1535 results about "Network processing" patented technology

The invention provides a method and system for analyzing defects in wafer manufacturing based on big data, and relates to the technical field of big data, and the method comprises the steps: collecting technological parameter information, defect scanning images and manufacturing process data information, and carrying out multi-scale feature pyramid network processing and fusion to obtain fusion features; the fusion features are analyzed through a deep learning network, the deep learning network adopts a multi-task learning framework to complete a defect classification task, a defect segmentation task and a defect evolution prediction task at the same time, consistency loss based on graph matching is introduced, and a defect type recognition result and a feature map of defect space distribution are obtained; based on the characteristic spectrum, when it is detected that the process parameters are abnormal, analyzing historical defect data by using a graph neural network containing a time sequence attention mechanism; technological parameter constraint conditions are set in combination with an expert knowledge base, and technological parameters are adaptively adjusted through a reinforcement learning method. According to the invention, the accuracy of defect detection and classification is improved.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling an agent interacting with an environment. In one aspect, a method comprises: receiving a user text sequence that characterizes a robotic task to be performed by the agent in the environment; converting the user text sequence into an input text sequence in a standardized format for a visual language understanding task; obtaining an observation image characterizing a state of the environment; processing (i) the input text sequence in the standardized format and (ii) the observation image using a policy neural network to generate a policy output that defines an action to be performed by the agent in response to the observation image; selecting an action to be performed by the agent using the policy output; and causing the agent to perform the selected action.

A system and method for adaptive image and video processing using a Persistent Cognitive Machine (PCM) architecture with visual strategy caching. The system receives degraded input media and extracts degradation fingerprints to query a PCM-based visual strategy cache containing previously successful processing strategies. When matching cached strategies are found above a relevance threshold, they are retrieved and applied directly. When no match exists, the input is processed through transform-domain networks to generate new strategies. A pattern synthesizer combines multiple strategies for complex degradation types. The system evaluates processing effectiveness using a feedback controller and stores successful strategies in the hierarchical cache. This cognitive approach enables real-time processing with continuously improving performance as the cache learns from successful patterns. The adaptive architecture eliminates redundant processing while maintaining high-quality output, making it suitable for diverse imaging and video applications requiring efficient enhancement capabilities with superior performance over traditional methods.

The invention provides an intelligent classroom teaching optimization method and system combining behavior recognition and the Internet of Things, and relates to the technical field of smart classs.The method comprises the steps that student behavior data, audio and video data and environment data are acquired through a distributed Internet of Things data acquisition system deployed at a student terminal; processing the data by adopting a multi-modal feature fusion network to obtain student interaction state features; and constructing a remote teaching interaction evaluation model based on the features, calculating the attention level and the classroom participation degree of the students in real time, and generating a personalized teaching guidance strategy. According to the method, intelligent sensing of student states and dynamic optimization of teaching strategies in the remote teaching process are realized.

The invention relates to the technical field of agricultural meteorological prediction, in particular to an agricultural meteorological disaster time sequence prediction system and method based on multi-modal data fusion, and the method comprises the steps: collecting and preprocessing agricultural meteorological disaster related data, constructing a dynamic semantic association graph, carrying out the multi-layer feature abstraction processing, and generating a semantic enhancement feature vector; the dual-branch prediction network processes time sequence dependence and local mode features, multi-granularity attention processing identifies key feature information, multi-scale feature fusion extracts different time scale feature information, and cascade fusion is carried out; the multi-target optimization module carries out model training based on the comprehensive feature representation and optimizes a plurality of targets; the multi-time scale prediction output module generates short-term accurate prediction, medium-term trend prediction and long-term risk assessment results, and provides prediction confidence, error range and risk level information; a dynamic semantic association graph and a multi-layer feature mapping mechanism are constructed, and deep fusion of multi-modal data on the semantic level is achieved.

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

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a time series data processing method, device and equipment and a medium. Performing a data enhancement operation on the original time series data to generate enhanced time series data; training a feature encoder based on the enhanced time sequence data in a comparative learning mode to obtain a pre-trained feature encoder; connecting the pre-training feature encoder with a sparse attention mechanism module to construct a time sequence modeling network; target time series data is processed using the time series modeling network to generate a processing result. According to the method, the enhanced view is constructed on the unlabeled data and the contrast learning training feature encoder is introduced, so that the time sequence representation with generalization ability is obtained, effective modeling of the long dependency relationship is realized in combination with a sparse attention mechanism, and the accuracy of time sequence modeling is improved under the condition of not depending on a large amount of labeled data.

The invention provides a marketing path dynamic generation method and device based on behavior preferences, equipment and a medium, and the method comprises the steps: obtaining user behavior data, and constructing a user behavior graph structure according to the user behavior data, the complex association comprising similarity between users, association between contacts and interaction weights of the users to the contacts; processing short-term behavior records of the user according to a Transform network, extracting sequence features, storing statistical features of long-term behaviors of the user according to a long-term memory network, and based on a user behavior graph structure, fusing the sequence features and the statistical features to obtain fused features; by constructing the user behavior preference graph and fusing the short-term and long-term behavior characteristics of the user, accurate modeling of the user behavior preference is realized. The graph structure not only captures the global structure of the user behavior, but also can dynamically reflect the current interest of the user; according to the method, the path candidate set is dynamically generated and optimized by adopting reinforcement learning and combining real-time feedback.

According to the multi-modal automatic knowledge graph construction method based on the large language model, a multi-modal data stream is preprocessed, features are extracted, and the multi-modal data stream is mapped to a unified semantic space through a cross-modal alignment network after being processed through the large language model, a visual converter and a time sequence neural network. In the space, entities and categories are recognized based on a large language model, a triple is generated by combining a multi-modal feature judgment entity relationship, mapping fusion is performed through an ontology alignment algorithm driven by a graph neural network and a predefined domain ontology, finally knowledge is stored in a graph database, and dynamic updating is performed by means of incremental learning and online reasoning. Standardized APIs and visualization components are provided. According to the method, the construction efficiency and the automation degree of the knowledge graph are remarkably improved, the cross-modal information fusion and knowledge maintenance capability is enhanced, and the application requirements of intelligent retrieval, recommendation, decision support and the like are met.

The invention discloses a highway pavement temperature forecasting method based on physical constraints, which comprises the following steps of: collecting and preprocessing meteorological data of all stations in a region, historical observation data of pavement temperature and geographic data, and adding time information to construct time features; building a U-net neural network model, carrying out space field data correction on the multi-station multi-meteorological element forecast data, and outputting corrected meteorological data of each station; constructing a bidirectional LSTM network on the basis of the U-net model, processing time sequence data, and evaluating and optimizing model performance by using a loss function; physical constraints including the time relation of temperature change, seasonal and day and night change characteristics, material thermal characteristics, a thermal balance equation and the like are added into the model, and a model result is optimized; and evaluating the generalization ability and forecasting precision of the model by using a test set, and finely adjusting model parameters. According to the method, the precision and reliability of road surface temperature prediction are remarkably improved, and a reliable basis is provided for traffic management and safety early warning.

The invention relates to the technical field of medical related information and communication, and discloses a hospital logistics one-stop service inspection and repair platform. The hospital logistics one-stop inspection and repair method comprises the following steps: processing equipment operation data through a multi-modal data fusion network, and generating a state feature vector set; constructing an equipment association graph model by using the space-time diagram attention network in combination with the topological information; detecting the abnormity of a single device and predicting a propagation path through a hierarchical time sequence abnormity detection network, and generating a minimum intervention strategy; an abnormal root cause analysis report is generated by means of a multi-stage interpretability engine, and decision making is supported; routing inspection routes and repair task allocation are optimized based on reports and strategies; according to the invention, integrated management of the whole process from abnormity prediction, inspection planning, fault diagnosis to maintenance dispatching can be realized, the hospital logistics pain point is solved, and the system has significant technical innovation and practical value.

The invention discloses a temperature monitoring and control system in a goat breeding house. The system comprises an environmental data sensing unit, an edge data preprocessing unit, an optimized long-short term memory neural network processing unit, an air internal and external circulation regulation and control strategy generation unit, an equipment linkage execution unit and a system operation state monitoring unit. Environmental data in a breeding house is collected in a self-adaptive mode through multiple types of high-precision sensors, after edge processing, temperature spatial-temporal characteristics are extracted deeply through an optimized long-short-term memory neural network, an air circulation regulation and control strategy is generated in combination with a multi-target optimization model, equipment is driven to execute precisely, and system dynamic optimization is achieved through closed-loop feedback. The system overcomes the defects of insufficient monitoring coverage and extensive regulation and control in the prior art, considers the coupling relationship between temperature and air components, realizes multi-factor collaborative accurate regulation and control, effectively reduces energy consumption, creates a stable and comfortable growth environment for goats, and improves the breeding benefit and intelligent management level.

The invention discloses an electric power scene defect small target detection method based on Gaussian mask supervision and cross-layer attention guidance, and the method comprises the steps: inputting an electric power scene image into a detection model, extracting an initial feature map through a backbone network, carrying out the multi-stage feature extraction of the initial feature map according to a convolution path, and carrying out the multi-stage feature extraction of the initial feature map; processing the multi-stage features based on a path aggregation network, and outputting a plurality of fusion feature maps with different feature levels from shallow to deep; and based on cross-scale window attention, guiding a shallow fusion feature map to carry out semantic information modeling by using a deep fusion feature map with high semantics in every two adjacent fusion feature maps, and after a plurality of output feature maps are obtained, respectively processing and outputting prediction results by using a multi-branch detection head. According to the method, shallow feature activation prediction and cross-scale window attention guidance are fused, and the detection robustness and positioning precision of a tiny fault target in an unmanned aerial vehicle inspection image can be effectively improved.

A method may include partitioning a tensor that includes a plurality of data values into a number (K) of subtensors, where each of the K subtensors may include a respective subset of the plurality of data values. The method may also include retrieving one or more first data values of the subset of data values included in a first subtensor of the K subtensors in accordance with an access pattern associated with a neural network processor. The method may include storing the one or more first data values of the subset of data values in one of K segments of cache memory, where each of the K segments may be associated with a respective one of the K subtensors. Further, the method may include processing, using the neural network processor, the one or more first data values of the subset of data values in accordance with the access pattern.

The invention discloses a three-dimensional shielding target tracking method based on multi-modal space-time interaction, and relates to the technical field of target tracking. The method comprises the following steps: acquiring a point cloud and an image and preprocessing to obtain global fusion features; obtaining an initial detection frame and region-of-interest features through region proposal network processing; projecting the non-empty voxel point cloud to the image features, and reconstructing shielded target features; convolution and neural network processing are utilized to obtain a refined detection frame; screening legal detection frames through distance calculation and legality judgment; the bipartite graph and the self-adaptive channel graph are adopted for convolution, and appearance correlation scores are calculated; and matching the detection frame and the trajectory based on a Hungary algorithm to realize whole-course tracking. The target identification accuracy and robustness are improved, the shielding problem is solved, the accuracy of the detection frame is ensured, the correlation accuracy is improved by using the bipartite graph and the adaptive convolution, the nodes are matched in combination with the geometric cost matrix, and whole-course tracking and error calibration are realized.

The invention provides a gear geometric parameter rapid detection method and system based on wireless transmission, and relates to the technical field of gear detection, and the method comprises the steps: for a to-be-detected gear, extracting gear features based on a deep learning network, and determining a position combination of optimal measurement points in combination with a gear precision grade requirement; collecting geometric parameter data of the gear based on the position combination; and processing the geometric parameter data by adopting a cascade neural network to obtain actual geometric parameters, sending the actual geometric parameters to a remote monitoring terminal through a wireless transmission module with a self-adaptive channel selection function, and generating a detection result report.

The invention discloses a compressed sensing MRI (Magnetic Resonance Imaging) reconstruction method and system based on a double-domain fusion expansion model. The method comprises the following steps: acquiring an MRI image, preprocessing the MRI image to obtain a preprocessed image and a corresponding compressed sensing measurement value, and constructing a data set; training, testing and verifying the double-domain fusion expansion model by using the data set; and outputting a reconstructed image from the MRI image after mask sampling by using a double-domain fusion expansion model. According to the double-domain fusion expansion model, a complex convolutional neural network and a K-space attention mechanism are introduced for complex value data processing and double-domain information fusion in compressed sensing MRI reconstruction. The complex value data is processed through the complex network, the amplitude and phase information of the complex field is fully utilized, the detail recovery capability and the noise suppression effect of the model are enhanced, and particularly, excellent robustness is shown at a low sampling rate. The method is excellent in performance in complex MRI data reconstruction, and has high reconstruction precision and generalization ability.

The invention belongs to the field of agricultural automation and intelligent equipment, and discloses a portable measuring method and device for pork intramuscular fat content. By screening the characteristic wave bands related to the fat content and selecting the corresponding narrow-band LEDs, the accuracy and effectiveness of the multispectral image are improved. And meanwhile, through multi-modal image fusion analysis, in combination with the RGB image, the black-and-white image and the multi-spectral image, the precision of target segmentation and content prediction is improved. And establishing an end-to-end content prediction model by adopting a mode of combining a CNN and an MLP network. Meanwhile, the FCNN network is used for processing the spectral data, the tedious spectral data preprocessing process is omitted, the advantages of the spectral data and the RGB image are fully played, and a new thought and method are provided for the field of spectral modeling. The technical scheme has the advantages of portability, high efficiency, accuracy and the like, provides powerful support for pork nutrition evaluation, and can provide reference for determination of the fat content of other livestock and poultry meat.

The invention discloses a traffic flow prediction method and system based on feature fusion and frequency enhancement. The method comprises the following steps: acquiring traffic flow data of a traffic monitor and external meteorological data and carrying out preprocessing; a dynamic space-time diagram generation module constructs a dynamic space-time diagram fusing a geographic neighbor diagram and a function similar diagram based on the preprocessed traffic flow data; inputting the dynamic space-time diagram into a space-time attention convolution layer to obtain a space-time matrix; fusing the space-time matrix with the external meteorological data subjected to one or more times of long-short-term memory network processing, and executing near-time slicing processing on the fused data; and constructing a hierarchical encoder-decoder model based on Transform, and generating traffic flow prediction data after the data subjected to slicing processing passes through the model. According to the method, the traffic flow prediction precision can be improved, and a more reliable prediction result is provided.

The invention discloses a chlorophyll calculation method based on a double-branch multi-scale fusion network model, which belongs to the technical field of chlorophyll calculation, is used for calculating a chlorophyll a concentration value, and comprises the following steps: taking the chlorophyll a concentration value of a measurement site as a label of a water body characteristic sequence data set, dividing the data set into a training set and a verification set, inputting the training set into the double-branch multi-scale fusion network model to obtain a trained double-branch multi-scale fusion network model; inputting the verification set into the double-branch multi-scale fusion network model to obtain a verified double-branch multi-scale fusion network model; and taking the neighborhood spectral feature tensor and the neighborhood inherent optical feature tensor as the input of the verified double-branch multi-scale fusion network model to obtain the chlorophyll a concentration value of the pixel at the corresponding position. According to the method, the calculation result of the chlorophyll a with relatively high precision is obtained through neural network processing, and the complex relationship among the concentration change of the chlorophyll a in the water body, the spectrum and other optical parameters can be captured.

The invention belongs to the technical field of medical image processing, and relates to a medical image registration method based on multi-scale deformation decomposition. Firstly, a multi-scale feature extraction network is constructed, the method focuses on extracting features of a mobile medical image, and the features are transmitted to a hierarchical feature fusion module to be processed; secondly, the hierarchical feature fusion network is used for establishing a mapping relation between the mobile medical image and the fixed medical image; in a feature decoding stage, a mobile decoding feature network and a fusion decoding feature network are respectively constructed, multi-scale decoding features are utilized to output a multi-scale deformation field, and a coarse-to-fine registration strategy is gradually realized by fusing the deformation fields of adjacent stages layer by layer. By designing the double-branch network structure and connecting the gating updating modules in the adjacent stages, the complex deformation processing capability of the registration network is enhanced, so that the final registration effect is improved.

The invention provides a falling detection method and system based on millimeter wave radar fusion human body postures, and relates to the technical field of falling detection, and the method comprises the steps: collecting human body echo signals, and constructing three-dimensional point cloud data through distance, angle and speed estimation; the point cloud is processed to generate a dynamic sequence, and skeleton features and key point coordinates are extracted in combination with a graph convolutional network. A skeleton connection relation is constructed based on the key points, attitude features are calculated, key change features are extracted through a self-attention mechanism, and the key change features are fused with a point cloud sequence to construct multi-modal features. And the double-branch state recognition network processes the fusion features, and when abnormality is detected, further analysis is carried out through residual attention and space-time diagram convolution, and finally, the falling state is recognized and the risk level is evaluated.

The invention provides an intelligent bee colony type quality optimization management method and system for multi-source tag data, and relates to the technical field of data tags, and the method comprises the steps: extracting a comprehensive feature vector of a tag through a feature extraction module, carrying out the processing through a deep feature enhancement network, generating a similarity and dependency feature matrix, and constructing a tag quality evaluation model; mapping the label data into bee colony network nodes, performing three-level division, and constructing a node optimization association network; and performing optimization based on the collaborative optimization rule set until the quality improvement value is smaller than a preset threshold value. The label data quality can be effectively improved, the data processing cost is reduced, and the optimization efficiency is improved.

The invention relates to the field of computer storage, and discloses a solid state disk data prefetching method based on an attention mechanism, which comprises the following steps: S1, collecting access track data of a solid state disk, and performing fixed time window segmentation; s2, related labels are generated according to the content, and the labels are converted into numeric vectors; s3, combining the tag vector with a traditional access mode feature to form a multi-mode feature vector; s4, processing the multi-modal feature vector by using a gating cycle unit network, evaluating the importance of data of different time windows through an attention layer, and calculating a weight; s5, predicting data blocks which may be frequently accessed in the future based on the attention weight and the gated loop unit network output; and S6, according to a prediction result, selecting a prediction result with high confidence coefficient for prefetching. By using the solid state disk data prefetching method based on the attention mechanism, which data blocks may be frequently accessed in the future can be more accurately identified and predicted.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting errors in a computation performed by a quantum computer. In one aspect, a method comprises obtaining error correction data for each of a plurality of time steps during the computation; initializing a decoder state; and for each of a plurality of updating time steps, wherein each updating time step corresponds to one or more of the time steps: generating an intermediate representation; and processing a time step input through a Transformer neural network to update the decoder state for the updating time step. The method comprises generating a prediction of whether an error occurred in the computation from the decoder state for the last updating time step of the plurality of updating time steps.

The invention provides a method and a system for detecting surface defects of a flange sealing element. The method comprises the following steps: expanding an annular surface image into a rectangular image; constructing a defect segmentation network to process the rectangular image to obtain a preliminary defect mask; the defect segmentation network adopts an encoder-decoder structure, a parallel multi-scale feature extraction module is arranged at the tail end of an encoder, and the parallel multi-scale feature extraction module comprises a plurality of parallel convolution branches with different receptive fields; training the defect segmentation network by using a composite loss function, and performing post-processing on the initial defect mask by using a full-connection conditional random field to obtain an optimized defect mask; and inversely transforming the optimized defect mask to a Cartesian coordinate system, and identifying and marking the position and contour of the defect on the original annular surface image.

The invention relates to an oncogene prediction method based on graph variation self-coding, and belongs to the field of bioinformatics. The method is based on a dual-path neural network framework: a main path processes an original network and features enhanced by a variational auto-encoder (VAE) by using a graph attention network (GAT) so as to capture a complex relationship between nodes; the auxiliary path generates an auxiliary network and features containing global information through an APPNP algorithm, and the auxiliary network and features are aggregated through GraphSAGE to retain structural information. The model introduces jump connection and residual connection to relieve gradient disappearance and enhance feature complementarity. And finally, integrating dual-path information output prediction through a linear layer. The method is verified on a plurality of biological network data sets, the prediction accuracy, robustness and hidden relation recognition capability are remarkably improved, and a reliable tool is provided for cancer research.

The invention relates to the technical field of medical image intelligent analysis and artificial intelligence auxiliary diagnosis, in particular to a medical image disease course prediction system based on an industrial neural network, and the system comprises a reference generation module which is used for obtaining static image data; processing the static image data by using a physical perception neural network to generate a pure ideal state reference; a perturbation simulation module; the industrial kinetic parameters are used as perturbation terms to be superposed to a pure ideal state reference, and a theoretical damaged state is generated; the projection verification module is used for acquiring real multi-modal observation data; generating a real residual error; generating a theoretical residual error based on the theoretical damaged state and the pure ideal state reference; calculating a manifold coupling confidence coefficient; the closed-loop correction module is used for performing inversion optimization on the industrial kinetic parameters; outputting a disease course prediction result according to the manifold coupling confidence coefficient; according to the method, the problem that a traditional medical model lacks physical consistency explanation is solved, and the credibility of artificial intelligence auxiliary diagnosis is remarkably improved.

A neural network processing device, an electronic apparatus, and an operation method The neural network processing device comprises a storage module, an input data processing module, a plurality of matrix compute-in-memory units, an output data processing module, and a control module, wherein the storage module comprises: a first storage sub-module configured to store an input feature map for neural network processing, and a second storage sub-module configured to store an intermediate feature map for neural network processing; and the plurality of matrix compute-in-memory units are configured to store the weight of at least one network layer of a neural network and use the weight to perform matrix-vector multiplication computation of the neural network. The neural network processing device can be applied to a compute-in-memory system for image processing, thereby lowering the computation delay, and reducing the area and power consumption.

The invention provides a scene semantic occupancy prediction method based on probabilistic three-dimensional Gaussian, and the method comprises the steps: processing a multi-view camera image through employing a two-dimensional backbone network, generating multi-view and multi-scale image features, predicting the three-dimensional occupancy distribution aligned with pixels through the image features, and initializing the position attribute of the three-dimensional Gaussian according to the distribution; the method comprises the following steps: taking three-dimensional gauss as point cloud data, generating sparse voxel data through voxelization, and processing the sparse voxel data by using three-dimensional sparse convolution to complete feature interaction between the three-dimensional gauss; by using a cross attention mechanism, updating three-dimensional Gaussian features by image features; performing layer-by-layer iterative optimization on each attribute in the three-dimensional Gaussian features by using a Gaussian attribute fine tuning model; and aggregating geometric attributes and semantic attributes of the three-dimensional Gaussian by using a probability Gaussian superposition method, and generating a three-dimensional semantic occupation prediction result of the scene. According to the invention, the problem of efficient adaptive three-dimensional semantic occupancy prediction of an automatic driving scene can be solved.