3707 results about "Attention network" patented technology

The present application relates to the technical field of load prediction, and provides an electrical load prediction method and device based on spatial-temporal correlation. The method comprises: performing spatial-temporal modeling on the basis of historical electrical load data and historical environment data to obtain a multi-layer spatial-temporal model, wherein each layer model is a directed graph and represents spatial causality in the fixed time, and a directed edge between the layer models represents temporal causality; on the basis of an interpretable spatial-temporal attention transformer, performing feature extraction and priori knowledge fusion on the multi-layer spatial-temporal model to obtain spatial-temporal characteristics of an electrical load, wherein the interpretable spatial-temporal attention transformer comprises a spatial causal attention network, a temporal attention network, and a spatial dependence comparison module; and on the basis of the spatial-temporal characteristics of the electrical load, and the real-time electrical load data and the real-time environment data of a target electrical node, predicting by using a trained STEF-DHNet model to obtain the predicted electrical load data of the target electrical node. The present application can improve the prediction accuracy and the model applicability.

The invention discloses an auditing decision support system and method based on a dynamic knowledge graph, relates to the technical field of computers, and aims to solve the problems that auditing data are heterogeneous and complex, risk identification is not timely and causal interpretation is lacked. According to the system, multi-modal audit data is collected in real time through a streaming event processing framework, and a dynamic audit knowledge graph with timeliness weight is constructed. Based on a graph calculation engine and cross-domain rule mining, identifying a high-frequency risk mode, and generating a risk conduction path graph; further fusing a multi-modal graph attention network, identifying and positioning abnormal entities, and outputting abnormal nodes and risk links thereof; and finally, the abnormal node embedding representation is dynamically updated through the time sequence diagram attention network, an interpretable audit causal map is generated in combination with a structural causal model, and closed-loop support from data acquisition and risk identification to interpretive audit decision is realized. The intellectualization and transparency of audit decision making are improved, and an efficient and traceable decision making basis is provided for a complex audit scene.

The invention provides a home abnormal state signal detection method and system based on multi-modal sensing, and relates to the technical field of detection, and the method comprises the steps: collecting human body motion, acoustics and environment parameters through a millimeter wave radar, an acoustic sensor and an environment sensor, inputting the parameters into a deep fusion network, a spatio-temporal attention mechanism and a multi-scale convolutional neural network are used to extract a spatio-temporal feature sequence, and joint probability features are formed in combination with an acoustic feature spectrum analysis result. And inputting the joint probability features into a time sequence knowledge graph, and obtaining scene adaptive features after processing by a graph attention network and a double-flow auto-encoder network. And finally, the multi-task learning network is combined with the risk propagation neural network and the recursive neural network to carry out anomaly prediction and risk level evaluation, and is combined with historical early warning information to output an early warning strategy. According to the invention, multi-modal data can be effectively fused, the accuracy and reliability of home abnormal state detection are improved, and a more accurate risk assessment and early warning strategy is provided.

A small sample steel defect detection method based on an attention feature pyramid mechanism, comprising: first acquiring initial multi-scale feature maps by means of a convolutional neural network, then by means of a top-down and transverse connection mode, using an improved multi-scale attention network to process the initial multi-scale feature maps one by one to obtain intermediate multi-scale feature maps, then by means of a bottom-up and transverse connection mode, using the improved multi-scale attention network to process the intermediate multi-scale feature maps one by one to obtain final multi-scale feature maps, and finally outputting a final classification result; using a pseudo-label semi-supervised learning algorithm to train a detection network model, using a locally linear embedding algorithm to perform dimension reduction and visualization processing on high-level features of training output results, screening for mistakenly labeled and incorrectly labeled images and removing labels, then putting the images into unlabeled image data, and performing re-training to generate pseudo labels so as to expand a training set until the training requirement is met; and inputting an image to be detected into a trained detection network model for identification.

The invention relates to a multi-modal dynamic optimization educational resource recommendation system and method, and the system comprises the following modules: a multi-modal data collection module integrates video behaviors, answer tracks, physiological signals and other data through edge calculation, and constructs a learning feature map; the student portrait module adopts an LSTM-Attention network in combination with a graph neural network to dynamically model knowledge mastery and learning styles; the resource matching engine realizes multi-objective optimization of knowledge gain, cognitive load and interest matching based on reinforcement learning and knowledge graph analysis; the tag adaptive module dynamically adjusts resource weights through causal inference and comparative learning, the personalized recommendation module generates a dynamic learning path and pushes adaptive resources based on student portraits and real-time behavior data, and the learning progress tracking module monitors a learning state in real time and feeds back the learning state to the resource matching engine to optimize a recommendation strategy in a closed loop mode. The technical defects that resource recommendation of a traditional education platform is rigid and personalized adaptation is lacked are overcome.

The invention provides an employee post matching and deploying method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an optimized employee capability feature vector based on employee historical work data, extracting an employee capability development trend, and combining team cooperation data to obtain an optimized employee capability feature vector; a cross-modal contrast learning network is utilized to obtain an employee comprehensive ability vector, a development potential index is calculated, a post skill knowledge graph is constructed based on post instruction data and historical recruitment data, and a post skill feature matrix and a team structure feature vector are generated through a dynamic heterogeneous graph neural network and an adaptive graph attention network. Calculating an employee-post matching degree score matrix in combination with an employee comprehensive ability vector, inputting the matching degree score matrix and a development potential index into a distributed multi-agent reinforcement learning model, constructing a reward function, generating and evaluating a deployment scheme through hierarchical task decomposition and Monte Carlo tree search, and finally outputting a deployment execution scheme.

The invention relates to a knowledge graph construction method and system based on a large language model, and the method and system achieve the automatic construction and dynamic maintenance of a knowledge graph through multi-modal data fusion, reinforcement learning and comparative learning joint optimization, teacher-student model knowledge migration, time sequence dynamic analysis and an incremental updating mechanism. Constructing a reinforcement learning framework, and taking accuracy and integrity as reward indexes to train a large model to extract an entity relationship; a large-scale knowledge graph is used as a teacher model, and conflict resolution and semantic alignment of newly added knowledge and an existing graph are realized through a graph attention network; a verification rule is dynamically generated based on historical data and domain knowledge, and relation periodicity and mutation points are detected in combination with Fourier transform and a CUSUM algorithm; and finally, generating a traceable knowledge graph through incremental updating and version control. And the multi-modal data processing precision, the entity relationship extraction dynamic adaptability and the knowledge graph maintenance efficiency are improved.

The invention discloses a power load prediction method based on a two-channel cross attention network, and belongs to the technical field of time sequence prediction in deep learning. The method comprises the following steps of obtaining a power load data set, performing preprocessing on the power load data set, decomposing power load data into a plurality of local components by applying VMD, iteratively optimizing VMD parameters through an APSO algorithm, and screening a subsequence with the lowest complexity to be combined with original features to serve as model input; and then constructing a dual-channel cross attention network model, processing time sequence data by using a parallel mechanism of a BiLSTM channel and a Transform channel, enhancing the feature expression ability through an improved cross attention layer, and performing power load prediction by using an MLP fused KAN network. According to the method, the time sequence prediction of the power load is carried out by constructing the two-channel cross attention network, the prediction precision and robustness are high, and the accuracy of load time sequence prediction is remarkably improved.

The invention provides a supply chain multi-dimensional fluctuation prediction and inventory optimization method based on artificial intelligence, and relates to the technical field of supply chain management, and the method comprises the steps: obtaining multi-dimensional data, carrying out the preprocessing of the multi-dimensional data, generating a standardized time series data set and a knowledge graph, carrying out the multi-scale decomposition through wavelet transformation, and extracting fluctuation features; and constructing a state matrix by combining a graph attention network and a multi-head self-attention mechanism, predicting a risk conduction path based on historical fluctuation pattern matching and a graph convolutional network, and generating a risk prevention and control scheme. According to the invention, the fluctuation characteristics and the risk conduction path in the supply chain system can be accurately identified, and the early warning accuracy and the response timeliness are improved.

The invention provides a three-dimensional point cloud data segmentation method and system based on multi-scale point features, and relates to the technical field of point cloud processing, and the method comprises the steps: obtaining original point cloud data, constructing a three-dimensional Hash index table, carrying out the self-adaptive hierarchical division through a local maximum value of a density distribution curve, and obtaining a local maximum value of a density distribution curve; noise point elimination is carried out based on a region density count value and a geometric distribution coefficient, an enhanced feature vector containing a main feature direction, a main curvature value and the like is constructed for non-eliminated points, a local geometric descriptor is constructed by adopting four spherical search regions with increasing radiuses, a three-branch space attention network is input to extract spatial features, and the spatial features are extracted; and fused feature data are obtained through a multi-level geometric feature extraction network and a bidirectional feature propagation network. And obtaining a final segmentation result through segmentation energy function optimization and a region merging strategy.

The invention discloses a machine vision-based resistor disc defect online detection system and a grading method, relates to the technical field of industrial machine vision detection, and solves the defect problems in the aspects of multi-scale defect dynamic perception, cross-level feature interaction and process adaptive optimization in the prior art. According to the scheme, metal reflection interference is inhibited through Retinex illumination correction and a combined denoising model; adopting a deformable convolution kernel and cavity space pyramid pooling to realize gradient entropy driving dynamic sensing of the multi-scale defect; constructing a bidirectional cross-layer attention network to realize early fusion of high-resolution details and high-level semantics; modeling local-global feature physical association based on a graph attention network and a self-supervised message passing mechanism; integrating reinforcement learning and a memristor random calculation unit to form a closed-loop parameter optimization system; according to the method, the multi-scale defect detection precision, the cross-modal feature fusion efficiency and the system adaptive capacity under complex working conditions are remarkably improved.

The invention provides a private domain live broadcast user behavior analysis and drainage recommendation method based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: collecting user behavior data, constructing a multi-mode deep neural network for processing, extracting user time sequence behavior features, extracting user text intention features through employing a residual connection structure and comparative learning, and carrying out the drainage recommendation of the private domain live broadcast user. User behavior link characteristics are acquired through a time sequence diagram attention network, user layering is performed through a Gaussian hybrid clustering model, a user loss probability is predicted by using a deep cross neural network, future activeness and purchasing power of a user are predicted in combination with a recurrent neural network, and commodities are matched through a diagram attention commodity knowledge network. And performing recommendation by using the improved double-tower deep neural network in combination with difficult sample mining and a variational auto-encoder, and searching an optimized push strategy to obtain an optimal push strategy.

The invention discloses a multi-level information security policy generation method based on a knowledge graph, and the method comprises the following steps: S1, collecting multi-modal data of a plurality of security data sources, and generating a standardized data set; s2, constructing and dynamically updating a multi-level knowledge graph based on the standardized data set; s3, based on the multi-level knowledge graph, a graph neural network is adopted to identify security threats and generate an evaluation report; s4, analyzing time-space characteristics of the security event, and predicting a threat evolution trend; s5, optimizing a protection strategy based on the graph attention network, and adjusting in combination with a real-time feedback mechanism; s6, simulating an attack scene by using a generative adversarial network, and optimizing a security protection strategy generation process; and S7, based on the optimized protection strategy, further adjusting and optimizing by adopting a meta-learning algorithm. Through combination of meta learning, the graph neural network and the generative adversarial network, an intelligent and dynamically adjusted network security protection strategy generation and optimization scheme is provided.

The invention provides a multi-level attribution and recommendation method and system for medical management decisions, and relates to the technical field of medical management decisions, and the method comprises the steps: extracting graph node evolution characteristics through a multi-level medical data knowledge graph and a decision knowledge base by employing a time sequence diagram convolutional network, constructing a decision influence propagation graph through a cross-layer interactive attention network, and carrying out the analysis of the decision influence propagation graph; calculating the contribution degree of each decision node to the medical effect, generating an attribution analysis result, determining a decision influence hierarchical system according to the attribution result, and generating and evaluating candidate decision paths in combination with a multi-objective optimization network, a hierarchical reinforcement learning network and a Monte Carlo search tree network. In conclusion, accurate attribution analysis and multi-objective optimization of the management decision can be realized, the hospital resource allocation efficiency is effectively improved, the operation cost is reduced, scientific decision support is provided for a hospital management layer, and the hospital fine management level and the overall operation efficiency are remarkably enhanced.

The invention relates to the technical field of fault prediction, and provides a fault prediction method for a multi-modal cross-attention enhancement graph neural network, and the method comprises the steps: collecting the data of equipment; performing adaptive enhancement and normalization processing on the image data, performing sliding window segmentation, standardization and noise suppression on a time sequence numerical signal, and performing semantic vectorization coding on a maintenance log text; extracting low-dimensional spatial features of image data by using the pruned lightweight convolutional neural network, connecting time sequence features of modeling time sequence numerical signals in series, extracting context semantic expressions of maintenance log texts, integrating the features into multi-modal data, alternately taking each modal feature as Query and the other modal features as Key and Value, and obtaining multi-modal data; calculating attention weight and performing weighted fusion; constructing a modal node weighted graph, and performing inter-node feature propagation through a multi-layer graph attention network; and a residual service life regression prediction module and a degradation level classification module are deployed in parallel, and fault early warning is completed through multi-task joint optimization.

The invention discloses a vehicle multi-modal trajectory prediction method based on an improved attention network, and belongs to the technical field of intelligent vehicle trajectory prediction, and the method comprises the steps: collecting historical trajectory data of a target vehicle and surrounding vehicles as an input sequence; secondly, constructing a vehicle multi-modal trajectory prediction model which comprises a motion feature extraction module, a space-time interaction module, a space-time fusion module and a trajectory output module; the motion feature extraction module uses a multi-scale convolution attention network and a gating circulation unit for processing, the space-time interaction module uses a dynamic graph attention network for extracting vehicle interaction information, and the space-time fusion module splices and fuses target vehicle motion features and space-time interaction features to obtain space-time fusion features; the track output module inputs the fusion features into a gating circulation unit, decodes the fusion features and then inputs the fusion features into a mixed density network, and multi-mode output of vehicle tracks is achieved; and finally, a proper loss function is selected for training, so that the prediction precision and the convergence speed of the model are improved.

The invention discloses a road traffic flow prediction method based on a space-time mixed attention network, and the method breaks through the limitation of a conventional time sequence model and a single deep learning architecture based on the systematic analysis of urban road traffic flow space-time heterogeneity, periodic non-stationarity and road network topological relevance, constructs the space-time mixed attention network, and achieves the prediction of road traffic flow. Spatial heterogeneous correlation of road network nodes is captured through a graph convolution network, dynamic time sequence evolution characteristics of traffic flow are modeled by adopting a hybrid architecture, a residual attention mechanism is introduced to realize layer-by-layer refining of multi-scale spatio-temporal characteristics, and the overall architecture of the method has remarkable advantages in the aspects of spatial topology modeling and time dynamic capture compared with a traditional model. Feature decoupling learning is carried out on multi-source heterogeneous data such as weather and events, adaptive integration of environment sensitive features is realized through a parameterized gating fusion strategy, and the prediction error fluctuation amplitude in an extreme weather scene is reduced by 34.8%.

The invention provides an integrated logistics management system for cargo transportation. The integrated logistics management system comprises a multi-mode collaborative perception module, a semantic reasoning module, a scene adaptation decision module, an intelligent execution module and a logistics trust chain module. The multi-mode collaborative sensing module collects logistics full-scene state data through a sensor network; the semantic reasoning module is used for constructing a knowledge graph based on a graph attention network and realizing unified modeling of multi-source information; the scene adaptation decision module is combined with a pulse coupled neural network (PCNN) and a swarm intelligence collaboration mechanism to complete routing optimization; the intelligent execution module adopts a hierarchical task management and dynamic adjustment mechanism, and adjusts a task scheme according to execution abnormity and credit evaluation; the logistics trust chain module constructs a logistics trust system through a hierarchical consensus mechanism, a credit scoring model and a trust propagation and early warning mechanism, and realizes risk prevention and control of the whole process. The system solves the technical problems of a traditional logistics system in the aspects of data integration, dynamic supervision, decision support, collaborative scheduling, safety traceability and the like.

The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based image segmentation and dynamic target recognition method, which comprises the following steps of: accurately positioning a candidate region through multi-modal space-time fusion and dynamic confidence coefficient screening; strengthening spatial-temporal feature expression in a layering manner through a multi-level feature decoupler, and generating a multi-dimensional feature enhanced spatial-temporal candidate region; through a deformable segmentation network, a deformation convolution kernel and edge motion matching loss are combined, joint optimization of a geometric boundary and motion continuity is realized, and the segmentation robustness of a flexible target is improved; through optical flow back propagation dynamic correction and confidence coefficient propagation, high-precision segmentation masks with consistent time and space are output; and through a target trajectory re-identification and completion mechanism driven by a graph attention network, and in combination with optical flow deformation prediction, stable tracking in a shielding scene is realized.

A generative adversarial multi-headed attention neural network self-learning method for aero-engine data reconstruction belongs to the field of end-to-end self-learning of aero-engine missing data. First, the samples are pre-processed, and the machine learning algorithm is used to pre-fill the normalized data first, and the pre-filled information is involved in the network training as part of the training information. Second, a generative adversarial multi-headed attention network model is constructed and the trained sample set is used to train the generative adversarial multi-headed attention network model. Finally, the samples are generated using the trained sample generator G. The method uses the generative adversarial network to better learn the distribution information of the data, and uses parallel convolution and multi-headed attention mechanism to fully exploit the spatial and temporal information among the aero-engine data.

The invention discloses an APT attack chain reconstruction method based on a knowledge graph and a graph neural network, and belongs to the technical field of network security. Multi-source heterogeneous attack clues are subjected to advanced analysis by introducing a large language model, and time sequence enhanced knowledge graph representation and the deep learning ability of a graph convolution attention network are combined, so that the multi-source heterogeneous attack clues are reconstructed. The method promotes the efficient completion and dynamic reconstruction of the attack chain, and solves the problems that in the prior art, due to the problems of data sparsity, relation complexity, time sequence characteristics and the like, obvious limitation exists in the aspects of attack chain completion and inference, and a traditional method lacks the deep learning ability for the implicit relation in the attack chain. And thus, the problem of insufficient inference capability on unknown attack behaviors is solved.

The invention relates to the technical field of service examination and approval, and discloses a service examination and approval processing method, device and equipment and a storage medium, and the method comprises the steps: receiving a user authorization request, verifying the user authorization request, and obtaining a sub-account certificate with a reading only permission; performing real-time data acquisition on the plurality of e-commerce platforms based on the sub-account credentials to obtain multi-dimensional user data including transaction records, operation data and evaluation information; inputting the multi-dimensional user data into a preset space-time sequence attention network and graph convolution hybrid model to carry out dynamic evaluation on a user behavior pattern to obtain risk quantitative indexes; inputting the risk quantitative indexes into a rule engine integrating an adaptive decision tree and a genetic algorithm for automatic decision making to obtain a graded approval decision; and performing multi-signature encryption on the execution instruction according to the graded approval decision to obtain a safety operation instruction and execution state information, thereby realizing full-process automation from data acquisition, risk assessment to decision execution.

According to the invention, based on a low-line-number road side laser radar, real-time extraction and optimization of multi-vehicle tracks are researched, and a track completion method and system combined with a road topological map are provided for solving the problem of track interruption caused by mutual shielding of vehicle targets in a complex environment. According to the method, track completion is carried out by obtaining a full-time motion mode, context information and environment characteristics of a vehicle in a scene and combining a road topological map. Specifically, the topological map is coded by adopting a gating loop unit and a map attention network, and missing trajectory data is generated by utilizing a diffusion probability model based on trajectory context and map condition constraints. By integrating point cloud data acquired by a roadside laser radar, a vehicle track is complemented, and a high-precision continuous track conforming to road constraints, traffic rules and vehicle kinematics characteristics is generated. The trajectory completion method has important significance in improving the intelligent traffic decision-making level and promoting the application of the intelligent driving technology.

The invention provides a crane remote instruction response delay detection and in-advance compensation method and system, and relates to the technical field of cranes, and the crane remote instruction response delay detection and in-advance compensation method comprises the following steps: adopting an adaptive space-time alignment algorithm to map real-time operation data to a dynamic knowledge graph, and generating a feature vector; inputting the feature vector into a depth map neural network integrated with a causal reasoning mechanism to generate an incidence matrix; a multi-head attention network with a residual structure is adopted to extract time sequence features; constructing a hybrid decision system based on the delay prediction tensor, and outputting an optimal compensation strategy; and establishing a double-closed-loop evolution system with an online learning capability, and dynamically optimizing a prediction and compensation strategy according to a compensation effect. Through the dynamic knowledge graph, causal reasoning, the multi-head attention network and the double-closed-loop evolution system, the remote instruction response delay can be accurately predicted, effective compensation is carried out, and the real-time performance and safety of remote control of the crane are improved.

The invention provides an erasure code compatible read-write method and system based on a bidirectional data access agency, and relates to the technical field of distributed storage, and the method comprises the steps: employing multi-agent reinforcement learning to construct a heterogeneous resource portrait and optimize task allocation, combining a space-time graph convolutional network and a federated learning framework to construct a data format conversion model, and constructing an erasure code compatible read-write model; data features are analyzed through an encoder based on an attention mechanism, a fragmentation strategy is generated, self-adaptive encoding is achieved through a capsule neural network, data integrity verification and repairing are conducted through a double-layer space attention network and a graph attention network, the data access efficiency of a heterogeneous storage system is improved, data format compatibility is enhanced, and the data access efficiency is improved. And the data integrity is ensured.

The invention relates to a multi-source remote sensing image classification method based on a key band retrieval attention mechanism, and belongs to the technical field of remote sensing image processing. The method comprises the steps of firstly performing preprocessing and data set division on multi-source remote sensing data, then constructing a key band retrieval attention network, performing training and evaluation on a model by utilizing a training set and a verification set after division, and finally performing visual analysis on a model result by utilizing a test set. According to the method, the features of the hyperspectral image and the laser radar / synthetic aperture radar image are effectively extracted and fused, redundant information interference is effectively reduced, the retention rate of hyperspectral key information is improved, the complementary expression ability among multi-source data is enhanced, and the remote sensing image classification precision and calculation efficiency are remarkably improved. The method is suitable for application scenes of multi-source remote sensing data fusion and classification, can meet efficient intelligent processing requirements of complex earth surface information, and provides an accurate and efficient remote sensing image classification solution.

The invention provides an AI agent process automation method and system for an education scene, and relates to the technical field of AI education, and the method comprises the steps: constructing a hierarchical knowledge graph through semantic segmentation, and constructing an agent encoder model through a graph attention network and comparative learning. Based on agent operation data and a feedback mechanism, operation parameters are dynamically adjusted, task decomposition is performed, sub-tasks are processed by using a meta-learner, and a knowledge cache module is established. And semantic analysis and association network construction are performed by using the knowledge cache module, a decision model is trained, and an execution process is optimized. The workflow evolution trend is predicted according to the agent state data, sub-task configuration is optimized, closed-loop optimization is formed, and therefore the automatic processing efficiency and adaptability of the AI agent in an education scene are improved.

The invention provides an intelligent customer risk assessment system and method based on a large language model, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining multi-modal data of a customer, carrying out the preprocessing, and extracting structured and unstructured features; constructing a hierarchical risk knowledge system, and realizing adaptive evolution of the knowledge system through a graph neural network and a generative model; constructing an initial negative sample library, and constructing a negative sample database in combination with a non-risk mode labeled by an expert and derivative layer analysis; optimizing the large language model by adopting a strong supervision, weak supervision and reinforcement learning cooperative training mechanism under each classification according to the customer type; mining risk features in a text by using the optimized large language model, processing multi-modal data through a multi-level attention network, and generating a positioning report including contradiction type coding, service influence dimension evaluation and risk level quantification; the accuracy, efficiency and flexibility of customer risk assessment are improved, and the risk management strategy is optimized.

The invention discloses a search method and system based on computer natural language processing, and the method comprises the steps: extracting a synonym set and a context association relationship of keywords in a query text through a preset semantic knowledge graph, and generating a semantic vector representing a semantic dimension in combination with a deep learning model; extracting a historical behavior feature sequence from the query log based on the semantic vector, and analyzing a user search intention by adopting an attention mechanism model; performing similarity matching on the pre-constructed database by utilizing a semantic matching algorithm, and screening an information matching set meeting a threshold value; performing distributed processing on the matching set through a context-aware dynamic fragmentation algorithm, and constructing an index fragmentation cluster; semantic aggregation is realized by adopting a cross-fragment graph attention network, and an optimized search result set is generated through dynamic semantic projection. According to the method, through multi-modal fusion of the semantic knowledge graph and deep learning and in combination with a dynamic distributed processing architecture, the accuracy of search intention recognition and the efficiency of large-scale semantic matching are improved.

The invention proposes a land utilization monitoring method and system based on remote sensing and big data, and relates to the technical field of land monitoring, and the method comprises the steps: dividing sub-regions, and obtaining the multi-temporal and multi-resolution remote sensing data of the sub-regions; performing feature extraction and classification on the remote sensing data based on a deep learning model to generate a land utilization classification map; based on the dual-temporal difference attention network, identifying a change area and constructing a change driving factor library fusing meteorological data and human activity data; detecting an abnormal area based on the driving factor library, and generating an abnormal type label and an attribution analysis report in combination with a dynamic early warning threshold; performing visual rendering on the monitoring result, and outputting an abnormal region early warning map and a disposal suggestion; high-precision feature extraction and classification are realized, change areas and driving factors are deeply analyzed, abnormal areas are effectively detected and early warning is performed, and the accuracy, timeliness and practicability of land utilization monitoring are improved.