368 results about "Automatic learning" patented technology

The invention relates to the technical field of power equipment fault diagnosis, in particular to a power equipment fault intelligent diagnosis method and system based on deep learning. The method comprises the following steps: automatically learning high-dimensional space-time correlation features in original time series data through a deep feature extraction network, and generating feature vectors representing potential abnormal modes of equipment; performing adaptive weight distribution on the high-dimensional space-time correlation features by using an attention enhancement mechanism, and marking a fault sensitive area to form enhanced fault features; inputting the enhanced fault features into a multi-level classifier for joint fault mode recognition and severity evaluation, and outputting a diagnosis result tensor containing a fault type and confidence; and an equipment maintenance decision signal is triggered based on the diagnosis result tensor, and the feature extraction network and classifier parameters are iteratively optimized according to feedback data, so that the intelligent level of operation and maintenance of the power equipment can be comprehensively improved.

The invention discloses an unmanned aerial vehicle intelligent inspection system based on AI vision and a detection switch cabinet, and relates to the technical field of unmanned aerial vehicle intelligent inspection, the unmanned aerial vehicle intelligent inspection system comprises an unmanned aerial vehicle inspection platform, and the unmanned aerial vehicle inspection platform is in communication connection with the following modules: an unmanned aerial vehicle end, which is used for collecting and preprocessing video stream data of an inspection area; extracting a key frame from the preprocessed video stream data; and the AI visual analysis module is used for analyzing the extracted key frame by using an AI visual algorithm and identifying key information and abnormal fragments in the key frame. According to the invention, through the AI vision algorithm based on the convolutional neural network model, abnormal features can be automatically learned and identified, the abnormal types and specific conditions can be rapidly determined through deep analysis of the key frames, accurate positioning of abnormal segments and comparison with the preset abnormal feature database, compared with manual detection, the accuracy is greatly improved, and the detection efficiency is improved. Tiny abnormal changes can be found in time, and potential faults can be warned in advance.

The invention discloses an entity digitization and link framework algorithm based on a heterogeneous graph attention network, and the algorithm comprises the following steps: S1, heterogeneous information network construction: carrying out the unified modeling of all multi-source heterogeneous data into a heterogeneous information network containing various types of nodes and various types of edges, s2, meta-path definition and guidance: defining "meta-paths" connecting different types of nodes to capture a complex deep semantic relationship, S3, heterogeneous graph attention network embedding: adopting an attention mechanism to enable a model to automatically learn importance of different neighbor nodes and different meta-paths, generating a final embedding vector of each entity, and establishing a heterogeneous graph attention network model; according to the method, the information fidelity is higher, modeling is directly conducted on different types of nodes and relations on a heterogeneous graph, more abundant and heterogeneous semantic information in data can be reserved compared with a multi-view method, and the end-to-end learning ability is higher; and the complexity of manually designing a fusion strategy is reduced.

The invention relates to the technical field of enterprise service intelligent recommendation, and discloses an enterprise service matching method and system based on a large language model, and the method comprises the following steps: S1, receiving an enterprise service demand text input by a user, carrying out the semantic analysis of the service demand text through the large language model, and obtaining a semantic analysis result; constructing a service semantic graph containing service nodes and semantic relationships; s2, establishing a plurality of agents representing different roles in the enterprise, wherein each agent corresponds to one service preference; s3, based on the service semantic graph and the service preference, node embedding is carried out by using a graph neural network, and the service combination potential is estimated; and S4, performing multi-target matching optimization according to the service preferences and the combined potentials of the multiple agents, and outputting a service matching result. A graph neural network is adopted to model service node embedding, so that a combination relationship among services is efficiently captured in a graph structure, and automatic learning of non-uniform importance among the service nodes is realized by introducing a multi-layer graph attention mechanism.

The invention belongs to the technical field of hydroelectric generating set fault diagnosis, and particularly discloses a hydroelectric generating set cavitation fault diagnosis method based on multi-channel acoustic emission signal fusion. Through the technical means of acquiring the acoustic emission signals at multiple positions, a more comprehensive cavitation phenomenon data basis is provided; the representation of time-frequency characteristics is optimized through the Mel time-frequency diagram, and the problem that the dynamic characteristics of acoustic emission signals cannot be effectively captured through a traditional method is solved; multi-channel feature extraction is performed on the Mel time-frequency graph through a preset deep convolutional neural network model, so that automatic learning of deep cavitation working conditions is realized; and multi-channel fusion and classification identification are carried out on a feature extraction result through the model, so that effective integration and fault classification of features are realized. Compared with the prior art, a more accurate and comprehensive cavitation fault diagnosis effect is realized, and the diagnosis precision is improved.

The invention discloses a protein palmitoyl transferase prediction method and system based on a multi-branch deep convolutional neural network, and belongs to the technical field of bioinformatics and artificial intelligence. The method comprises the following steps: S1, obtaining a to-be-detected protein sequence; s2, inputting the protein sequence into a pre-trained iPalmT model; and S3, judging whether the target protein is palmitoyl transferase or not according to a model output result. The iPalmT model comprises a coding module, two paths of parallel convolution branches, a feature fusion module and a classification module; and after the convolution layers of each convolution branch are stacked, an SE module is arranged and is used for channel weighting and feature re-calibration. The model extracts multi-level sequence features through convolution kernels of different scales, realizes high-precision prediction through feature fusion and a residual structure, can automatically learn multi-scale features from large-scale data, realizes end-to-end palmitoyl transferase recognition, and has high accuracy and good universality.

The invention relates to the technical field of industrial equipment state monitoring and fault diagnosis, in particular to a carrier roller fault monitoring method based on sound multi-feature fusion for a carrier roller of a belt conveyor. The method comprises the following steps: firstly, collecting a sound signal when the carrier roller runs, and segmenting the sound signal into segments with fixed duration; then, for each sound segment, a logarithmic Mel spectrum, a Mel frequency cepstral coefficient graph and a spectral contrast graph are extracted in parallel. The feature images are superposed and fused into a three-channel feature image after being subjected to independent channel normalization processing and size unification. And finally, inputting the fused three-channel feature image into a convolutional neural network for training and reasoning of a fault classification model, and realizing identification of various carrier roller fault types. According to the method, the defects of an existing carrier roller fault monitoring method in the aspects of recognition accuracy, noise immunity, fault type subdivision capability, intelligent degree and the like are overcome, and the accuracy and robustness of fault monitoring can be remarkably improved by fusing multiple complementary acoustic features and utilizing the powerful automatic learning capability of the deep learning model.

An object of a system according to an embodiment is to automatically learn a behavior pattern of a target person, detect an abnormality, and issue an alert.SOLUTION: A system according to an embodiment includes a GPS device, a AI learning unit, an abnormality detection unit, and an alert generation unit. The GPS device keeps track of the current location of the subject. The AI learning unit automatically learns a daily behavior pattern based on the position information of the target person acquired by the GPS device. The abnormality detection unit detects an abnormality by comparing the behavior pattern learned by the AI learning unit with the current position information. The alert issuing unit issues an alert based on the abnormality detected by the abnormality detection unit.SELECTED DRAWING: Figure 1

The invention relates to the technical field of deep learning, in particular to a radiotherapy plan dose distribution verification method based on deep learning, and the method comprises the following steps: collecting historical radiotherapy plan data, generating a physical reference dose field through a Monte Carlo algorithm, unifying the voxel resolution of an anatomical structure to 1 cubic millimeter, and normalizing the dose according to a prescription, data enhancement is carried out only by adopting translation and mirror transformation, trace Gaussian noise is added, and a physical information enhanced three-dimensional training data set is constructed. According to the method, a three-dimensional convolutional network is utilized to automatically learn a dose distribution rule of a historical high-quality plan, a physical constraint module is embedded to ensure that a prediction result accords with a radiology principle, a real-time clinical rule engine is combined to instantly identify and correct a violation hot spot cold region, and an uncertainty quantification technology is assisted to position a high-risk region, so that the accuracy of a prediction result is improved. Finally, minute-level full-automatic verification is achieved, executable optimization suggestions are output, and efficiency is improved by dozens of times while safety is improved.

The invention discloses a multi-modal data collaborative analysis risk quantitative evaluation system, and the system comprises a multi-source data collection unit which is used for collecting different modal data; the data integration unit adopts a data cleaning technology and is used for integrating data of different formats and sources into a unified database; the fusion module is used for applying a deep neural network model based on an attention mechanism to automatically learn importance degrees of different modal data during risk assessment and dynamically distribute weights; the visual interaction unit is used for generating a visual interface according to the multi-modal data; the dynamic evaluation unit is used for constructing a personalized initial evaluation model by utilizing a machine learning algorithm; the method has the beneficial effects that the importance of different modal data in risk assessment is automatically identified by building an omnibearing acquisition system, collecting physiological, behavior, language and text multi-modal information and mining associated features through cross-modal contrast learning, and potential risk signals of a target are more accurately captured.

The invention provides a multi-dimensional financial risk early warning and dynamic management and control system based on AI driving, and the system comprises a data collection and preprocessing module which is used for collecting internal and external multi-source heterogeneous financial data of an enterprise in real time, carrying out the cleaning, standardization and feature engineering processing, and generating a structured feature vector; the AI risk assessment module is used for constructing a multi-dimensional risk assessment model based on a federated learning framework, and the federated learning framework comprises a longitudinal federated learning sub-framework, a transverse federated learning sub-framework and a model optimization unit; according to the method, a self-adaptive feature alignment algorithm based on differential privacy is adopted, cross-mechanism feature distribution and semantic level alignment are automatically learned, a mapping relation does not need to be manually preset, the dynamic change of multi-source data can be quickly and accurately adapted, the feature alignment efficiency is greatly improved, and the accuracy of feature alignment is improved. Therefore, federal learning can more timely utilize multi-mechanism data to carry out risk assessment, and more timely financial risk early warning is provided for enterprises.

The invention discloses an unmarked steel rail surface defect screening method based on self-supervised learning, and relates to the technical field of steel rail maintenance. Comprising the following steps: S100, acquiring steel rail surface image data and carrying out data preprocessing to generate an enhanced image pair; s200, constructing a defect screening basic feature encoder through a multi-scale visual pre-training model, and generating a final multi-scale fusion feature vector based on the enhanced image pair; and S300, constructing a dynamic pseudo tag generation unit, and calculating the cosine similarity between the final multi-scale fusion feature vector and the nearest neighbor normal sample feature vector. According to the method, a multi-scale visual pre-training framework is constructed, deep visual features representing the normal state and the abnormal state of the surface of the steel rail are automatically learned from massive original steel rail images on the premise that manual labeling is not needed, and a dynamic pseudo-label generation mechanism and a cross-scene migration adaptation unit are combined, so that the real-time performance of the system is improved. High-precision automatic screening of steel rail surface defects is achieved, and the generalization ability of the model in a complex environment is improved.

The invention discloses a deep coal bed gas seam network density prediction method, and relates to the technical field of reservoir development. The method comprises the following steps: extracting an average seam net distance from microseismic event point data as a seam net density representation label; establishing a multi-source data fusion framework taking a fracturing section as a sample unit, and splicing geological and engineering parameters into a feature vector; a machine learning algorithm is adopted to construct an intelligent prediction model, and a complex nonlinear mapping relation between multi-source features and labels is automatically learned. According to the method, intelligent prediction of the fracture network density is realized, and reliable support can be provided for fracturing effect evaluation and construction parameter optimization.

The invention discloses a vulnerability misinformation detection method and electronic equipment, and relates to the technical field of computers, and the method comprises the steps: obtaining feature statistical information based on a sample data set from a plurality of data providers, determining a feature subset with a distinguishing capability, and carrying out the joint model training based on a standard data set corresponding to the feature subset, and generating a vulnerability false alarm identification model. The model can automatically learn difference characteristics of misinformation and non-misinformation code snippets among different items, and realizes cross-item and cross-version misinformation identification and filtering. The technical problem of rule failure caused by dependence on context information such as specific code paths or line numbers and code structure adjustment or version updating is solved, and the technical effects of automation of false alarm filtering, reduction of manual intervention and maintenance cost and improvement of code security analysis accuracy and overall efficiency are achieved.

The invention discloses an industrial operation gesture recognition model based on deep learning, and the model comprises the following steps: S1, collecting image data of an operation gesture of a worker, and carrying out the preprocessing of the image data; s2, constructing a gesture recognition model by using a convolutional neural network as an infrastructure; s3, training the model by using the preprocessed gesture image data set; s4, integrating the trained gesture recognition model into an industrial operation auxiliary system; the model based on deep learning can automatically learn the complex features of the gesture image and accurately recognize various gestures, the accuracy of industrial operation gesture recognition is effectively improved, misjudgment and missed judgment are reduced, product quality and production efficiency can be improved, for example, in a complex gesture recognition test, the accuracy of industrial operation gesture recognition is improved, and the accuracy of industrial operation gesture recognition is improved. Compared with a traditional method, the accuracy is improved by more than 20%.

The invention relates to the technical field of new energy power prediction, in particular to an extreme weather offshore wind power generation situation prediction method based on artificial intelligence, and the method comprises the steps: obtaining data under historical extreme weather, dividing the data into a training set and an optimization set, removing noise, extracting environment data, and carrying out the feature data fusion. The method comprises the following steps: determining a freezing proportion according to an extreme weather disaster grade, freezing partial layer parameters of a pre-trained conventional power generation prediction model, training an unfrozen layer, constructing an extreme weather power generation prediction model, periodically obtaining data in an optimization set through constructing a simulation time axis, carrying out automatic learning, and finally obtaining environmental data in real time for prediction. And judging the prediction accuracy according to the similarity between the prediction result and the optimization set data, and if the prediction accuracy is not accurate, analyzing an abnormal reason and correcting related parameters. The method provided by the invention effectively overcomes the difficulty of inaccurate offshore wind power generation prediction in extreme weather, and significantly improves the accuracy and reliability of wind power generation prediction under extreme weather conditions.

The invention discloses a target detection-based molten pool working condition monitoring method in a laser directional energy deposition process and application, which uses a deep learning-based target detection network to identify the molten pool working condition, and comprises the following steps of: 1, constructing an image data set of target detection, 2, constructing a target detection model, and 3, identifying the molten pool working condition in the target detection model. Comprising a multi-level feature extraction module, a feature fusion module and an output module, 3, training of a target detection model, and 4, obtaining a gray level image detection result of laser directional energy deposition to be monitored through the target detection model. According to the method, molten pool position information can be automatically detected from the gray scale molten pool image in the laser directional energy deposition process, working condition types can be recognized, intelligent recognition and classification of the working conditions of the molten pool are achieved, the proposed target detection model can directly and automatically learn deep features of the molten pool from the original gray scale image, complex pretreatment steps are avoided, and the working efficiency is improved. The adaptive capacity of the model is remarkably improved through deep learning, and reliable technical support is provided for intelligent monitoring of the laser directional energy deposition process.

The invention provides a spatial intelligent visual physical process inference method based on an implicit physical large model, and belongs to the field of spatial intelligent and artificial intelligence modeling calculation. The problems of low prediction precision and lack of physical consistency for complex physical scenes in the prior art are solved. The method comprises the following specific steps: acquiring multi-modal data of an environment, and representing the multi-modal data in a unified coordinate system; preprocessing the multi-modal data, designing a geometric coding model, a dynamic prediction model and an energy conservation constraint, introducing a space-time attention mechanism, predicting the motion state of an object according to the multi-modal data, and obtaining prediction data of the motion state of the object; acquiring real observation data of object motion, comparing the difference between the prediction data and the real observation data, and performing correction and weight updating on the prediction data of the model through a self-adaptive residual term; according to the method, visual information and implicit physical law modeling are fused, and the self-supervised physical consistency constraint is utilized, so that automatic learning and prediction of a potential physical process are realized.

The invention relates to the technical field of steelmaking continuous casting, in particular to a continuous casting slab longitudinal crack real-time prediction method based on deep learning, which comprises the following steps: data acquisition and preprocessing: acquiring continuous temperature time sequence data on a plurality of rows of thermocouples of a crystallizer copper plate; a hybrid neural network model is constructed, an input layer is used for receiving L * N-dimensional temperature time sequence data, L is a time window length, N is a crystallizer thermocouple number, a feature extraction module comprises a multi-scale feature extraction module and a feature interaction bidirectional LSTM layer, and a classification module constructs a network structure of a full connection layer, a Dropout layer and an output layer; according to the method, online prediction of slab longitudinal cracks is realized, deep features of temperature time sequence data are automatically learned through an end-to-end model, and prediction precision and real-time performance are remarkably improved in combination with a hybrid neural network architecture.

The invention discloses a semantic-driven intelligent query method, system and device based on a security event library and a medium, and the method comprises the steps: improving the query accuracy through deep semantic understanding and cross-modal correlation analysis; potential threat association difficult to recognize can be found through multi-modal semantic association and threat graph construction; the query habit and professional domain knowledge of the user can be automatically learned in combination with a query optimization mechanism driven by reinforcement learning, personalized customization of a query strategy is realized, the manual adjustment frequency is reduced, and the man-machine cooperation efficiency is improved; on the whole, based on a dynamic optimization mechanism of a plug-in architecture and a large vertical model, the system can quickly adapt to changes of a network security environment of a power system, the response speed to novel threats is increased, and the adaptability of the system is enhanced.

The invention provides a satellite-borne single-photon laser radar point cloud self-supervised filtering method and system, and relates to the technical field of laser radar data processing, and the filtering method comprises the steps: carrying out the point cloud self-supervised filtering through a core hypothesis that a source point cloud and a target point cloud which are randomly split from a same coarse filtering point cloud slice should have similar distribution; a completely self-supervised deep learning filtering framework is constructed, and efficient denoising of the satellite-borne single photon point cloud can be realized without manual annotation or external prior knowledge. The statistical consistency of the point cloud is used as a supervision signal, and the limitation that structural noise with a specific mode cannot be effectively filtered out through a traditional threshold value method is overcome. Distribution characteristics of the point cloud are automatically learned through a deep learning model, coordinate correction is carried out, full-automatic processing is achieved, and the processing efficiency of mass satellite-borne point cloud data is improved; according to the method, the fine filtering point cloud after coordinate correction is directly output, and terrain details can be better kept.

The invention belongs to the technical field of wireless communication safety monitoring and intelligent supervision, and particularly relates to a distributed low-cost hotel view-stealing and photo-stealing intelligent supervision system which comprises a wireless probe and a sky-wing cloud wireless analysis management and control platform. The wireless probe is connected to a hotel room router, collects space electromagnetic signals and terminal and AP wireless data, and pre-processes and reports the space electromagnetic signals and the terminal and AP wireless data; the cloud platform is constructed based on OpenStack, network mapping is updated through an ANO adversarial online automatic learning algorithm, wired / wireless attributes and event characteristics are subjected to correlation analysis by means of a VAE-CWGAN model, risks are identified, and an alarm is issued. The system realizes lightweight deployment, low-cost operation and maintenance, real-time intelligent supervision and room-level accurate positioning, can detect hundreds of wireless security incidents and dozens of cameras, is suitable for the fields of secrecy, national security and public security, and assists in security guarantee of smart civilized cities.

The invention relates to an image registration method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a first image and a second image obtained through image collection of a target physiological region, the first image data being a three-dimensional image, and the second image being a two-dimensional image; and determining a deformation field describing a mapping relation between the first image and the second image, and performing nonlinear deformation on the first image according to the deformation field to obtain a deformed image. And projecting the deformed image to obtain a two-dimensional third image capable of measuring a registration effect with the second image. According to the embodiment of the invention, through the architecture for image deformation and projection and image registration based on the deep learning automatic learning deformation field, the elastic registration between the images can be simply, quickly and accurately realized. Meanwhile, the image registration mode is realized by depending on a software algorithm without depending on the improvement of a hardware structure, so that the cost is low.

The invention discloses a shield attitude prediction method, and provides a shield attitude prediction method fusing a channel domain attention mechanism, a convolutional neural network and a transformer model based on a shield attitude change value at each historical moment. The method comprises the following steps: firstly, constructing an input tensor of a model, automatically learning influence weights of different parameters on a shield attitude through a channel domain attention mechanism, and updating to obtain a new input tensor; performing operations such as convolution and pooling on the new tensor through a convolutional neural network, and capturing spatial features of the shield attitude data; the time sequence characteristics of the shield attitude data are mined by using the transform model, and the global characteristics of the data can be mined by using the transform model, so that the prediction precision of the method is improved; and finally, outputting shield attitude data of future time and space through the full connection layer. According to the method, the multi-parameter influence difference and the spatial-temporal characteristics of the shield attitude data are comprehensively considered, and the prediction accuracy of the shield attitude can be improved.

The embodiment of the invention provides an operation track data enhancement method and device, a storage medium and a program product. The method comprises the following steps: generating a hypothesis instruction which can be completed by an error track according to the error track which enables an intelligent agent not to complete a preset instruction; and based on the error trajectory and the hypothesis instruction, performing data enhancement on the agent. According to the technical scheme, the reasonable hypothesis instruction is generated through reverse reasoning of the error trajectory, original invalid operation data are converted into trainable samples, and the data utilization rate is increased. According to a traditional method, a knowledge base only stores a correct operation path and usually discards wrong trajectory data, so that an intelligent agent lacks adaptive capacity to abnormal conditions, and the method solves the problems that a large amount of correct planning data which depends on manual construction exists in the related technology, automatic learning and improvement from errors are difficult to continuously and automatically learn and improve, and the working efficiency is high. And the technical problems of high data construction cost and poor adaptability are solved.

The invention discloses an AI large model fused API asset intelligent management method and system, and relates to the technical field of API data security management, and the API asset intelligent management mainly comprises the following steps: (1) carrying out multi-source API data collection and preprocessing; (2) on the basis of the preprocessed API data, extracting API features by fusing natural language processing and flow feature analysis, and generating an API comprehensive feature vector fusing API document features and flow features; and (3) identifying API assets based on the API comprehensive feature vector, firstly identifying normal API assets to form an enterprise API asset list, and then identifying shadow APIs and zombie APIs based on the enterprise API asset list. According to the scheme, the accuracy of API asset identification can be improved; meanwhile, a deep learning model and algorithm are adopted, features can be automatically learned and extracted, manual intervention is reduced, and efficiency is improved; moreover, according to the scheme of the invention, intelligent identification and anomaly detection of API assets can be realized, shadow APIs and zombie APIs can be found in time, and the security and stability of the system are guaranteed.

The invention discloses a virtual simulation data intelligent generation and enhancement method based on deep learning. The method comprises the following steps: step 1, collecting and converging multi-source heterogeneous simulation data; 2, simulation data preprocessing and feature extraction; step 3, constructing and configuring a depth generation model; step 4, iterative training and optimization of the depth generation model; step 5, intelligently generating and synthesizing new simulation data; step 6, generating data quality verification and enhancement feedback; step 7, carrying out controllability adjustment and directional enhancement on the generated data; and 8, model online learning and data generation closed-loop optimization are carried out. The method can automatically learn the internal law and distribution of the simulation data, efficiently generate highly-vivid and diversified new simulation data meeting specific requirements, and deeply enhance the existing limited data, thereby significantly reducing the simulation data acquisition cost, improving the data quality and richness, and improving the simulation data acquisition efficiency. And powerful data support is provided for development and test of a virtual simulation system and simulation-based AI model training.

The invention relates to the technical field of meteorological disaster monitoring, and particularly discloses a severe convective weather tracking and early warning method based on radar data and deep learning, and the method comprises the steps: collecting original radar data in real time through a laser radar and a spaceborne radar, obtaining the original radar data, and carrying out the cleaning and feature extraction processing, thereby obtaining real-time spatial-temporal feature data; real-time spatial-temporal characteristic data of radar data are automatically learned through a deep learning model, so that the deep learning model has the capability of automatically identifying and tracking a severe convection system, the boundary and strength of the severe convection system are directly output through the deep learning model, and a mask of the severe convection system is directly generated through one-time forward propagation. The calculation amount of radar data is effectively reduced, finally, the future influence area of the severe convection system is predicted based on the evolution characteristics of the severe convection system, early warning information is generated and output, the whole process from data processing to early warning release is free of manual intervention, and the automation degree and efficiency of early warning are improved.

The invention discloses a visual language interaction method and system based on a multi-modal large model, and relates to the technical field of visual language interaction.The method comprises the steps that multi-modal data are collected and preprocessed; a multi-modal large model is constructed, a multi-modal fusion module is additionally arranged in the large model, the module adopts an attention mechanism, the large model can automatically learn the importance degree between different modal data, and effective fusion of multi-modal information is achieved; inputting the preprocessed multi-modal data into a large model, and training and optimizing the large model, so that the large model can better process the multi-modal data; and inputting multi-modal information to the large model, wherein the large model specifically executes the following operations: performing feature extraction on the multi-modal information, fusing features of different modals through a multi-modal fusion module, mining semantic association therein, generating an interactive response according to the fused features, and presenting the interactive response to a user according to a preset output form. The visual language interaction experience in different scenes can be met.

The invention belongs to the technical field of hydraulic engineering safety management, particularly relates to an intelligent monitoring system for hydraulic engineering seepage, and aims to solve the problems that an existing intelligent monitoring system is difficult to adapt to time-varying effects such as material aging and fracture development in an engineering operation period in a use process, so that a simulation result gradually deviates from an actual state, the precision is reduced and the working efficiency is low. In order to solve the problem of poor prediction extrapolation in the face of extreme working conditions, the invention provides the following scheme that the method comprises a sensing array module, and the sensing array module is connected with a data acquisition module. The problem that a simulation result of a traditional fixed parameter model is gradually distorted is fundamentally solved, automatic learning can be carried out along with engineering operation, and a real physical state is continuously approached, so that high precision of seepage trend prediction and scientificity of early warning are still kept under time-varying conditions such as material aging and extreme working conditions.