif, extracting a motif unit which expresses the local high-order structure features through the construction of a semantic hierarchical graph structure in combination with a frequent sub-graph mining and cross-layer motif alignment mechanism, and carrying out the recognition of the abnormal propagation path of the multilayer causal motif. Stable and uniform multi-layer motif representation is formed; then, on the basis of the structural equation model, motif variables are regarded as endogenous variables of a causal model, a causal path between motifs is mined by introducing conditional mutual information and a Bayesian structure learning algorithm, an average causal effect is calculated to construct a causal consistency matrix, and causal community division is realized in combination with a weighted modularity optimization method; and finally, quantifying the dynamic change of a community causal structure by constructing a causal deviation graph between an expected causal graph and an observed causal graph, and assisting in identifying a causal-driven abnormal propagation path. According to the method and the system, accurate detection and causal traceability of equipment-level and subsystem-level abnormal modes in an industrial system can be realized.

The invention discloses an IOT (Internet of Things) equipment fault prediction method based on GraphRAG. The IOT equipment fault prediction method comprises the following steps: step 1, collecting and preprocessing multi-source heterogeneous data of various IoT equipment terminals; 2, constructing a graph structure based on the preprocessed data, a dynamic edge weight mechanism and a knowledge sub-graph; according to the graph structure, physical connection, functional dependence and communication topology between IOT devices are naturally expressed, so that the relevance between the operation states of the devices is fully captured. Sensor data, historical maintenance records and equipment configuration information are coded into node attributes and edge weights, so that the system can realize unified representation of multi-source heterogeneous data. The GraphRAG framework combines the structure learning ability of a graph neural network and the knowledge fusion characteristic of a retrieval enhancement generation mechanism, and shows stronger generalization ability in the recognition of a non-fault mode. When an abnormal signal occurs in a certain device, the model can trace the potential influence range through the state propagation path of the adjacent node, and the accuracy of early warning is improved.

The invention relates to the technical field of satellite internet, and discloses a low-orbit satellite phased array multi-beam interference modeling and suppression method and system, and the method comprises the steps: selecting a Kaiser window as a core filtering method, and achieving the optimization of beam characteristics through the dynamic adjustment of a shape parameter beta; generating an initial beam directional diagram based on a digital phase matching method, and multiplying the Kaiser window function coefficient by the excitation weight of the 64-array-element linear array element by element to realize spatial domain weighted filtering; the method comprises the following steps: constructing a training data set containing multi-scene interference characteristics, calculating a corresponding covariance matrix and an accurate inverse matrix thereof to form a sample pair, designing a deep neural network architecture, inputting a flattened covariance matrix vector, and learning a complex nonlinear mapping relation from the covariance matrix to the inverse matrix through a multi-layer full-connection structure; a mean square error is used as a loss function to constrain network output precision, and a multi-beam interference system model is constructed; according to the invention, stable and efficient communication of the low-orbit satellite system in a complex electromagnetic environment and under rapid channel change is ensured.

The invention belongs to the technical field of article recommendation, and provides an article recommendation method and system based on big language model enhanced graph representation learning, and the method comprises the steps: obtaining a user-article graph recommendation data set; extracting semantic knowledge features of the obtained graph recommendation data set; according to the extracted semantic knowledge features and an article recommendation model, completing article recommendation learning; wherein the article recommendation model adopts a big language model to enhance a graph representation learning model, embeds semantic similarity through the big language model, adopts an adaptive graph structure learning mechanism to identify a semantic boundary so as to determine structure information, and performs bidirectional knowledge interaction transmission on semantic and structure information; interactive noise is filtered and relieved in combination with information bottleneck regularization, optimization of an article recommendation model is carried out with the purpose of minimizing a comprehensive multi-objective loss function, and article recommendation is completed by calculating preference scores of articles.

The invention discloses a small sample remote sensing image classification method based on hierarchical spatial structure learning. The method comprises the following steps: firstly, extracting multi-scale features of a remote sensing image by using a ViT (Visual Transform) model, and capturing rich semantic information and spatial structure relationships in the image; secondly, constructing a graph structure based on spatial adjacency and attention weight to model a structured relationship between samples, and encoding graph node features through a graph convolutional network (GCN) so as to enhance the discrimination ability of the features in a structural semantic space; thirdly, a residual enhancement mechanism is introduced to fuse global semantic information, and the discrimination capability of graph embedding is improved; then, based on the structural similarity between the support set and the query set, performing classification decision, and realizing accurate classification under a small sample condition; and finally, carrying out joint optimization on the whole model by adopting a training strategy of a small sample meta learning task and a supervision loss function.

The invention provides an industrial process fault detection method based on a space-time causal diagram autoencoder, and the method comprises the steps: 1, carrying out the data preprocessing of the space-time process data of all process variables collected in the operation process of a target industrial process for the target industrial process; step 2, establishing a causal graph space-time auto-encoder CGSTAE; 3, executing a three-step causal graph structure learning algorithm to realize training of a causal graph space-time auto-encoder CGSTAE, wherein the training comprises three steps of pre-training, causal extraction and fine tuning; and step 4, obtaining a fault detection result based on hidden layer features of the causal graph space-time auto-encoder CGSTAE and residual data output by reconstruction. According to the method, effective process monitoring and fault detection are realized by constructing two statistical magnitudes in a feature space and a residual space. Compared with other methods, the fault detection method provided by the invention can improve the reliability and interpretability of industrial process monitoring.

The invention provides a method for predicting miRNA-lncRNA-disease ternary correlation through deep tensor decomposition and hypergraph convolution, and relates to the technical field of miRNA-lncRNA-disease ternary correlation prediction. Comprising six steps of integration of multi-source heterogeneous data, generation of three-dimensional tensor representation, hypergraph convolution modeling high-order interaction, graph attention network feature refining, depth graph convolution network enhancement and correlation prediction. Node features in a graph attention self-adaptive refining similarity network are integrated, global structure learning is enhanced by adopting a depth graph convolutional network, and the combination can generate stable and information-rich embedding for final ternary correlation prediction, so that potential complex correlation among various biological entities such as diseases, genes and drugs can be accurately extracted, and the prediction accuracy is improved. The potential relation and mechanism between the biological entities are further disclosed, and comprehensive ternary correlation prediction is achieved.

The invention relates to the field of data processing and information technology operation and maintenance, and discloses an intelligent operation and maintenance decision-making method and system based on multivariate heterogeneous data fusion. Comprising the following steps: constructing a dynamic causal graph reflecting a system state and an environmental factor causal relationship based on a causal structure learning algorithm; constructing a multi-agent game decision model based on the dynamic causal diagram, and respectively setting a global operation and maintenance target and a component local demand as a leader strategy and a follower strategy of the game; and solving game equilibrium by using multi-agent reinforcement learning, and outputting an optimal joint operation and maintenance decision instruction. The system is composed of a data acquisition preprocessing module, a multi-modal feature fusion module, a causal structure learning module, a game decision solving module and an execution monitoring module. According to the method, the causal inference and the game theory are fused, so that the causal logic can be accurately extracted, the decision robustness and interpretability are improved, and the global optimal collaborative configuration is realized.

The invention relates to a fine-grained target real-time image segmentation method and system based on a dynamic state modeling network, and belongs to the technical field of intelligent image processing. The method comprises the following steps: extracting multi-scale detail features by using a lightweight backbone network; through a dual-scale two-dimensional selective scanning module, the features are divided into a thin branch and a thick branch, and local scanning and global scanning are executed respectively; a dynamic cross-scale feature selection and aggregation module is adopted, redundancy is suppressed through reweighting and statistical filtering, and key target responses are highlighted; at a decoding end, local details and global semantics are fused through jump connection and an edge extractor; and finally, introducing a form-guided pseudo label hierarchical supervision strategy, and improving the structure learning ability of the model by using a coarse-to-fine morphological prior. According to the method, the segmentation precision, the boundary integrity and the tiny target recall rate of the fine-grained target under the scenes of ore separation, industrial defect detection, pavement crack recognition and the like are remarkably improved.

The invention discloses a sports market demand prediction method and device based on deep learning and a medium, and relates to the technical field of market demand prediction, and the method comprises the steps: collecting sports demand data, and carrying out the preprocessing; performing relation mining and graph structure learning on the preprocessed sports demand data through a graph attention space-time network to generate a macroscopic demand potential energy graph; performing potential area identification on the macroscopic demand potential energy diagram by adopting a pre-trained sports market simulation model, and outputting local demand prediction data; performing weighted fusion and error correction on the macroscopic demand potential energy map and the local demand prediction data, and outputting a sports demand prediction score; and making a sports market demand strategy according to the sports demand prediction score and the multi-granularity demand prediction report, and transmitting the sports market demand strategy to an enterprise manager through an enterprise decision support interface. According to the method, multi-level accurate prediction and decision support of sports market demands are realized through dual-mechanism cooperation of the graph attention space-time network and the space-time convolution.

The invention discloses a GNN-MAPPO task unloading method based on dominant value noise addition, which is characterized in that an MLP is changed into a GNN on the basis of the existing MAPPO framework, a multi-agent system can be directly modeled into a graph structure, an interaction relationship among multiple agents can be better established, Gaussian noise is added on the dominant value, the exploration capability of a model is enhanced, and overfitting is reduced. According to the method, the powerful graph structure learning ability of GNN is combined with an innovative dominant value noise adding mechanism, and the mixed reward function is elaborately designed, so that the MAPPO algorithm can more effectively learn a cooperation strategy between agents and optimize time delay and energy consumption in the aspect of edge computing task unloading, and the efficiency of the MAPPO algorithm is improved. And the exploration capability of the strategy and the avoidance capability of the communication risk can be obviously enhanced, so that a more robust and efficient intelligent task unloading scheme adapting to a dynamic environment can be obtained.

The invention discloses a cross-page table recognition system and method based on reinforcement learning, and belongs to the technical field of computer vision and natural language processing. The system comprises a data construction module, a model training module and a structured output module; the data construction module is used for extracting a table sample from the PDF document and executing paging, structure labeling, rendering generation and image pairing; the model training module is used for carrying out feature extraction and structured learning on the image based on a vision-language multi-modal large model, introducing a generalized relative strategy to optimize a GRPO algorithm, and constructing a cross-page table recognition model; the structured output module is used for detecting a table area, identifying a cross-page relationship and outputting a complete structured file; the cross-page table recognition system and method based on reinforcement learning provided by the invention solve the problems of inaccurate cross-page table recognition, table structure breakage, merged cell loss and the like in the prior art.

The embodiment of the invention discloses a graph neural network architecture search method and device based on a large language model and a medium. The search effect on a graph neural network architecture on graph data can be improved. According to the method, a graph neural network architecture and a graph structure are jointly optimized through an iterative updating method based on curriculum optimization so as to realize structure denoising; the large language model is integrated into a graph architecture search process to cope with semantic noise; therefore, graph structure learning and text modeling are incorporated into the denoising process in the search process, so that the search effect is improved, and a graph neural network architecture with better performance is obtained.

The invention relates to a category-independent attitude estimation method based on potential structure learning and electronic equipment, and the method comprises the steps: extracting features from a support image set and a query image set respectively, and obtaining support image set feature embedding and query image set features; generating two-dimensional coordinates of the key points based on feature embedding of the support image set; supporting image set features to be embedded and input into a variational auto-encoder based on iterative structure perception, and learning to output an adjacent matrix represented by a potential structure; querying a related fusion structure diagram by using a combined diagram structure transfer strategy based on the adjacent matrix sample and the global feature representation of the query image; and inputting the query related fusion structure diagram and the support image set feature embedding into the image convolutional network, updating the support image set feature embedding and the key point two-dimensional coordinates, and outputting the target key point coordinates of the query image after multi-layer iteration updating. Compared with the prior art, the method has the advantages of improving the accuracy and stability of category-independent attitude estimation and the like.

The invention relates to the technical field of behavior modeling, in particular to a multi-scene market declaration and execution coupling method based on energy storage behavior response modeling, which comprises the following steps of: establishing an index, confirming continuity and boundary connection of a declaration direction, and introducing structured jump pairing logic on the basis of constructing a cross-market behavior sequence; a high-frequency behavior chain in a time sequence is accurately captured, the network attribute of a jump structure is remodeled, the structure recognition capability between behavior segments is enhanced, a dynamic transition rule of the behavior sequence is established by introducing time window recognition and tail segment anomaly positioning operation between the jump segments, continuous interference caused by data abrupt change or delay jump segments is effectively eliminated, and the stability of the behavior sequence is improved. Through multiple innovative actions of sequence construction, behavior clustering, structure learning, time sequence comparison and price intersection pairing, a full-chain closed loop from response to declaration behavior correction is formed, and multiple improvements of market behavior understanding ability, data jump expression precision and real-time correction response ability are realized.

The invention relates to the technical field of server cluster operation and maintenance management, and discloses a server cluster-oriented operation and maintenance exception self-healing method and system, and the method comprises the steps: collecting multi-source operation and maintenance data of a server cluster, and carrying out the data preprocessing and multi-modal feature fusion; carrying out topological relation extraction and structure learning, identifying the change of a topological dependency relation and carrying out incremental updating; performing server node state prediction, and identifying abnormal nodes; performing causal inference and abnormal root cause positioning; rule reasoning is carried out, and the abnormal type of the root cause node is identified; constructing a strategy network, verifying and deploying the strategy network, and making a decision for the diagnosis identifier; carrying out multi-dimensional risk assessment and grading processing, monitoring an execution effect in real time and carrying out failure rollback; according to the method, a complete closed loop from anomaly detection, root cause positioning to strategy execution is formed, and an effective solution is provided for intelligent operation and maintenance of a large-scale server cluster.

The invention provides a photoelectric volume pulse wave identity recognition method and system, and relates to the technical field of identity recognition, and the method comprises the steps: obtaining a to-be-recognized PPG signal; and inputting the PPG signals into a trained identification model, firstly extracting linear features and nonlinear features, then projecting the features fused by the two features into a feature space by using a learned discriminant projection matrix to obtain multi-view features, and finally classifying the PPG signals by using the multi-view features to obtain an identity identification result. According to the method, manifold regularization and inter-class-error double sparse constraints are combined, the problems of intra-class discretization and inter-class overlapping of a linear model are solved, a graph structure learning method for adaptive local density adjustment is provided, and the manifold modeling precision of non-stationary PPG signals is improved.

The invention discloses a swimming skeleton point coordinate data denoising method and device based on spatio-temporal topological structure learning, and belongs to the field of data processing. The method comprises the following steps: acquiring and splicing a swimming video of a target object; detecting a target object in the video, and obtaining skeleton point coordinates and corresponding confidence scores of the target object by adopting a posture estimation model; preprocessing the coordinates to obtain feature vectors; adjacency information of each skeleton point is obtained based on the human body skeleton topology, the feature vectors of all the skeleton points are input into a spatial domain noise removal network, the adjacency information of the skeleton points is aggregated, multi-scale pooling is carried out, and denoised spatial domain features are obtained; sampling the high-dimensional spatial-temporal characteristics by adopting a deformable time convolutional network; and carrying out deformable convolution operation and decoding operation on the sampling features to obtain skeleton point coordinates after time-space domain denoising. Linear and nonlinear noise in data is processed in a time-space domain in a cooperative manner, so that the limitation of a traditional low-pass filter method and an existing ST-GCN method is effectively overcome.

The invention discloses a drug-target interaction prediction method and system, and belongs to the technical field of biological information. The method comprises the following steps: firstly, acquiring molecular structure data of a drug and sequence and structure data of a target spot; then, respectively extracting molecular map structure characteristics and SMILES sequence characteristics of the medicine, and amino acid sequence characteristics and three-dimensional space structure characteristics of a target spot; further, taking drugs and targets as nodes, taking the fused multi-modal features as node features, and combining known interaction and similarity information to construct an initial heterogeneous graph; inputting the heterogeneous graph into a dynamic graph neural network, dynamically learning an inter-node connection weight by using a graph attention mechanism, and iteratively updating node representation through multi-layer message transmission to obtain depth feature representation of drugs and targets; and finally, splicing the depth features, inputting the depth features into a multi-layer perceptron classifier, and predicting the drug-target interaction probability. According to the method, through multi-modal feature fusion and dynamic graph structure learning, the prediction accuracy and robustness are remarkably improved.

The invention discloses a code processing method and device fusing grammar structure and graph structure learning, and the method comprises the steps: collecting high-performance project codes, extracting hot code segments, and analyzing the structural features of the hot code segments; converting the hot code segment into an abstract syntax tree AST file, performing structured analysis, converting the file into Python tree structure data, and expanding node attributes; converting the tree structure data into graph structure data, generating edge, graph and node index files, and constructing the graph structure data and corresponding code structure labels; constructing a graph attention network model for different code structures, and training a plurality of GAT models based on graph structure data and labels; and converting the user input code, inputting the converted code into each GAT model for prediction, and integrating and outputting a feature vector. According to the method, grammar logic is accurately captured through AST conversion and node expansion, multi-language AST is unified into a graph structure, the recognition accuracy of a complex code structure is improved through a GAT multi-model architecture, full-process automation is achieved, and the labor cost is reduced.

The invention provides a real estate data visualization display method and system based on a GIS technology, and the method comprises the steps: carrying out the collection, cleaning, standardized mapping and entity alignment of multi-source real estate data, such as registration, transaction, planning approval, judicial, tax, sensing terminals, remote sensing images, and the like, building a space-time joint index with a space unit ID and a time slice ID as main keys, extracting a feature set and a target attribute set for modeling; constructing a structured causal model in the space-time database, and establishing a causal query index through a structure learning algorithm and parameter training; receiving the scene parameter set, analyzing the scene parameter set into an intervention instruction, executing a do-intervention operation on a causal model, collecting user interaction behaviors and external observation data, triggering online calibration of the model and a mapping rule when an error exceeds a threshold value, updating a causal query index and a mapping threshold value, and supporting version rollback; and a data, model, display and feedback closed-loop link is formed.

The invention relates to the technical field of intelligent control of tunnel boring equipment, in particular to an automatic adjusting method and system for a TBM shield process, and aims to solve the problem that a dynamic causal knowledge graph taking a geological state as a root cause cannot be constructed in the prior art. Bayesian structure learning, model-independent element learning, hierarchical reinforcement learning and a multi-target game mechanism cannot be fused, and interpretable modeling and self-adaptive decision making of a geology-equipment-control coupling relationship are difficult to realize; according to the method, a dynamic causal knowledge graph taking a geological state as a root cause is constructed, Bayesian structure learning, model independent element learning, hierarchical reinforcement learning and a multi-target game mechanism are fused, interpretable modeling and self-adaptive decision making of a'geology-equipment-control 'coupling relationship are realized, causal changes caused by geological evolution can be captured online, and the method has the advantages of being high in reliability and high in reliability. Rapid migration of strategies and generation of layered actions are supported, and accurate control instructions conforming to engineering priorities are output.

The invention relates to an air conditioner load prediction method based on a self-adaptive double-flow graph attention network, and belongs to the technical field of building energy conservation and intelligent control. The method comprises the following steps: collecting historical power and environmental data of an air conditioner, and after preprocessing and normalization, constructing an input sequence through a sliding window and dividing a data set according to time; a prediction model is constructed, and a causal graph learning module, a multi-scale graph structure learning module, a self-adaptive space-time attention module, an uncertainty quantization module and a self-adaptive sampling module are integrated; a training set and a joint loss function training model are adopted, and a load prediction result and uncertainty estimation are output through Monte Carlo Dropout during testing. According to the method, the dynamic causal relationship between variables and multi-scale space-time dependence can be adaptively learned, reliable uncertainty quantification is provided while the prediction precision is improved, and the method is suitable for intelligent regulation and control and energy efficiency optimization of the air conditioning system.

The invention provides an interpretable respiratory event detection method based on prior guidance graph structure learning, and the method comprises the steps: carrying out the preprocessing and division of a multi-modal physiological signal, and obtaining a time slice sequence; performing fragment-level graph learning and feature extraction processing on the time fragment sequence to obtain fragment-level overall representation and a fragment adjacency matrix; performing global-level learning and long-range dependency extraction by using the fragment-level overall representation to obtain a global-level overall representation and a global adjacency matrix; constructing a target function by using the fragment adjacency matrix and the global adjacency matrix; updating the detection model by using the target function to obtain an updated detection model; and obtaining a prediction result through the updated detection model. According to the method, a graph structure learning mechanism guided by clinical priori knowledge is introduced, and the continuity of physiological signals or a specific coupling relation between modes is used as a constraint, so that random noise and false correlation in data are effectively filtered.

The invention provides a multi-source sensor monitoring method and device, electronic equipment and a storage medium, and the method comprises the steps: configuring a heterogeneous sensor network according to a monitoring target and environment; a sensor node topological relation graph is established, a unique space-time identifier is distributed, and timestamp synchronous correction is carried out; processing data of different sampling frequencies and then extracting core feature vectors to form a multi-dimensional feature matrix; constructing a dynamic graph structure, learning a space-time dependency relationship between sensors, and generating a node embedding vector; establishing a space-time propagation model to predict a data evolution trend, constructing a normal working model, and calculating a reconstruction error threshold value; when abnormality is detected, triggering a sensor network adaptive reconfiguration mechanism, and adjusting the sampling frequency and sensitivity parameters of the key sensor; and constructing a hierarchical decision tree, fusing the monitoring result, generating confidence evaluation, outputting a differential early warning signal according to the risk level, and generating a monitoring report. According to the invention, the monitoring precision and the abnormal detection and fault diagnosis capability are improved.

The invention belongs to the technical field of petroleum geological exploration, and relates to a logging lithology intelligent identification method and system based on well network position map structure learning and feature fusion, and the method comprises the steps: obtaining the original data of logging in a target work area, and carrying out the standardization preprocessing, the original data comprises logging data, well position coordinate data and lithology label data; according to the original data after standardization preprocessing, a well network position map structure is established; performing feature extraction on the well pattern position map structure by using a map neural network to obtain high-dimensional features; fusing the high-dimensional features with original logging features, and obtaining an enhanced feature set to train a lithology classification model; and utilizing the trained lithology classification model to predict the lithology of a to-be-identified well section, and outputting a lithology identification result of the to-be-identified well section. The lithology identification accuracy and the geological rationality can be obviously improved.