159results about How to "Raise attention" patented technology

The invention discloses a PPY-YOLO-based steel surface defect detection method and a PPY-YOLO-based steel surface defect detection system. The PPY-YOLO-based steel surface defect detection method comprises the following steps: step 1, preprocessing a steel surface defect image; 2, a neck structure Faster-Neck, a deep learning module C3K2Faster and a spatial pyramid pooling module SPPFSim are introduced into the YOLOv11 network, and a PPY-YOLO target detection model is obtained; step 3, training a PPY-YOLO target detection model; and 4, acquiring a steel image, and inputting the steel image into the trained PPY-YOLO target detection model to obtain the category and positioning information of the steel surface defects. A new model is constructed, the backbone network and the neck network are improved, the backbone network is formed by connecting the input end of the C2PSA module with the output end of the SPPFSim module, and an up-sampling group and a down-sampling group are added to the neck network, so that the model has a relatively good detection effect.

The invention relates to the technical field of machine learning, in particular to a supervised time sequence water level data generation method and system and a storage medium. The method comprises the following steps: acquiring multi-source hydrological monitoring data; performing abnormal value identification and data preprocessing according to the hydrological monitoring data to generate standardized hydrological basic data; constructing a data generation model of the conditional generative adversarial network based on the standardized hydrological basic data; hydrological variable driven condition feature regulation and control are carried out on the data generation model, parameter optimization of the generation network is carried out, and hydrological adaptability enhanced condition input feature data are generated; and injecting the condition input characteristic data into the data generation model to generate initial water level time sequence data. According to the method, the rare extreme hydrological events are oversampled by using a hierarchical importance sampling and self-service method (Bootstrap), so that the performance of the generative model in rare event prediction is remarkably enhanced.

The invention belongs to the technical field of power distribution network fault recovery, and provides a power distribution network fault recovery method and system based on multi-agent reinforcement learning, and the method comprises the steps: taking a recovered load as a target function based on the load priority of a power distribution network, and taking the operation limitation of each node line in the power distribution network as a constraint condition; constructing a power distribution network fault recovery problem model; based on the power distribution network fault recovery problem model, constructing a multi-agent reinforcement learning agent set, and converting the power distribution network fault recovery problem into a Markov decision process; and a multi-agent reinforcement learning framework is constructed based on the agent set of multi-agent reinforcement learning, multi-agent training is carried out, each agent generates a decision according to respective observation information, and a fault recovery strategy is obtained. The objective function and the constraint condition constructed based on the load priority of the power distribution network and the node line operation limitation can ensure the accuracy and the feasibility of the recovery strategy. And the strategy generation time is shortened through the multi-agent parallel decision-making process.

The invention relates to a multi-modal implicit sentiment analysis method based on a multi-granularity attention mechanism, which belongs to the field of artificial intelligence, and comprises the following steps: S1, extracting visual features in image information by using a visual feature extraction strategy of multi-scale attention fusion; s2, generating a text description of the image information by adopting an image translation engine, splicing the text description with an original text, and extracting text features through a hierarchical attention mechanism; s3, carrying out normalization processing; s4, performing multi-modal fine-grained correlation fusion on the visual features and the text features by adopting a bidirectional cross attention mechanism, and extracting fine-grained features in the visual features and the text features through a multi-head attention mechanism and residual connection; and S5, fusing the fine-grained features by using an adaptive multi-modal classification module to complete sentiment classification. The multi-modal sentiment analysis method solves the problems that multi-modal sentiment analysis is insufficient in inter-modal correlation mining, insufficient in fine-grained information capture and large in noise interference.

The invention provides a data security traceability method and system based on artificial intelligence, relates to the field of data security, and is used for solving the problems of relatively poor data security traceability accuracy and response delay in the prior art. The method comprises the following steps: updating a user behavior attention model, and carrying out multi-dimensional aggregation analysis on all data flow paths comprising abnormal nodes and abnormal edges; determining the path with the highest comprehensive score value in all the data flow paths as a target abnormal path; performing data security traceability analysis on the target abnormal path to obtain a traceability analysis result; and determining a chain creation rule, taking each data circulation event on the target abnormal path as a block in combination with the chain creation rule, connecting the data circulation events in series to form a traceability chain, and starting a corresponding safety response strategy according to a traceability analysis result, the safety response strategy being obtained through self-adaptive generation according to the traceability chain. According to the technical scheme provided by the invention, the accuracy of data security traceability is improved.

The invention provides a sick juvenile fish identification method based on an improved YOLOv10 model, and the method comprises the steps: collecting a fish image in a water area as a to-be-detected image, and carrying out the preprocessing of the to-be-detected image, and obtaining an image data set; the method comprises the following steps: constructing a YOLOv10-UDFishNet model; the image data set is input into the YOLOv10-UFFishNet model, the YOLOv10-UFFishNet model is trained through the image data set, and the trained YOLOv10-UFFishNet model is obtained; and inputting a fish image in a water area to be detected into the trained YOLOv10-UFFishNet model to obtain a fish detection result and position information of a fish individual target. According to the method, the sick juvenile fish in the turbid underwater environment can be accurately and efficiently identified.

The invention discloses a cross-modal foreground distillation method for multi-view three-dimensional target detection, and belongs to the technical field of automatic driving perception. According to the method, a laser radar or a multi-modal three-dimensional target detection model is used as a teacher model, a multi-view aerial view BEV perception three-dimensional target detection model is used as a student model, and the model performance is improved through multi-scale feature distillation; a space attention mechanism and a foreground mask module are introduced, and the student model can simulate feature responses of the teacher model in different space positions under multiple scales, focus on a foreground target and suppress background noise; the adaptive module ensures the consistency of the multi-scale features in feature dimensions and spatial resolution, and finally, the difference between the features and regression output is measured through loss functions such as mean square error and KL divergence, so that efficient cross-modal and multi-scale foreground feature distillation is realized. Finally, the multi-view three-dimensional target detection precision and efficiency are improved, and meanwhile, the distillation requirements of multi-scale features are fully considered.

The invention discloses a self-adaptive multi-hop retrieval knowledge graph construction method for large language model questions and answers. The method comprises the following steps: extracting triples of an input question and a corpus; self-adaptive multi-hop retrieval matching is carried out, and a specific triple related to the question in the corpus is retrieved and recognized; constructing a knowledge graph by using the question in the adaptive multi-hop retrieval matching process and a corpus triple; the answer predictor model comprises node embedding, message passing and selective filtering, and the node embedding is used for encoding questions and a knowledge graph; carrying out message transmission based on the coded problem and the knowledge graph based on a graph convolutional network; selective filtering is used for filtering redundant information of the knowledge graph; according to the method, the problem that the large language model generates illusion can be relieved, the complex reasoning process can be understood, and therefore the question answering and logical reasoning performance of the large language model is improved.

The invention discloses a gas outburst monitoring and early warning method and system based on an improved ConvLSTM model, and relates to the technical field of gas monitoring, and the key points of the technical scheme are that the method comprises the following steps: constructing a mine gas data set; cleaning the gas data set, converting unstructured data into structured data, and performing data cleaning, denoising and standardization on the structured data; performing high-order derived feature extraction on the preprocessed gas data set; an improved ConvLSTM model is constructed; using a historical gas data set to train the improved ConvLSTM model; identifying a potential gas high-risk area; based on an analysis result of the improved ConvLSTM model, identifying a potential gas outburst ignition risk area; and constructing a monitoring platform. By improving the ConvLSTM model, efficient collection and processing of gas data are achieved, monitoring accuracy and real-time performance are improved, and powerful support is provided for mine safety management.

The invention discloses an AI algorithm optimization method and system based on a deep learning technology, and relates to the technical field of deep learning model optimization, and the method comprises the steps: carrying out the preprocessing of a data set, carrying out the normalization and standardization processing, and dividing the data set into a training set, a verification set and a test set; a self-attention mechanism is introduced into a convolutional layer of the ResNet-18 deep learning model, and the weight of each channel is dynamically adjusted by calculating a self-similarity matrix of a feature map; training the model by using the training set data, and dynamically adjusting the model learning rate in the training process; an optimal hyper-parameter combination is automatically searched through a hyper-parameter tuning method; the number of model parameters and the calculation amount are reduced through a static quantification method; exporting and deploying the model, importing the test set, and testing the performance of the model; according to the method, a self-attention mechanism is introduced into the convolutional layer of the ResNet-18 model, the weight of each channel is dynamically adjusted, the attention of the model to important features is enhanced, and the feature extraction precision is improved.

The invention discloses a physically-guided critical region fluid nonlinear wave motion prediction method, system and device and a medium. The method comprises the steps that a critical region fluid computational domain and a nonlinear time-varying equation are established; constructing a sample data set; constructing a physical information neural network model; constructing a loss function including nonlinear time-varying equation residual error loss, initial point error loss and boundary point error loss; training the physical information neural network model to obtain a critical region fluid free water surface displacement wave envelope prediction model; weighting internal matching point samples in each iteration round by adopting a gradient-free nonlinear attention mask method based on residual accumulation in the training process; and inputting the time-space coordinates of the to-be-predicted point of the critical region fluid into the prediction model to obtain the free water surface displacement wave envelope of the to-be-predicted point fluid. According to the method, error accumulation is effectively reduced, the model convergence speed is increased, and the prediction precision of the nonlinear wave motion of the fluid in the critical region is improved.

The invention relates to the technical field of mars surface rocks, and particularly discloses a mars surface stone extraction method and system based on a lightweight network, and the method comprises the steps: obtaining a mars surface image shot by a mars patroller, and screening a rock image; making labels based on the rock images, performing image processing on the labels and the rock images to expand the number of the images, and dividing the rock images into a training set, a test set and a verification set; dynamically adjusting the sampling shape and size of a convolution kernel of the training set through adaptive kernel convolution so as to extract rock features in the rock image; separating the rock image from the surrounding environment by using an improved lightweight Transform model; the attention of rocks covered by sand and dust is improved through an enhanced multi-dimensional convolution attention mechanism; and evaluating the lightweight Transform model based on the pre-training index, if the evaluation passes, obtaining a final model, and based on the final model, carrying out rock surface extraction on the input Mars rock image.

The invention relates to a network abnormal behavior detection method based on dynamic data balance and generation, and the method comprises the steps: obtaining original network flow data, inputting the original network flow data into a dynamic abnormal data balance model, and obtaining an enhanced data set; the method comprises the steps of marking original network flow data based on an abnormal data balance model, performing selective undersampling on marked data, adjusting a ratio of normal data to abnormal data, obtaining a balance data set, generating new abnormal data according to the balance data set, and merging the new abnormal data with the balance data set to obtain an enhanced data set; and inputting the enhanced data set into a TimeDiT noise reduction model for denoising to obtain a target data set after noise reduction, inputting the target data set into an LSTM-Transform weighted training model for network abnormal behavior detection, and weighting abnormal data in the target data set in combination with a weighted loss function to obtain a detection result. According to the invention, the network traffic abnormal behavior detection efficiency and accuracy can be improved.

The invention belongs to the field of traffic control systems, and relates to a vehicle driving track prediction system and method based on an improved graph neural network model, and the system comprises an FPGCN model and a Social LSTM model. The FPGCN model comprises a graph convolution layer, a Dropout layer, a differential pooling layer, a feature fusion module and a full connection layer; obtaining node feature vectors from graph structure data through a graph convolution layer, a Dropout layer and a differentiable pooling layer, then dividing and conquering node features, generating global features through a dense attention mechanism and principal component analysis, and generating local features through virtual filling and matrix calculation; after global features and local features are obtained, a feature fusion module is used for fusion, then the features are input into a full connection layer, and the influence score of a historical trajectory diagram on a current state diagram is calculated; integrating the preprocessed driving data and the data of which the score is calculated by the FPGCN model, and inputting the integrated data into a Social LSTM model; according to the system, while the model expression ability is maintained, the parameter quantity is remarkably reduced, the calculation logic is optimized, and the prediction accuracy is improved.

The invention discloses a regional power grid wind power generation power prediction optimization method which comprises the following specific steps: firstly, collecting data such as wind speed and wind direction through meteorological sensors arranged in a wind power plant and around the wind power plant, and carrying out cleaning and de-noising preprocessing; secondly, decomposing the meteorological data into intrinsic mode function components with different frequencies by adopting variational mode decomposition, constructing a convolutional neural network to perform feature extraction on each component, and adaptively adjusting the size of a convolution kernel according to the periodic characteristics of the data; then, a bidirectional long-short-term memory network based on an attention mechanism is used for fusing features, time sequence information is captured, power prediction is achieved through a full-connection neural network, and the weight of the FCNN is initialized through a random forest regression model to improve the initial performance of the model; according to the method, the prediction precision is improved through multi-level characteristic decomposition and a self-adaptive mechanism, the real-time response capability is good, and the requirements of the regional power grid for the precision and timeliness of wind power generation power prediction are met.

The invention relates to a wind power prediction feature extraction and fusion method based on multi-source space-time attention, and the method comprises the following steps: carrying out the optimization processing of discrete NWP data, and carrying out the error correction through Kalman filtering; establishing a multi-source space-time attention graph convolution model, performing feature extraction through superposition of space attention and time attention, and performing one-dimensional convolution on a time scale to realize extraction and fusion of depth features; establishing a multi-layer time feature extraction and fusion model, performing assignment on features by using position information codes and time information codes, and performing feature activation and fusion by using cross linear attention and a multi-layer perceptron; discrete NWP data is processed to form a data format suitable for model input, and effective prediction and feature fusion of wind power are realized; the method has the advantages that an embedded representation and fusion method is utilized, station position information is considered, a feature extraction and cross fusion model is established, and optimal features are extracted and fused.

The embodiment of the invention relates to the field of train safety detection, and discloses a bolt looseness detection method, device and equipment and a storage medium, and the method comprises the steps: obtaining a train image, and at least part of the train image comprises an area where a bolt is located; inputting the train image into a positioning identification model to identify an area where the bolt is located, and determining area boundary information of the area where the bolt is located; segmenting the train image according to the area boundary information of the area where the bolt is located, and generating an image of the area where the bolt is located; and inputting the image of the area where the bolt is located into the bolt looseness detection model for bolt looseness detection. According to the method, the positioning recognition model is utilized to position and segment the area where the bolt is located for the input train image, and then the bolt looseness detection model is utilized to accurately detect the looseness and falling of the bolt for the segmented image, so that the area where the bolt is located is determined firstly, and then the bolt looseness condition in the area where the bolt is located is detected. The efficiency and the detection precision are improved.

The invention relates to the technical field of flood prediction, in particular to a non-consistent regional flood frequency analysis method based on the Bayesian theory. The method comprises the following steps: acquiring flood sequence data, meteorological data and drainage basin characteristic data of hydrological stations in a drainage basin; performing regional flood dynamic change analysis on the flood sequence data and the meteorological data to generate regional flood time sequence dynamic change data; on the basis of the regional flood time sequence dynamic change data, hydrological station flood characteristic parameters in the drainage basin are extracted from the drainage basin characteristic data, and hydrological station flood characteristic parameters in the region are obtained; and performing flood distribution parameter regression coefficient prior information derivation on the flood characteristic parameters of the hydrological stations in the region to obtain a maximum likelihood estimation value of a single hydrological station distribution parameter. According to the method, the Bayesian theory, the spatial correlation analysis and the spectral analysis are introduced, so that the accuracy and the applicability of flood frequency analysis based on the Bayesian theory are improved.

The invention discloses an electric energy meter fault classification method and system based on confrontation comparison. The method comprises the following steps: adjusting the number of constructed target-neighbor sample combinations according to a weight calculation strategy to obtain a target-neighbor sample combination set; establishing a neural network, performing feature extraction on the constructed sample combination, comparing a target sample with a neighbor sample, determining a category matching degree, and obtaining a label prediction probability of the target sample for the first time; balancing the number of the minority class samples and the number of the majority class samples based on the generative adversarial network, determining a balanced data set, training a neural network binary classifier based on the balanced data set, performing label prediction on test set data, and obtaining a label prediction probability of a target sample for the second time through the neural network binary classifier; integrating the label prediction probabilities of the target sample obtained twice in proportion to obtain a label prediction result of the to-be-tested sample; and based on the label prediction result of the to-be-tested sample, classifying the fault samples of the intelligent electric meter.

The invention belongs to the technical field of latex glove quality detection, and particularly relates to a latex glove quality detection method based on deep learning. Comprising the steps of image acquisition, labeling, model training, quality detection and the like. A shooting device on a latex glove mold is adopted for image collection, images are labeled through an active learning technology based on class balanced sampling, and the type of quality problems is clarified. A deep learning model is constructed by using a convolutional neural network (CNN), a space attention mechanism is introduced, and the model is trained by using an annotated image to improve the accuracy and generalization ability. Finally, the trained model is used for automatically detecting the glove quality, a detection result is output, the gloves with quality problems are classified, and the production efficiency and the product quality are improved.

The invention discloses an acne grading method, system and equipment based on multi-modal fusion and a medium, belongs to facial acne severity grading in the technical field of artificial intelligence, and aims to solve the technical problem of poor facial acne severity grading effect in the prior art. A constructed facial acne grading model comprises a VGG feature extraction module, a multi-modal feature fusion module, an acne feature sensing module and a comprehensive grading module, the VGG feature extraction module extracts facial features in different modals, and the multi-modal feature fusion module models the relation between the facial features in different modals through cross attention for fusion. The acne feature sensing module enhances the sensing of acne features through a cross attention mechanism, and the comprehensive grading module makes a comprehensive and accurate acne severity grading result. According to the method, comprehensive grading of the severity of the acnes is realized by combining the multi-modal face pictures of the patient, and the grading effect of the severity of the facial acnes is greatly improved.

The invention provides a bar code detection method and system based on improved YOLOv8, and relates to the technical field of target detection.The method comprises the steps that a sample set with a plurality of bar code pictures is established, and each bar code picture is provided with a bar code position label and a bar code category label; a bar code detection model is constructed, and the bar code detection model comprises a backbone network module, a neck network module and a detection head module which are connected in sequence; establishing a joint loss function comprising a DFL loss function and a CIoU loss function; inputting the extended data set into a bar code detection model to train the bar code detection model until the joint loss function value is smaller than a preset joint loss function value; obtaining a to-be-detected bar code picture; and inputting a to-be-detected bar code picture into the trained bar code detection model, and outputting a predicted bar code position and a predicted bar code category. Detection resource consumption is reduced, and detection accuracy and detection speed are improved.

The invention discloses a flood prediction method based on deep learning. The method comprises the following steps: acquiring and processing geographic space data and rainfall data; inputting the obtained data into a flood numerical model to obtain a flood sample; preprocessing the obtained geographic space data, rainfall data and flood sample data obtained from the numerical model, and constructing a model training database; and taking the one-dimensional rainfall data and the two-dimensional geographic space data in the model training database as model input, and taking the flood sample as model output to train a flood prediction model. Before extreme rainfall occurs, the flood risk map of a research area can be calculated only by inputting geographic space data and rainfall sequence data of the research area into the model.

The invention is suitable for the technical field of advertisement pushing, and particularly relates to a digital media advertisement pushing method and system, and the method comprises the steps: dividing an internal region of a shopping mall into a plurality of sub-regions, each sub-region corresponding to a category, marking the deployment position of a Bluetooth gateway in each sub-region, and carrying out the pushing of a digital media advertisement through the Bluetooth gateways, reading a Bluetooth beacon signal of the visitor, extracting a unique identifier, acquiring consumption data of the visitor through pre-bound identity information, and determining an interest category of the visitor; acquiring video monitoring data of an entrance and an exit of the shopping mall, and identifying the number of visitors; and if the number is 1, passing the identity information. According to the method, the same item is pushed to all the visitors of the same bank, so that not only can personalized commodity recommendation and promotion information be pushed, but also attention and interaction of the visitors to the information can be increased, the attraction of the advertisement is improved, and thus the advertisement pushing accuracy and the participation degree of the visitors are improved.

The invention discloses an ultra-lightweight speech enhancement neural network system and method based on adaptive convolution. The system comprises an encoder; a frequency spectrum compression module, which is used for carrying out dynamic range and frequency dimension compression on a noisy speech frequency spectrum; the self-adaptive convolution attention module is used for carrying out frequency dimension downsampling and feature extraction analysis on the compressed feature map through a self-adaptive convolution layer and time channel attention, and then sending the feature map into an intensifier; the intensifier is a dual-path packet recurrent neural network and is used for modeling sub-band time features and intra-frame frequency features of the voice; the decoder comprises a self-adaptive transpose convolution attention module which is used for performing frequency dimension upsampling and feature reconstruction on the feature map output by the enhancer; and the frequency spectrum expansion module is used for expanding the frequency dimension of the feature map output by the self-adaptive transpose convolution attention module. According to the invention, high-performance speech enhancement can be realized with extremely low calculation complexity.

The invention relates to the technical field of remote sensing image processing, and discloses a remote sensing ground feature classification extraction network and drawing method in a complex scene, and the method comprises the following steps: S1, obtaining high-resolution satellite remote sensing image data, carrying out the preprocessing of an image, and generating a multi-scene ground feature data set; s2, constructing a two-layer U-shaped network comprising an encoder and a decoder, wherein the two-layer U-shaped network is used for multi-scale feature extraction and ground feature boundary segmentation; s3, inputting the preprocessed remote sensing image into a two-layer U-shaped network, and carrying out ground feature classification and segmentation; s4, performing splicing processing on the segmentation result, and generating a ground feature type spatial distribution map; and S5, performing noise elimination and post-processing operation on the generated distribution map. The multi-scale features are selectively extracted through the feature selection module (FSM), the attention of the network to small target ground features can be enhanced, and the segmentation precision of the small-scale ground features is remarkably improved by selecting the features containing important details.

The invention discloses an optical diffraction element defect detection method and system based on deep learning and a medium. The method comprises the following steps: acquiring a first to-be-detected image, preprocessing the first to-be-detected image to obtain a first ROI sub-image of the first to-be-detected image, and establishing a geometric rectangular coordinate system; a first deep learning model deployed in advance is loaded to perform semantic segmentation on the first ROI subgraph, the first deep learning model is used for identifying the first dispensing area and outputting a first feature graph, the first deep learning model is based on a Unit + + network structure, and a multi-scale large-kernel convolution module is adopted by both an encoder and a decoder of the first deep learning model; detecting a first defect in the first ROI sub-graph by adopting a first 2D traditional algorithm; the first feature map detects a second defect and a third defect by using a second 2D traditional algorithm. According to the method, a deep learning algorithm is combined with a 2D traditional algorithm, so that the detection speed and accuracy of the optical diffraction element can be improved.

The invention discloses a rotating machine fault diagnosis method based on a conditional decoupling representation enhanced network, and the method comprises the steps: capturing fault features from a known source domain and an unknown target domain for an acquired rotating machine vibration signal through a deep convolution feature extractor for a scene of rotating machine data category imbalance and cross-domain fault diagnosis; high-quality class semantic information is generated through a decoupling representation enhancement module to enhance minority class features in a high-dimensional feature space, so that inter-class data distribution is balanced, the influence of number difference of different classes of samples is reduced, then, the attention degree on minority classes is improved through condition discrimination comparison loss, and the accuracy of classification is improved. The intra-class feature compactness of the same class cross-domain is enhanced; and finally, a required unbalanced domain generalization mechanical fault diagnosis model is formed. According to the method, the influence of class imbalance can be overcome, the generalization ability and robustness of an unknown target domain can be improved, and the accuracy of rotating machine fault diagnosis is effectively improved.

The invention provides an MRI-TRUS image registration method and device based on weak supervised learning, and the method comprises the following steps: firstly, obtaining a to-be-registered MRI image and a to-be-registered TRUS image, inputting the to-be-registered MRI image and the to-be-registered TRUS image into a pre-trained segmentation sub-network, and obtaining a corresponding mask; and inputting the masks and the original image into a pre-trained registration sub-network to obtain a registration result. The registration sub-network comprises a transformation field and a registration unit, the transformation field unit generates a dense transformation field, the registration unit takes the MRI mask and the image as a moving object and takes the TRUS mask and the image as a fixed object, and the moving object is registered into the fixed object according to the transformation field. A to-be-registered MRI mask and a to-be-registered TRUS mask are obtained by segmenting a sub-network, and then accurate registration is carried out in a registration sub-network by taking the to-be-registered MRI mask and the to-be-registered TRUS mask as guidance.

The invention discloses a drought cause index screening method, and relates to the technical field of drought cause index screening, and the method comprises the steps: obtaining multi-scale drought data in time and space, and extracting the drought features of the multi-scale drought data; constructing a comprehensive drought index based on the drought characteristics; obtaining a data set based on the comprehensive drought index and the multi-scale drought data; analyzing the data set based on a drought evaluation model to obtain a loss value, inputting the loss value into an SHAP model to obtain an SHAP value, and screening drought cause indexes based on the SHAP value to obtain a screening result; the problems that in the process of obtaining the causal relationship between observation data and drought, an existing method neglects the influence of underlying surface factors on drought events, the relationship between agricultural and hydrological drought conditions and observation data cannot be mined, spatial features are neglected, and the spatial evolution features of the drought process are difficult to reflect can be solved.