862 results about "Attention model" patented technology

Disclosed is a complex scene deformation monitoring and classifying method based on interferometric synthetic aperture radar (InSAR) and a deep learning self-attention model, including: generating a deformation interferogram by using an InSAR extraction technology combining persistent scatterer (PS) points and distributed scatter (DS) points and relying on a shuttle radar topography mission digital elevation model (SRTM DEM) image; constructing a Delaunay triangulation network and an annular zone, obtaining a time series deformation data of PS points and DS points through adaptive arc densification and omni-directional point expansion, and extracting settlement points on a bridge; decomposing a time series by an SAR-self-attention model, decomposing InSAR time series data into a trend component and a seasonal component, and interpreting and analyzing deformation of the sea-crossing bridge; and describing time series dynamics and a seasonal pattern by comparing a curve fitting method with a seasonal and trend decomposition method.

The invention discloses a defect detection method and system based on joint distribution optimization and structural knowledge guidance. The method comprises the following steps: analyzing an equipment structured document to construct an equipment physical topological graph containing a physical connection relationship; processing the topological graph by adopting a graph convolutional network to generate a structured feature vector, and extracting a sensing feature vector of the multi-modal sensing data; constructing a knowledge-enhanced attention model, and guiding a perception feature vector to perform cross-modal fusion by using a structured feature vector as a key and a value; and training the model through a combined optimization target containing joint distribution divergence loss and Lipschitz stability constraint so as to ensure the consistency and physical authenticity of the multi-modal features in the shared semantic space. According to the method, the defect detection accuracy can be remarkably improved, and the root cause diagnosis report with physical interpretability is generated.

The invention discloses a multi-model fusion parallelization reservoir parameter prediction method, system, equipment and medium, and the prediction method comprises the following steps: standardizing logging curve data and actually measured reservoir parameter data, constructing a BiGRU-TCN-Transform model, determining an optimal parameter combination, and training to obtain a final model; inputting the training set data into the model for training until the model converges, and obtaining a trained prediction model; predicting the test set data, and outputting a final prediction result; secondly, an improved VAE-Attention model is constructed, the prediction result in the first step serves as input, and a final operation result of the model is output through model processing; the invention further comprises a multi-model fusion parallelization reservoir parameter prediction system, equipment and a medium. The precision is improved, and good stability, reliability and applicability are achieved.

The invention relates to an immersive VR psychological detection system and method based on multi-modal AI. The system comprises a data acquisition and processing module which is used for acquiring a multi-modal data set of a user in a virtual reality scene based on unified clock synchronization, performing time-space alignment and noise reduction standardization processing on the multi-modal data set, and extracting key biological characteristics. And the correlation model construction module performs space-time correlation mapping through a spatial transformation network, constructs a three-dimensional space attention model, and generates a real-time fluctuation curve after inputting the key biological characteristics into the trained model. And the state report generation module identifies a real-time fluctuation curve by using a time sequence analysis model, performs backtracking analysis in combination with the psychological state conversion node and a multi-modal cross validation result, and finally generates a three-dimensional interactive report. By adopting the method, multi-modal data fusion can be realized, the dynamic change of the psychological state of the user can be effectively captured, the psychological state of the user can be comprehensively and deeply analyzed, and a scientific basis is provided for psychological health assessment and intervention.

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 discloses a petroleum pipeline abnormity early warning and monitoring method and system based on a multi-source data fusion attention mechanism and a petroleum pipeline abnormity early warning technology, and aims to realize early-stage tiny leakage abnormity detection of a petroleum pipeline through a multi-source data fusion technology and a deep learning technology. According to the technical key points, historical data of pipeline operation states are collected from sound wave, temperature, negative pressure wave and vibration sensors, and a data set is constructed; obtaining a time sequence segment code for algorithm training; constructing a fusion attention module for algorithm training; on the basis of the obtained multi-source data, an attention Transform model is fused, and an adversarial learning method is adopted for training so as to predict the future operation state of the petroleum pipeline; training a petroleum pipeline early warning model by using Gaussian distribution with learnable scale parameters; the trained multi-source data is fused with an attention Transform model to serve as a petroleum pipeline abnormity early warning model, future operation state abnormal behaviors are recognized, and the failure probability is obtained. According to the method, the multi-source data analysis technology and the deep learning technology are fused, and the innovative Gaussian kernel scaling parameters and the failure probability calculation method are combined, so that the accuracy and reliability of early warning of the abnormity of the petroleum pipeline are effectively improved, and the method particularly has important application value in the aspect of micro leakage detection.

Provided is an efficient and scalable attention model that can be referred to as multi-axis attention. Example implementations can include two aspects: blocked local and dilated global attention. These design choices allow global-local spatial interactions on arbitrary input resolutions with only linear complexity. The present disclosure also presents a new architectural element by effectively blending the proposed multi-axis attention model with convolutions. In addition, the present disclosure proposes a simple hierarchical vision backbone, example implementations of which can be referred to as MaxViT, by simply repeating the basic building block over multiple stages. Notably, MaxViT is able to “see” globally throughout the entire network, even in earlier, high-resolution stages.

A convolutional layer of a computer model generates a dynamic convolutional filter based on the input feature map of the convolutional layer. The convolutional layer includes an attention model that generates a set of attention weights to dynamically adjust the convolutional filter applied by the model based on the input to the convolutional layer. The attention weights are generated with respect to multiple dimensions, which may include spatial position, input channel, output channel, and a respective combination of a set of static convolutional filters. The weights be generated with respect to each of the static convolutional filters, such that the different types (i.e., dimensions) of the weights may be applied element-wise to the respective convolutional filters and the filters, after application of the weights, may then be combined to generate the dynamic convolutional filter.

The invention discloses a question and answer generation method based on a knowledge graph, and relates to the technical field of artificial intelligence and natural language process.The method comprises the steps that a bidirectional semantic encoder is adopted to process input text corpus, entity words and relational words are extracted, a first semantic vector is formed, and the first semantic vector is converted into a basic knowledge structure through a triple decomposer; aligning the basic knowledge structure with deep semantic information in a preset knowledge graph by using a hierarchical knowledge fusion network to generate a multi-dimensional knowledge feature matrix, calculating association strength among knowledge nodes by using a dynamic weight distributor, and constructing a hierarchical question and answer path according to a sequence of the association strength from high to low; and inputting the hierarchical question and answer path into a constructed attention model, and generating a question and answer pair data set according to a question and answer template library. According to the method, the knowledge extraction accuracy and the knowledge fusion effect are improved, the question and answer path construction process is optimized, and a question and answer pair data set with a complete structure is formed.

The invention discloses a large-format remote sensing image semantic segmentation method for super-long context modeling. The method comprises the following steps: acquiring remote sensing data; constructing a basic network comprising a backbone network based on a visual self-attention model, a receptive field expansion layer, a feature aggregation layer, a global feature guidance network and a decoder based on hybrid experts; performing hierarchical pre-training on the backbone network and the global feature guidance network; performing end-to-end training and fine tuning on the basic network; and obtaining a final network model to realize large-format remote sensing image semantic segmentation. By means of the method, the visual context with the hundred million-level sequence length can be processed on a single device, and meanwhile it is guaranteed that calculation and memory overhead are within a reasonable range. Based on the backbone network architecture, an efficient expert hybrid computing strategy is adopted, and the decoder part of the model is further optimized, so that the decoder part can effectively cope with semantic segmentation tasks of different data environments.

The invention discloses a strip steel surface defect detection method based on a multi-branch parallel attention model, and belongs to the technical field of industrial defect target detection. On the basis of a YOLOv8 network, space depth conversion convolution is used for replacing all down-sampling convolution in a backbone network CSPDarkNet, a multi-branch parallel attention module is used for replacing a C2f module, and an internal feature interaction module is added behind the backbone network, so that the strip steel surface defect detection precision of the model is improved. The strip steel surface defect detection precision is effectively improved while model parameters are slightly increased, and compared with an existing strip steel surface defect model, the problem that the image detection effect is poor due to the fact that background interference is strong, various defects are large in difference, the number of small defects is large, and defect targets are likely to be confused in an industrial scene can be better solved.

The invention discloses a multi-language object illusion relieving method based on a cross-language attention mode, solves the problem of how to relieve multi-language object illusion during detection of a large visual language model under non-English questions, and belongs to the technical field of multi-mode questions and answers. The method comprises the following steps: identifying a cross-modal attention head set of which a visual language model shows obviously different behaviors for English and a target language when same semantic questions in different languages are processed; constructing image description queries of English and target languages for the same image, respectively inputting the image description queries into respective visual language models for reasoning, obtaining attention output under the English and target languages, and taking an average difference between the attention output as a language migration vector of the target languages; and in the reasoning process of the target language question, intervening the attention heads in the attention head set by using the language migration vector, so that the visual understanding ability of the visual language model under the non-English question is closer to the English question.

The invention provides an artifact restoration method for an ultrasonic image, a program product, electronic equipment and a storage medium, and the method comprises the steps: inputting a feature map corresponding to a to-be-restored ultrasonic image and a classification result into an artifact perception attention model, and obtaining an attention mask outputted by the artifact perception attention model, the feature map is obtained by performing feature extraction on the to-be-repaired ultrasonic image, and the classification result represents the type of artifacts in the to-be-repaired ultrasonic image; inputting the attention mask into a dynamic condition generator to obtain a depth feature output by the dynamic condition generator; the depth features are input into an ultrasonic image generator, a target ultrasonic image output by the ultrasonic image generator is obtained, and the target ultrasonic image is an image obtained after artifacts in the ultrasonic image to be repaired are repaired. According to the scheme, the artifact repairing algorithm fusing the dynamic condition generative adversarial network and the artifact perception attention model is provided, and the accuracy of repairing the artifacts in the ultrasonic image is improved.

The invention belongs to the technical field of electroencephalogram signal detection, and particularly discloses an epilepsy electroencephalogram signal detection system and method based on a diffusive attention model.The system comprises an acquisition unit, a signal processing unit, a signal processing unit and a signal processing unit, the reconstruction unit is configured to be used for inputting the preprocessed electroencephalogram signals into a distraction attention model for denoising and extracting features related to epileptic seizure at the same time; wherein the distraction attention model reconstructs a signal by using a diffusion model through step-by-step denoising, and introduces an adaptive gradient correction term to iteratively optimize the signal; in the reconstruction process of each stage, the extraction of epileptic seizure related features is enhanced through a self-adaptive attention mechanism; and the detection unit is configured to use a classifier to classify the epileptic seizure related features to obtain an epileptic seizure detection result. According to the method, the feature extraction capability of the high-dimensional noise electroencephalogram signals is improved, and the accuracy of epileptic seizure detection is remarkably improved.

The invention relates to a dexterous hand multi-mode motion trail prediction model generation method and a related device, which are used for a dexterous hand simulation grabbing process, and the method comprises the following steps: generating a motion trail of a dexterous hand according to initial position information of the dexterous hand and end position information of a target object; based on the plurality of motion track points, a multi-modal data set of motion of the dexterous hand in the motion track is obtained, and the multi-modal data set comprises environment image data, first multi-modal data and second multi-modal data; performing multi-modal feature fusion on the environment image data, the first multi-modal data and the second multi-modal data to obtain an input sequence; and based on a preset sparse self-attention mechanism, inputting the input sequence into a to-be-trained self-attention model for training to obtain a motion track prediction model. According to the scheme provided by the invention, the real-time performance and the accuracy of the motion trail prediction of the dexterous hand can be improved through the fusion of the multi-modal data, and the grabbing success rate of the dexterous hand is improved.

The invention discloses a security hierarchical federal learning method based on isolated forest anomaly detection, and relates to the technical field of electric digital data processing. The method comprises the following steps of: establishing a GRU self-attention model at an edge server side, extracting a time sequence characteristic state from global parameters through the GRU self-attention model, establishing a tree for the time sequence characteristic state of the updated parameters of the aggregated GRU self-attention model by using an isolated forest algorithm, and calculating the time sequence characteristic state of the updated parameters of the GRU self-attention model. And comparing the abnormal detection intensity evaluation coefficient with an abnormal detection intensity threshold after parameter aggregation of the GRU self-attention model, and taking measures of adaptively adjusting the abnormal detection intensity or giving an alarm according to a comparison result. According to the method, the personalized anomaly detection intensity threshold decision is generated for each server, the attack degree difference suffered by different servers is fully considered, the detection intensity is reasonably distributed, the effect of improving the security of the server is achieved, and the problem that the security of the server is insufficient in the prior art is solved.

The invention discloses a multi-level multi-attention target detection method based on a radar and a camera, and the method comprises the steps: firstly obtaining a radar point cloud and an image of a public data set, and carrying out the preprocessing; secondly, constructing an SC-DLA34 feature extraction network based on a DLA34 network to obtain an associated point cloud, and performing multi-level fusion on the associated point cloud and the image to obtain a preliminary fusion feature; and then splicing the associated point cloud features and the preliminary fusion features at a channel level, and inputting the spliced features into a PEA pyramid attention model to obtain deep fusion features. Secondly, the deep fusion features are sent to a 3D detection head to be decoded, the depth, speed, rotation and attribute information of a detection target is estimated, and a target 3D detection frame is generated; according to the invention, cross-modal fusion of radar information and image information is realized, the expression capability of point cloud is improved, and the detection capability of the model on small targets is ensured.

The invention provides an intelligent traceless erasing method and system for video picture characters, and relates to the technical field of video stream processing, and the method comprises the steps: carrying out the space-time dual-domain feature extraction of a preprocessed first video frame sequence, and obtaining a first space-time feature; and establishing a space-time double-domain attention model according to the first space-time feature, the space-time double-domain attention model calculating fusion space domain and time domain features through cross attention, dynamically distributing fusion weights based on the character movement speed, obtaining a second video frame sequence of the to-be-processed video stream, preprocessing the second video frame sequence, and extracting a second space-time feature. Texture and motion characteristics of front and back frames are synchronously referenced through a space-time double-domain attention model, so that the time sequence coherence of a repaired area is improved, a time sequence consistency compensation mechanism driven by an SSIM is combined, the flicker frequency is reduced, and the time-space domain weight is adaptively adjusted according to the character speed through a dynamic weight distribution strategy, so that the time sequence coherence of the repaired area is improved. And stable erasing and rapid moving of characters are realized.

The invention relates to a geological analysis technology, and discloses a multi-head attention model training method fused with geological rules, which is used for improving the scientificity and accuracy of geological body model prediction and improving the interpretability of a geological analysis process. According to the scheme, firstly, feature vectors of geological attribute data and geological coordinate data are spliced to obtain a fusion feature vector, then spatial locality and directivity are considered at the same time, geomorphic multi-head attention calculation is conducted on the fusion feature vector, and an attention feature sequence is obtained; then extracting multi-scale features from the input geological map, constructing a cross-fault weight mask matrix based on fault constraints, and obtaining comprehensive features through fusion; calculating a loss value by adopting a joint loss function containing prediction loss and fault non-penetrability constraint terms, and updating model parameters through a back propagation algorithm to complete model training; and finally, drawing an attention thermodynamic diagram for visual display, and superposing the attention thermodynamic diagram with the three-dimensional geologic model.

The present invention belongs to the field of computer vision technology, and provides a generalizable deep fake image detection method and system based on noise perception. The method comprises the following steps: obtaining a video containing a face identity to be detected; preprocessing the video data to be detected to obtain a facial image; inputting the facial image into a pre-trained deep fake image detection model to extract deep fake evidence features of the image; the deep fake image detection model mines the noise information in the image as fake evidence through an adaptive noise perception attention model; and uses the features as fake evidence to determine whether the facial image has been deeply faked. The present invention has lower testing costs, higher efficiency, better generalization, and can cope with a wide range of deep fake image detection scenarios.

The invention discloses a code generation method based on dynamic hierarchical sparse attention, and belongs to the technical field of data processing, and the method comprises the following steps: S1, collecting an input data set; s2, constructing a dynamic hierarchical sparse attention model; s3, optimizing the dynamic layered sparse attention model by using the input data set, and generating a final dynamic layered sparse attention model; and S4, generating a code by using the final dynamic hierarchical sparse attention model. According to the method, the model can still keep high-quality code generation capacity at extremely low labeling cost, and a flexible and efficient solution is provided for agile development and automatic testing.

The invention relates to a universal multi-level fusion attention model for urban coast SAR image ship detection, which comprises the following steps: establishing sub-modules DFEM, CMAFM and SMAFM: firstly designing the DFEM to extract rich features with different resolutions and semantic information for multi-scale ship detection; then designing CMAFM and SMAFM to fuse channel attention blocks and space attention blocks on different levels of feature maps so as to better learn dependency features from channel and space dimensions to enhance feature representation; and finally, inputting the fused feature map into an existing ship detection network to obtain a detection result. According to the method, more important part of information in an input image can be focused, the position information of the ship is enhanced, unimportant feature information is weakened, the model performance is improved through a small number of additional parameters, and the precision of identifying large and small ships under the complex urban coast background is improved.

The invention provides a crop early-stage remote sensing identification method fusing effective accumulated temperature, and provides a crop extraction method based on a space-time multi-level attention model and a long-short time memory neural network and integrating effective accumulated temperature and multi-source remote sensing data aiming at an early-stage crop growth environment and own characteristics. Complementary information between SAR and optical data is fully utilized, fusion is carried out in a feature layer, and further fusion with effective accumulated temperature data is carried out to realize enhancement of crop environment information, so that the model can better understand relevance between early-stage characteristics of different crops and the environment, and the early-stage recognition performance of the crops is improved.

The invention relates to a cultural scenic spot people flow prediction and scheduling method based on machine learning, and belongs to the field of scenic spot management. 2, constructing and training a mixed attention model, wherein the constructed mixed attention model comprises a spatial-temporal feature extraction module and a mixed attention mechanism module; step 3, people flow prediction and result output: inputting real-time data into the trained mixed attention model to carry out people flow prediction, and outputting a future time period passenger flow volume value; a quantile regression technology is combined to output a confidence interval of the passenger flow volume; step 4, scheduling strategy implementation: constructing a dynamic thermodynamic diagram based on a prediction result, and marking crowd density levels of different areas in the thermodynamic diagram; early warning information is pushed in real time, and touring suggestions are sent to tourists in advance.

The invention relates to the technical field of communication networks, and provides an image data transmission system and method based on multi-scale compression and time sequence analysis, and the method comprises the steps: S1, carrying out the preprocessing of transmitted video data through a transmitting end, and obtaining a preprocessed video frame; s2, performing feature extraction on the video frame by using a CNN (Convolutional Neural Network) and a hierarchical attention model based on Transform; s3, modeling information between the video frames through local motion compensation and global motion compensation to obtain multi-scale information, and processing the multi-scale information to obtain compressed video data; and S4, the compressed video data is sent to a video receiving party by establishing a long-short term memory network model and selecting opportunity, and the receiving party recovers the video data by using neural network parameters which are the same as those of a sending end during compression. According to the method disclosed by the invention, the efficient transmission of image data is realized by utilizing a scheme of transmitting a smaller data volume of a video and dynamically adapting to network change during transmission.

The invention relates to an automatic log anomaly detection method and system based on deep learning. The method comprises the following steps: extracting a representative log template sequence; encoding the log template sequence to generate a high-dimensional semantic vector for expressing the log template; a CNN-BiLSTM-Attention model is constructed, and the difference between the predicted value and the actual value of each data point is output based on the high-dimensional semantic vector; adopting a supervised learning method to train and optimize the model; reasoning to-be-tested log data through the trained model, and if the difference between a predicted value and an actual value exceeds a set threshold value, triggering an alarm. According to the method, the log template extraction capability based on the large language model, the semantic extraction capability of the Sension-BERT and the log sequence feature capture advantage of the CNN-BiLSTM-Attention model are fully played, and the accuracy and efficiency of log anomaly detection are remarkably improved.

The invention provides a patent agency whole-process intelligent management method based on artificial intelligence and big data, and the method comprises the steps: carrying out the real-time access of a global patent database, a legal status platform and a customer document through the collection of multi-source heterogeneous data and the construction of a dynamic patent knowledge graph, and storing technical feature entities, legal clause nodes and the like through a Neo4j graph database, calculating an edge weight in combination with the semantic vector to realize knowledge association; text feature dimension reduction and technical scheme similarity calculation are carried out through an improved TF-IDF algorithm and an LSTM-Attention model, and context dependency is effectively captured; and optimizing a reply strategy based on a deep reinforcement learning framework (DQN), defining an opinion feature vector and an action space, and dynamically adjusting a reply decision through a reward function (delta P is an authorization probability improvement amplitude, and T is time cost). The method is suitable for patent retrieval, technical scheme analysis, reply strategy generation and process automatic management.

A method estimating a depth of an environment includes generating, via a cross-attention model, a cross-attention cost volume based on a current image of the environment and a previous image of the environment in a sequence of images. The method also includes generating, via the cross-attention model, a depth estimate of the current image based on the cross-attention cost volume, the cross-attention model having been trained using a photometric loss associated with a single-frame depth estimation model. The method further includes controlling an action of the vehicle based on the depth estimate.

The invention discloses a cross-view geographic positioning method based on a spatial frequency domain attention model, and the method comprises the steps: constructing a spatial frequency domain attention model which employs a double-branch network structure, and one branch network structure comprises a backbone network, a spatial frequency domain attention module and a classifier which are connected in sequence; wherein the backbone network is used for extracting multi-scale semantic backbone features from unmanned aerial vehicle images or satellite images, and the spatial frequency domain attention module is used for extracting spatial structure features and spatial frequency domain cross-dimensional interaction features; according to the training sample, training the spatial frequency domain attention model through a mixed triple loss function and a cross entropy loss function to obtain a trained model; and performing identification matching on the to-be-detected image through the trained model to obtain another visual angle image at the same geographic position so as to realize effective cross-visual angle geographic positioning.

The invention discloses an airport ground guarantee work key time node automatic identification method and device, and relates to the technical field of image processing, and the method comprises the steps: inputting each airport scene image in an airport scene monitoring video into a trained target detection model, obtaining category information and position information of behavior subjects in each airport scene image; by using a target tracking model, according to category information and position information of key time node behavior subjects in all airport scene images, determining space-time motion information and confidence of each behavior subject in the airport scene monitoring video; and inputting the category information, the space-time motion information and the confidence coefficient of each behavior subject in the airport scene monitoring video into a trained dynamic graph attention model to obtain an identification result of the airport scene ground support work key time node. According to the invention, the accuracy and efficiency of important ground guarantee working time node identification in the airport scene image are improved.