3457 results about "Learning network" patented technology

The invention provides a dynamic health adaptive monitoring method and system using artificial intelligence, and relates to the technical field of health monitoring, and the method comprises the steps: carrying out the noise reduction through a hierarchical diffusion probability model through multi-source heterogeneous health data, extracting preliminary features through combining with an improved attention auto-encoder, learning discriminative features through a contrast learning network, and carrying out the recognition of the discriminative features; inputting the discriminative features into an adaptive wavelet neural network for multi-scale feature decomposition, inputting the discriminative features into a dynamic graph neural network to obtain a health feature tensor, obtaining time sequence features by using a hierarchical memory enhancement network, and generating a health knowledge graph in combination with a dynamic routing capsule network and a causal reasoning network; and inputting the time sequence characteristics and the health knowledge graph into a deep Bayesian network for health risk assessment, and obtaining an adaptive monitoring scheme through a model prediction control network by using a hierarchical reinforcement learning network and combining a multi-agent collaborative optimization system and a deep deterministic strategy gradient network.

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 provides a cross-modal similarity-based text mining data query method and system, relates to the technical field of data mining, comprises a cross-modal similarity-based text mining data query method and system, and belongs to the technical field of natural language processing. The method comprises the following steps: constructing a multi-level feature extraction network to respectively obtain text, image and time sequence feature vectors; feature fusion is realized by using an adaptive cross-modal contrast learning network, and a cross-modal similarity matrix is constructed; performing hierarchical screening on the target text by adopting a multi-level screening model to obtain a candidate text set; a text relation reasoning module is constructed based on a graph neural network, and text clustering and relation prediction are completed through a multi-task learning framework. According to the method, the accuracy of cross-modal text data query is improved, the adaptivity of feature fusion is enhanced, and efficient text relationship mining is realized.

The invention provides a chip production logistics optimization scheduling method and system based on deep reinforcement learning, and relates to the technical field of deep reinforcement learning. An improved bidirectional long-short-term memory network and a graph neural network are used for extracting a material flow time sequence rule, equipment operation cycle characteristics and logistics network topology characteristics, a fusion state representation is generated through a contrast learning network, a layered and collaborative deep reinforcement learning architecture is constructed, an improved encoder-decoder structure is used for generating a strategy vector, and the strategy vector is constructed. And performing hierarchical joint training through a multi-agent execution network, inputting a real-time state and a strategy vector into the trained deep reinforcement learning architecture, generating a scheduling instruction, and performing hierarchical model prediction control in combination with a Bayesian neural network ensemble model and a recurrent neural network prediction model to realize optimized scheduling of chip production logistics.

The invention provides a reinforcement learning-based database adaptive data flow acquisition optimization method and system, and relates to the technical field of databases, and the method comprises the steps of constructing a distributed acquisition proxy network, obtaining database data flow information, and carrying out multi-modal feature decomposition and fusion. And calculating an optimal initial acquisition parameter combination by using the fusion feature vector and combining a genetic algorithm, constructing a double-layer deep Q learning network structure, performing hierarchical training, and generating a hierarchical acquisition strategy model. The model adaptively adjusts a global acquisition strategy and parameters according to a characteristic drift degree and a performance index so as to realize continuous optimization of the acquisition strategy. According to the method, through multi-modal feature fusion, reinforcement learning and a self-adaptive tuning mechanism, the data flow collection efficiency is effectively improved, the collection delay is reduced, and the resource utilization rate is optimized.

The present invention provides an automatic intelligent inspection system for the equipment, facilities and surrounding environment along a railway line, including an unmanned aerial vehicle (UAV), a movable ground base station, a remote server and a working computer, wherein the working computer includes an image processing module that is configured to detect an obtained image by using a deep learning network model trained with a database in advance, obtain any object that is suspected to involve a defect by analysis, and transmit the object for manual review while calculating for the specific geographic coordinate information of the defect, report for early warning after the defect is confirmed by the manual review, inform the operation and maintenance personnel, and store and record the defect. The present invention further provides a method for inspection by using the automatic intelligent inspection system for the equipment, facilities and surrounding environment along a railway.

The invention discloses a deep learning-based deep fusion network production line fault prediction method, which comprises the following steps of: firstly, acquiring SCADA (Supervisory Control And Data Acquisition) system or sensor data of an automatic production line, and carrying out data preprocessing, feature extraction and fault prediction label construction; then a deep fusion network 1D-CNN-LSTM-Attention-DCE model combining a one-dimensional convolutional network, a long short-term memory neural network, a fusion attention mechanism and a dynamic cross entropy loss function DCE is established, the preprocessed data is input into the constructed model for training, and finally the trained model is used for fault prediction to obtain a fault prediction result. According to the method, a 1D-CNN-LSTM-Attention-DCE fault prediction model is provided, local features are extracted from time sequence data by using the 1D-CNN, then a long-time dependency relationship is captured through LSTM, and finally weight distribution of key features is enhanced through an attention mechanism, so that the processing capability of the fault prediction model on complex time sequence data is comprehensively improved, and the fault prediction efficiency is improved. And the fault of the production line can be accurately predicted within a certain time length range.

The invention relates to the technical field of digital factories, and discloses an equipment real-time monitoring and fault prediction method and system based on a digital factory. The method comprises the following steps: carrying out iterative filtering interpolation processing on equipment operation data collected by a multi-modal sensor in a digital factory to obtain smooth time sequence data, and constructing a multi-modal feature matrix based on the smooth time sequence data; inputting the multi-modal feature matrix into a joint training generalized Siamese-like wide learning network for model training to obtain an equipment state monitoring model; executing system-level state evaluation and subsystem-level fault prediction calculation based on the equipment state monitoring model, and generating an equipment operation and maintenance strategy; according to the equipment operation and maintenance strategy, a fault risk heat map is generated, a digital twin visualization system is constructed, and an equipment maintenance scheduling scheme is output, the production efficiency and equipment health management are balanced, and collaborative optimization of a maintenance plan and a production plan is achieved.

The invention provides an anomaly detection and maintenance decision optimization method and system for automatic test equipment, and relates to the technical field of automation, and the method comprises the steps: deploying an edge calculation unit and a multi-level sensor network to collect equipment data, carrying out the fault diagnosis through a deep network model, and constructing a maintenance decision model in combination with equipment operation data. The model is input into a quantum enhancement dual-network system to calculate a reward value, and is used for training a maintenance strategy network under a federal reinforcement learning framework. And generating a maintenance resource configuration scheme through a quantum evolutionary algorithm, and generating a maintenance scheme through optimization of the multi-criterion evaluation model and the self-adaptive multi-target differential evolutionary algorithm. According to the scheme, a high-fidelity digital twin model and a multi-task deep transfer learning network are input for verification and updating, updated data are transmitted to a field control system through an industrial Internet of Things platform, and the updated data are recorded by using a block chain technology for updating a deep network model.

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 discloses a multi-modal diagnosis and treatment data analysis and auxiliary diagnosis method for internet medical treatment, which comprises the following steps: S1, performing feature extraction and fusion on multi-modal diagnosis and treatment data through a nonlinear mapping algorithm based on quantum state optimization; s2, constructing a self-adaptive dynamic relation network, and capturing a complex interaction relation between cross-domain features in the diagnosis and treatment data; s3, fusing implicit features in the sparse medical data with semantic association; s4, optimizing the diagnosis and treatment decision process by applying a causal inference technology; s5, processing anomalies in the diagnosis and treatment data by using a fusion generative adversarial network and sparse tensor decomposition technology; s6, constructing a decentralized encrypted federated learning network to protect the privacy of the patient; s7, predicting a disease evolution path in real time to form dynamic decision support; and S8, performing accelerated analysis on the high-dimensional feature mode in the diagnosis and treatment data in combination with quantum calculation. The method has the advantages of high data fusion capability, high diagnosis precision and high real-time decision support capability.

The invention provides an unmanned aerial vehicle return detection method and system based on AI recognition, and relates to the technical field of image recognition, and the method comprises the steps: extracting multi-scale features through an improved moving window hierarchical converter model, obtaining target attitude information through the combination of cross-modal comparative learning and a dynamic routing capsule network, and obtaining target attitude information; an alignment feature representation is obtained by using a feature enhancement module and an adversarial feature alignment method, the alignment feature representation is input into a graph attention diffusion model to obtain an accurate state representation, and a state evolution trajectory is obtained in combination with an adaptive causal convolutional network and an ordinary differential equation network; posterior distribution is determined through a variational inference network and an autoregression flow model, a state sequence is predicted by using a neural process network and a dynamic point process model, an expert strategy is learned by using a hierarchical generative adversarial imitation learning network and a distributed trust domain evolutionary strategy algorithm, and optimal parameter configuration is obtained; and accurate prediction of the return state of the unmanned aerial vehicle is realized.

The invention relates to the technical field of image processing, and discloses a detection method, system and equipment for intelligent welding of pipe pile splicing and a medium. The method comprises the steps that noise reduction is conducted on a target weld seam image through a filtering algorithm, registration and splicing are conducted on the image through feature matching, and a weld seam unfolding image is obtained; segmenting the weld joint expanded image by using a deep learning network, and extracting a weld joint center line and surface features to obtain weld joint feature data; defect detection and recognition are conducted on the weld joint feature data, hierarchical calculation is conducted through a rule set, and weld joint quality data are obtained; parameter mapping is conducted according to the weld joint quality data, a welding parameter instruction is generated, welding process variables are adjusted, and target welding parameters are obtained; and the welding seam quality data and the target welding parameters are stored, and a quality report is generated through data analysis. The detection accuracy of intelligent welding for pipe pile splicing is improved.

The invention relates to a multi-protocol transmission text data monitoring and warning method and system, and the method comprises the steps: generating a multi-source protocol transmission instance based on dynamic authorization and hardware security verification, and collecting and analyzing text data; a network connection state, a data backlog amount and sensor numerical value content parameters are monitored in real time through multiple threads, and a transmission state and content exception event queue is generated; learning a causal relationship among network congestion, equipment faults and alarm events by using a Bayesian network algorithm, calculating a root cause probability in combination with a dynamic weight distribution strategy, and generating a comprehensive alarm list of priority ranking; on the basis of user feedback data, protocol weights and alarm strategies are adaptively updated, abnormal early warning triggering, data snapshot binding and closed-loop optimization of alarm logs are achieved, and the problems that in a multi-protocol mixed transmission scene, safety adaptability is poor, the monitoring dimension is single, root cause analysis depends on static rules, and strategy updating lags are solved. And the real-time performance, the accuracy and the self-adaptability of data transmission of the industrial Internet of Things are improved.

The invention discloses a reinforcement learning unmanned forklift truck obstacle avoidance scheduling method and system for a dynamic obstacle, relates to the technical field of unmanned driving, and discloses the reinforcement learning unmanned forklift truck obstacle avoidance scheduling method for the dynamic obstacle. According to the method, the system and the system, the system and the system, global path planning and local obstacle avoidance decision making are carried out in combination with the reinforcement learning network, the problems of obstacle avoidance response delay and unreasonable path planning in a dynamic environment are solved, the obstacle avoidance response speed, the path planning rationality and the multi-modal data fusion precision in the dynamic environment are improved, and then the safety and efficiency of unmanned forklift dispatching are improved.

The invention relates to a drug-disease relationship prediction method and system based on a dual-channel fusion knowledge graph. The method comprises the following steps: S1, constructing a biomedical fusion knowledge graph; s2, constructing a drug-disease sub-graph based on the biomedicine fusion knowledge graph; s3, constructing a dual-channel adaptive fusion feature module, and embedding a drug-disease sub-graph; s4, performing enhanced splicing on the drug-disease sub-graphs in the dual-channel fusion knowledge graph pre-embedded network, importing an edge-node iterative updating learning mechanism, training a spliced sub-graph relation perception learning network, and obtaining enhanced sub-graph feature embedding; and S5, utilizing the trained enhanced subgraph features to embed, calculate and output the prediction probability of the drug-disease relationship. According to the method, the semantic representation capability of the knowledge graph and the topological modeling advantages of the graph neural network are integrated, and efficient and accurate prediction of the drug-disease relationship is realized through multi-modal feature interaction, sub-graph enhancement and a dynamic sub-graph learning mechanism.

The invention provides a deep learning-based avalanche early warning model construction method and system, and the method comprises the steps: firstly obtaining multi-source environment monitoring data, including meteorological time sequence, topographic space and accumulated snow layer physical data, of a target region, carrying out the time dimension alignment of the meteorological time sequence data to generate a feature sequence, carrying out the meshing of the topographic space data to generate a feature set, and carrying out the construction of an avalanche early warning model; the method comprises the following steps: extracting parameters from accumulated snow layer physical data to generate a state vector, inputting a deep learning network model containing time sequence attention, spatial convolution and cross-modal interaction units, generating a fusion feature vector, constructing a training set based on historical avalanche event annotation data, performing dynamic weight optimization on the fusion feature vector, and generating an avalanche risk prediction model. And finally, receiving current monitoring data in real time, outputting a risk level and an early warning trigger threshold value by the avalanche risk prediction model, and generating a multi-level early warning signal when a real-time risk value exceeds the threshold value, thereby realizing accurate avalanche early warning.

The invention discloses a surgical robot resection navigation method based on multi-modal image registration. The method comprises the following steps: acquiring a preoperative multi-modal medical image of a patient and reconstructing a three-dimensional model containing a focus and a dangerous structure; acquiring an intraoperative real-time image; extracting cross-modal invariant features from the pre-operative and intra-operative images by using a first deep learning network, and establishing an initial corresponding relation; predicting an initial non-rigid deformation field by using a second deep learning network, based on the pre-operative / intra-operative image and the initial corresponding relation and in combination with physical model constraints; tracking a surgical tool in real time, and updating a non-rigid deformation field in real time by adopting an incremental calculation strategy; applying the updated deformation field to the preoperative three-dimensional model to generate a deformed model; and mapping the tool position to the deformed model, and generating a navigation instruction. According to the method, soft tissue deformation can be accurately compensated, multi-modal image information is effectively fused, navigation information is dynamically updated in real time, and the precision and safety of robot surgery are remarkably improved.

The invention discloses an image self-supervised learning method and system fusing contrast learning and feature mask modeling. An original image which is low in cost and does not need to be labeled is used as training data, a double-branch self-supervised learning network is constructed, one branch is a comparison learning branch based on a SimCLR framework, the other branch is a mask self-encoder branch based on a mask feature model, and the whole network comprises an image preprocessing module, an image feature extraction module and a shared weight encoder. And carrying out self-supervised training by combining the two branches with a loss function specially designed for the two branches, and finally obtaining a pre-training model capable of fully extracting image features. According to the method, the advantages of comparative learning and feature mask modeling are combined, information is effectively extracted from unlabeled image data, and image understanding is enhanced. The method has the main advantages that the robustness is high, the method is particularly suitable for limited data sets in the remote sensing and medical fields, the model precision is improved, and the data requirement and labeling cost are reduced.

The invention relates to the technical field of power station operation and maintenance, and discloses a power generation equipment intelligent operation and maintenance method and system based on model autonomous selection, and the method comprises the steps: obtaining the multi-mode operation and maintenance data of a photovoltaic power station, and generating a multi-mode operation and maintenance data set; inputting each modal data of the multi-modal operation and maintenance data set into a corresponding module for feature extraction; inputting the extracted feature vectors into a contrast learning network for cross-modal alignment, and outputting an executable decision result by combining a retrieval enhancement generation technology with a knowledge graph; and constructing a privacy protection training framework through federated learning, inputting an executable decision result into a digital twin system for strategy verification, and generating a trained multi-modal large model for the photovoltaic power station to select a corresponding module in the trained multi-modal large model based on the feature data for real-time monitoring. According to the invention, the problems of poor accuracy and untimely reaction in the traditional operation and maintenance process of the photovoltaic power station are solved, and the operation and maintenance of the power station can be carried out timely and accurately.

The invention discloses a financial network security defense method and system based on multiple Agents and a dynamic large model. A detection Agent is deployed in an edge layer, financial network node flow data and system logs are collected in real time, time sequence features are extracted through a lightweight convolutional network, and a preliminary anomaly score is generated. And the cloud layer constructs a decision Agent, receives the feature abstract transmitted by the edge node in an encrypted manner, inputs the feature abstract into a dynamic large model for multi-modal feature fusion, and outputs defense action probability distribution. And the intelligence Agent constructs a cross-institution federated learning network. And constructing a dynamic game engine, constructing a revenue matrix based on the attack cost and the defense revenue, solving a Nash equilibrium strategy, and generating an optimal defense instruction set. And dynamically allocating detection tasks according to the threat level and the edge computing power state. According to the method, efficient acquisition and analysis are realized, the abnormal behavior recognition capability is improved, support is provided for making a defense strategy, the defense strategy is optimized, and the intelligent, automatic and efficient levels of defense are improved.

The invention discloses a cloth defect detection method based on multi-scale feature fusion and a diffusion pyramid network. The method comprises the following steps: S1, constructing a defective cloth image data set; s2, building a deep learning network model which comprises a backbone feature extraction network, a neck feature focusing diffusion pyramid network and a cross-stage multi-scale feature fusion module used in the backbone feature extraction network, the feature focusing module and the detection head structure are used in the neck feature focusing diffusion pyramid network; s3, training the deep learning network model; and S4, detecting a to-be-detected cloth image based on the trained deep learning network model. According to the invention, by introducing the cross-stage multi-scale feature fusion module, detail features of flaws in cloth can be effectively captured, so that the accuracy and robustness of detection are improved.

The invention relates to the technical field of crack identification, and discloses a crack identification analysis method and system for a building shear wall, and the method comprises the steps: carrying out the multi-angle shooting and image standardization processing of the surface of the building shear wall, and obtaining a standardized image data matrix; performing multi-model degradation processing and multi-angle image combination to obtain target enhanced image data; inputting the image into a convolution denoising auto-encoder for noise separation to obtain a denoising feature image matrix; inputting a dynamic graph convolutional neural network to carry out local feature and global feature extraction to obtain a local crack feature matrix and a global crack feature matrix, and carrying out feature fusion to obtain a fused feature matrix; and inputting the twin metric learning network to carry out feature measurement and crack classification, and outputting a target crack classification result, thereby realizing effective capture of deep semantic information between crack areas of the building shear wall, and realizing efficient integration of crack features of different scales.

The invention discloses an image super-resolution reconstruction method based on deep learning, and belongs to the technical field of image signal processing. Comprising the following steps: S1, realizing multi-dimensional feature representation of a low-resolution input image by using a deep learning network; s2, realizing accurate alignment and lossless mapping of cross-scale features, and effectively solving the problems of feature dimension inconsistency and information distortion; s3, explicitly learning an increment mapping relation between low-resolution input and a reconstructed high-resolution image, and relieving the gradient disappearance problem of a deep learning network structure; s4, enhancing the naturalness and detail fidelity of the reconstructed image through adversarial training; s5, integrating pixel-level reconstruction loss, perception loss and structural similarity loss, and effectively balancing objective indexes and subjective visual quality of the reconstructed image; and S6, realizing joint optimization of deep learning network parameters through a unified gradient back propagation mechanism, and finally outputting a high-quality super-resolution reconstructed image.

The invention discloses an image label noise learning method based on granular ball calculation and contrast learning, and the method comprises the steps: obtaining image data, carrying out the preprocessing of the image data, inputting the preprocessed image data into a trained image label noise learning model, and obtaining an image classification result. The training process of the image label noise learning network comprises the following steps: acquiring an image training data set, and performing preprocessing and data enhancement on the image training data set; respectively extracting feature representations of the strong data enhancement sample and the weak data enhancement sample by adopting a contrast learning framework SimSiam, inputting the feature representation of the weak data enhancement sample into a granular ball calculation layer, inputting a feature matrix of the granular ball sample and a label thereof into a classifier after clustering to obtain an image classification result p, and calculating classification loss; predicting the feature representation of the strong data enhancement sample through a prediction layer, and calculating comparison loss by taking an image classification result p as an indicator function; and the network is optimized through back propagation of the comparison loss and the classification loss. According to the invention, the network performance can be obviously improved.

The invention discloses an industrial environment information wireless monitoring system based on the Internet of Things and a control method thereof, and relates to the technical field of industrial management, and the system comprises an intelligent edge calculation module, a data visualization module, a self-healing network management module, a precise energy consumption management module, an environment anomaly detection module and a wireless network optimization module. According to the industrial environment information wireless monitoring system based on the Internet of Things and the control method thereof, real-time analysis and decision are realized at a data source by using an intelligent edge computing module, data transmission delay is reduced, response can be quickly made in an emergency, potential faults are predicted through a time sequence abnormity self-learning network model, and the monitoring accuracy of the industrial environment information wireless monitoring system based on the Internet of Things is improved. And network self-healing is actively carried out before a problem occurs, so that the network downtime is effectively reduced. In addition, due to the introduction of a low-power-consumption ad hoc network protocol, the energy consumption optimization management of the sensor nodes is realized, and the service life of equipment is prolonged.

The invention provides a tree identification method and system based on ground-air fusion three-dimensional laser point cloud, and belongs to the field of environment remote sensing data identification, and the method comprises the steps: obtaining laser radar point cloud data in a set forest region through employing an unmanned plane, and collecting the point cloud data in the set forest region through employing a laser scanning device; preprocessing the two groups of data to obtain preprocessed laser radar point clouds, and performing ground-air data seamless fusion on the preprocessed laser radar point clouds to obtain fused data; generating a canopy height model based on a watershed segmentation algorithm, and performing individual tree segmentation on the fused point cloud data by using the model; extracting a vector boundary after single tree segmentation, distributing a tree ID for each tree, and extracting a single tree point cloud according to the tree ID; the single tree point cloud is further processed to construct a multi-angle point cloud sample data set; and training a deep learning network model by using the multi-angle point cloud sample data set, and detecting the tree species to be detected by using the trained deep learning network model.

The invention discloses a deep interpretable skin disease identification method based on small sample learning, which belongs to the field of image processing, and comprises the following steps of: respectively inputting similar or heterogeneous skin disease images into a double-branch small sample contrast learning network, segmenting a lesion area of the skin disease image to generate lesion segmentation masks, extracting shape information in the lesion segmentation masks, and identifying the shape information of the lesion segmentation masks; positioning a minimum square frame containing a lesion area, and performing clipping and zooming to obtain a lesion image; extracting the depth features of the branches corresponding to the lesion images of the two branches, calculating the contrast loss of the depth features of the double branches to optimize network parameters, and extracting multi-scale texture information at the same time; metadata containing shape information and texture information and depth features of an image are subjected to multi-modal fusion for skin disease identification, and through combination of a multi-stage deep learning network and medical diagnosis logic, features such as form and texture of a lesion area are extracted and fused step by step. The classification accuracy of skin disease identification and the interpretability of the model are improved.

The invention discloses an amphibious unmanned aerial vehicle riverway sediment concentration real-time monitoring method based on underwater light field imaging and cross-modal fusion, and relates to the technical field of riverway sediment monitoring. Comprising the following steps: acquiring aerial multispectral data and preprocessing the aerial multispectral data; an integrated polarized light field camera is carried on an amphibious unmanned aerial vehicle to capture underwater light field data, and underwater three-dimensional light field information is reconstructed and sediment scattering characteristics are extracted through an optical model and a lightweight deep learning network; the air multispectral data and sediment scattering characteristics are fused through space-time alignment and an attention mechanism, a sediment concentration prediction model is generated, and then a spatial distribution diagram of the river sediment concentration is output; and carrying out real-time monitoring and early warning on the river sediment concentration according to the spatial distribution diagram of the river sediment concentration through edge calculation and cloud collaboration architecture. The method is combined with an advanced deep learning algorithm, high-resolution silt concentration inversion can be realized, and the method has real-time performance and adapts to a complex water body environment.

The invention discloses a short-time rainfall prediction method based on radar image and reanalysis data fusion, and the method comprises the following steps: collecting radar images and reanalysis data at continuous times, and generating input data in a unified grid format through spatial interpolation, time alignment and standardization processing; respectively extracting spatial and temporal features of the radar image and the reanalysis data by using a dual-channel encoder, and carrying out weighted fusion through a channel attention mechanism to generate a fusion feature tensor; inputting the fusion features into a ConvLSTM (Convolutional Long Short-Term Memory Neural Network), modeling a spatio-temporal evolution process of a rainfall system, and outputting a preliminary rainfall prediction image in 0-3 hours in the future; constructing a residual learning network, and performing deviation correction on the preliminary prediction result based on historical residual and observation information; when the radar image input is missing, the completeness of the input structure is maintained through the replacement feature generation module; generating a rainfall intensity image or a probability graph in 0-3 hours in the future; the method supports visual output.