1822 results about "Data reconstruction" patented technology

The embodiment of the invention discloses a data distributed storage method and device, and the method comprises the steps: carrying out the fragmentation processing of to-be-stored data, and obtaining an initial fragmentation result; utilizing a reinforcement learning model to perform popularity prediction on the data fragments according to the historical access data to obtain the access popularity of the data fragments; determining the number of copies based on the access popularity and importance of the data fragments, generating fragment copies of the data fragments according to the number of the copies, and storing the data fragments and the fragment copies into storage nodes according to the access popularity and node load state scores; when the loads of the storage nodes are unbalanced, data migration is carried out on the data fragments stored in the storage nodes, and when the node health state of a certain storage node is abnormal or the stored data fragments are lost, data reconstruction is carried out on the data fragments stored in the storage node. According to the method, the problems of non-uniform access load, resource waste, data loss and the like in a distributed storage system can be relieved.

A generative adversarial multi-headed attention neural network self-learning method for aero-engine data reconstruction belongs to the field of end-to-end self-learning of aero-engine missing data. First, the samples are pre-processed, and the machine learning algorithm is used to pre-fill the normalized data first, and the pre-filled information is involved in the network training as part of the training information. Second, a generative adversarial multi-headed attention network model is constructed and the trained sample set is used to train the generative adversarial multi-headed attention network model. Finally, the samples are generated using the trained sample generator G. The method uses the generative adversarial network to better learn the distribution information of the data, and uses parallel convolution and multi-headed attention mechanism to fully exploit the spatial and temporal information among the aero-engine data.

The invention relates to the technical field of data verification, and discloses an integrity verification and correction method and system for flight data and a storage medium. The method comprises the following steps: performing spatial-temporal feature extraction processing on a multi-modal feature data stream, and constructing an incidence matrix according to a multi-level data mapping relationship to obtain a spatial-temporal feature association network; performing hierarchical inspection processing on the spatial-temporal feature association network to obtain abnormal mark data; performing regional recursion positioning processing on the abnormal mark data to obtain an abnormal propagation link diagram; performing correction scheme design processing on the abnormal propagation link diagram, and obtaining a data correction execution sequence based on a data reconstruction and influence evaluation optimization scheme; and performing progressive processing on the data correction execution sequence to obtain corrected flight data. According to the method, a data integrity checking and correcting mechanism is established in a distributed data acquisition environment, so that data exception can be accurately identified and positioned, and the corrected data can be ensured to meet physical constraint and space-time consistency requirements.

The invention relates to the technical field of cable cover plate detection, and discloses a cable cover plate anomaly detection method and system based on a variational auto-encoder, and the method comprises the steps: carrying out the denoising processing of the original data of a cable cover plate collected by a sensor, obtaining a denoised signal, carrying out the dynamic resampling of the denoised signal according to a local change rate, and carrying out the detection of the anomaly of the cable cover plate. Resampling data is obtained; inputting the resampling data into a pre-established data reconstruction model to obtain reconstructed data, the data reconstruction model being constructed based on a variational auto-encoder and being obtained by training based on a dynamic learning rate adjustment strategy and a data enhancement strategy; and performing anomaly detection according to the reconstruction error change rate of the reconstruction data to obtain abnormal data, and generating a corresponding alarm prompt according to the abnormal data. According to the method, the integrity and reliability of data can be improved, the flexibility and accuracy of anomaly detection are improved, the change of the operation state of the cable cover plate can be responded in time, and the real-time performance and effectiveness of anomaly recognition are improved.

The invention discloses an energy-saving control method and system for a water chilling unit, and relates to the technical field of energy-saving control. During operation of the system, a data set is synchronously collected from a water chilling unit system through a multi-channel asynchronous sampling mechanism, time sequence compression and redundancy removal are carried out, an embedded perturbation calculation mechanism is used for calculating and generating a non-dominant control core coefficient, and the non-dominant control core coefficient is used for controlling the energy-saving control of the water chilling unit; a three-dimensional coefficient space is converted and output through an entropy state change structure and is used for constructing a state balance atlas, comprehensively calculating a control state index SEEI, evaluating the current energy state offset degree of a system, determining whether intervention is carried out or not, carrying out rule search and nonlinear modeling based on the control state index SEEI value, generating an adjustment matrix, and carrying out state balance analysis. And starting a data reconstruction micro-strategy of a short-time historical window, receiving an adjustment matrix, converting the adjustment matrix into a device-level instruction, executing an action through an edge controller, feeding back a response error epsilon (t) in real time, and predicting a potential performance degradation trend based on long-time system operation data.

A method and apparatus for reconstructing a three-dimensional (3D) scene from a two-dimensional (2D) input image of the scene using a fully-differentiable transformer-based encoder-decode. A 2D input image encoded into a set of image features using a pre-trained vision transformer model, wherein the vision transformer model is pre-trained with multi-view RGB image supervision and point cloud supervision. The set of image features is projected onto a 3D triplane representation using a transformer decoder to obtain output triplane tokens. A triplane representation is created from the tokens and queried. 3D point features of color and density for volumetric rendering re predicted using a multi-layer perceptron. The geometry of the generated 3D asset is represented with a surface mesh including vertices and triangular faces. A texture map by is created with a multichannel image in UV space. Multiple views of the 3D scene are simultaneously generated based on the surface mesh.

The invention provides an LED packaging material defect detection method, which comprises the following steps: performing image enhancement processing on each layer of separated image to obtain each layer of clear material image; carrying out local fine scanning on the identified cross defect area to obtain micron-sized three-dimensional structure data, and reconstructing fine morphology characteristics of the cross defect; according to the reconstructed cross flaw structure, combining various material performance parameters pre-stored in a material database to simulate and calculate the influence of flaws on the LED performance, including optical, thermal and mechanical properties; and judging the position, the size, the type and the influence degree on the performance of the flaws, and carrying out quantitative scoring on the overall quality of LED packaging to obtain a final detection result and quality grade judgment.

The invention relates to the technical field of processing technology evaluation, and discloses a tagatose milk tea processing technology evaluation method and system, and the method comprises the steps: carrying out the collection and preprocessing of multi-dimensional parameter data in the production process of tagatose milk tea, and obtaining a multi-dimensional time series data set; a TSVT-PNNT model is input for data reconstruction, and reconstructed data is obtained; performing multi-feature complementary fusion to obtain full-band fusion features; performing process parameter constraint analysis to obtain a process parameter constraint range; generating a plurality of candidate process parameter adjustment schemes by using a Bayesian network and a reinforcement learning method; and in various production scenes, verifying and evaluating the plurality of candidate process parameter adjustment schemes, selecting the optimal candidate process parameter adjustment scheme as a target process parameter adjustment scheme, and outputting the target process parameter adjustment scheme. According to the invention, an optimized process parameter adjustment scheme can be adaptively generated according to different production scenes, so that the production efficiency and the product quality stability of the tagatose milk tea are improved.

A computer-implemented method for decoding a coded mesh bitstream of a dynamic mesh representing three-dimensional content includes that: geometry information of the dynamic mesh is reconstructed from a geometry component bitstream in the coded mesh bitstream, the reconstructed geometry information includes data specifying vertices of the dynamic mesh; connectivity information of the dynamic mesh is reconstructed from a connectivity component bitstream in the coded mesh bitstream, the reconstructed connectivity information includes data specifying faces of the dynamic mesh; the reconstructed connectivity information is refined based on the reconstructed geometry information to generate refined connectivity information by at least dividing a face specified by the reconstructed connectivity information into two faces based on a vertex specified in the reconstructed geometry information; the dynamic mesh is reconstructed based on the reconstructed geometry information and the refined connectivity information; and the reconstructed dynamic mesh is caused to be rendered for display.

The invention discloses a composite board ultrasonic defect detection method which comprises the following steps: S1, fixing a carbon fiber circumferential winding composite pressure container on a bracket, and establishing a coordinate system with the axis of the container as a z axis; s2, a 40 MHz dry coupling phased array ultrasonic probe is attached to the scanning starting point, and the contact angle of the probe is recorded; s3, moving the probe along the spiral track of the outer surface of the container at the speed of 20mm / s, and collecting A-scanning echo signals of all array elements; s4, performing pulse compression and time domain deconvolution processing on the acquired signal to obtain a time domain echo sequence; s5, delay time is calculated according to the shell curvature, the array element signals are compensated, and focusing B-scanning data are generated; s6, splicing the B-scanning data at the step length of 0.5 mm * 0.5 mm, and reconstructing C-scanning data corresponding to the space coordinates; and S7, inputting the C-scanning data into the trained Transform network, and outputting defect types, sizes and three-dimensional coordinates. The method realizes high-frequency ultrasonic composite board defect accurate detection, remarkably improves the microcrack recognition rate, and is widely applied to safety evaluation scenes of high-pressure hydrogen storage tanks and pressure vessels.

The invention discloses a data reconstruction method and device, a storage medium and a program product, and relates to the technical field of data storage, and the method comprises the following steps: reconstructing an initial redundant array of independent disks according to a hard disk state of the initial redundant array of independent disks to obtain a target redundant array of independent disks; after the reconstruction of the initial redundant array of independent disks is completed, establishing a mapping relation between the initial redundant array of independent disks and the target redundant array of independent disks, and storing the mapping relation based on metadata to obtain corresponding target metadata, and recovering the target redundant array of independent disks to the initial redundant array of independent disks according to the target metadata. When the disk fails or restores to normal for data reconstruction, the corresponding data can be directly written back according to the mapping condition between the blocks, additional check block calculation is not needed, and the data recovery speed is increased.

The invention relates to a method for improving satellite data inversion soil salinity by using hyperspectral data of an unmanned aerial vehicle. The method comprises the following steps: collecting a soil sample in a test area, and calculating soil salt content; acquiring and preprocessing satellite remote sensing image data of a test area; storing data, outputting a single-band satellite image, and calculating a spectral index; obtaining and preprocessing hyperspectral image data of the unmanned aerial vehicle in the test area; reconstructing and upscaling unmanned aerial vehicle image data pixels to a spatial resolution of 10m; reconstructing the narrow-band spectral data of the unmanned aerial vehicle into broadband spectral data consistent with a satellite image, storing and outputting a single band, and calculating a spectral index; screening a soil salt content characteristic variable, and constructing a soil salt content inversion model by taking the screened characteristic variable as an independent variable and the actually measured soil salt content as a dependent variable; and fitting a function relationship between the upscaling unmanned aerial vehicle soil salt content inversion model and the satellite inversion model by adopting a numerical regression function method to realize correction of the satellite inversion model.

The invention relates to the technical field of smart cities, in particular to an urban climate risk monitoring system based on multi-source data fusion and early warning application thereof, and the urban climate risk monitoring system comprises a data reconstruction module, a precursor identification module, a trend chain extraction module, a grade assignment module and a risk diffusion module. According to the method, the reliability of multi-source meteorological data is effectively enhanced, the precision of data fusion and the reliability of spatial positioning are improved by carrying out credibility level evaluation on a plurality of data sources, and the sensitivity of risk precursor events and the accuracy of factor propagation prediction are enhanced by utilizing deep identification of trend consistency and response delay relation; according to the method, the regional spatial continuity factors are combined, the stability and spatial continuity of risk grade division are optimized, spatial impedance characteristics are brought into diffusion path prediction, accurate deduction of urban climate risk spatial diffusion paths and dynamic marking of risk regions are realized, and the accuracy and dynamic of urban climate risk monitoring and prevention and control decisions are enhanced.

The invention provides an avalanche monitoring and early warning method based on deep learning, and the method comprises the steps: obtaining environment monitoring data in a period of time before a current moment, carrying out the preprocessing, and constructing a training data set; an avalanche monitoring and early warning model is constructed and trained; inputting forecast data of environmental monitoring into the trained avalanche monitoring early warning model for data reconstruction to obtain corresponding reconstruction data; calculating a data reconstruction error; and determining that the data is abnormal according to the error and a preset threshold value, and completing the avalanche monitoring and early warning. The problem that an anomaly detection model is difficult to capture key features under high-dimensional and dependency complex data is solved, the precision and robustness of the anomaly detection model are improved, and the method is particularly suitable for processing multivariate time series data such as avalanche monitoring data, capturing data anomalies in the avalanche monitoring data and achieving avalanche monitoring and early warning.

The invention relates to the technical field of wire harness fault diagnosis, and discloses a vehicle-mounted wire harness fault diagnosis method and device and a storage medium. The method comprises the following steps: carrying out equivalent circuit modeling on a measurement node and a wire harness section of the vehicle-mounted wire harness to obtain a node connection relation matrix; performing feature analysis on the voltage data and the current data of the measurement nodes to obtain a fault feature vector and a fault type discrimination rule base; inputting the voltage data, the current data and the node connection relation matrix into a recursive variational graph automatic encoder for data reconstruction to obtain time sequence feature reconstruction data and topological relation reconstruction data; carrying out distributed sub-model division and model fusion to obtain a standardized feature vector; and performing matching analysis on the standardized feature vector and the fault type judgment rule base to obtain fault position information and fault type information, so that automatic optimization and screening of fault features are realized, and the reliability and interpretability of a diagnosis result are improved.

The invention relates to the technical field of bridge long-term health monitoring, and discloses a mass fatigue damage monitoring data abnormity identification method based on time series data reconstruction and an adaptive threshold, and the method comprises the steps: obtaining normal data from original bridge strain data of a monitoring target, and training a time series data reconstruction model based on a deep learning network; performing high-precision reconstruction on possible abnormal data in the original bridge strain data of the monitoring target by using a time sequence data reconstruction model to obtain a transient energy response decision coefficient of data of each time period in a plurality of time periods on a frequency domain and a strain amplitude response decision coefficient of data of each time period on a time domain; and then clustering the mahalanobis distances between the data, and dynamically and accurately identifying real abnormal data in the possible abnormal data of the monitoring target by combining a time domain-frequency domain two-dimensional adaptive abnormal threshold. According to the method, the abnormal recognition of the mass fatigue damage monitoring data can be efficiently and precisely realized, and the generalization ability is relatively high.

The invention provides a hybrid fault-tolerant control method and system for a multi-sensor simultaneous fault scene, and the method comprises the steps: inputting the historical time sequence feature data of sensors into a long short-term memory network and a Stacking model, and obtaining the output data prediction values, outputted by the long short-term memory network and the Stacking model, of the sensors at the current moment; according to a difference value between an output data prediction value and an output data actual value of the sensor at the current moment output by the long-short-term memory network, whether the sensor breaks down or not is determined through sequential probability ratio inspection; and under the condition that the sensor has a fault, determining an output data reconstruction value of the sensor at the current moment according to an output data prediction value of the sensor at the current moment, which is output by the long-short-term memory network and the Stacking model. According to the invention, fault detection and output data reconstruction can be carried out on the sensor in real time, and the stability and reliability of the system under the condition of multi-point observation failure are improved.

The invention discloses a river flow missing data reconstruction method, which comprises the following steps of S1, constructing a river network topological structure, and quantifying hydraulic correlation; s2, spatio-temporal feature fusion and multi-source information extraction; s3, performing multi-task cooperative flow reconstruction and confidence coefficient prediction; s4, dynamic weighting and result correction of meteorological factors; and S5, performing anomaly detection and model dynamic updating. According to the method, a river flow missing data reconstruction method is set, the steps are mutually fused and used, and topology-aware space-time diagram convolution is performed: a river network topology structure is encoded into a weighted adjacency matrix for the first time, space correlation features are extracted through a diagram convolution network, and the problem that a traditional method ignores hydraulic connection is solved; a multi-task collaborative learning mechanism: synchronously outputting a flow reconstruction value and confidence, combining topological smooth constraints, and realizing reconstruction reliability quantification while ensuring precision; and meteorological dynamic weighted correction: dynamically adjusting the node weight based on the real-time rainfall intensity, and accurately adapting to the nonlinear response of the water flow in the heavy rainfall period.

The invention relates to the technical field of image data processing, in particular to a data reconstruction method and system for a building house remote sensing surveying and mapping model, and the method comprises the steps: obtaining the three-dimensional point cloud data of a building house, and carrying out the initial layering through the local geometric features of the three-dimensional point cloud data, and obtaining all target levels; determining whether false point cloud data exist in the target levels and determining correction parameters of the false point cloud data by utilizing a geometric structure relationship of joint surfaces between the target levels of the adjacent building stages; under the condition that the false point cloud data exists in the target hierarchy, correcting the point cloud data in the target hierarchy by using the correction parameter to obtain corrected point cloud data; and determining a thinning parameter of the target level by using the corrected point cloud data, and determining a target point cloud model of the building house by using the thinning parameter. According to the method, error points or irrelevant points can be effectively removed, the three-dimensional data of the building is ensured to be more suitable for the actual situation, and meanwhile, the processing amount of false point cloud data is reduced.

The invention discloses an animal behavior recognition method and system based on deep learning. The method comprises the steps of data collection, data reconstruction, individual behavior classification, group correlation analysis and animal behavior recognition. The invention relates to the technical field of animal behavior recognition, in particular to an animal behavior recognition method and system based on deep learning, and adopts a comprehensive animal behavior recognition method combining individual analysis and group analysis to recognize a specific behavior type through individual behavior analysis. Comprehensive analysis and clustering are carried out by combining group correlation analysis with a plurality of animals, so that the recognition depth and comprehensive availability of animal behavior recognition are improved; performing individual behavior classification by adopting a residual convolutional recurrent neural network combined with structure and attention improvement; and performing group correlation analysis by adopting a dynamic and static graph neural integration network combined with reinforcement learning.

A unified platform for multi-type data compression and decompression is disclosed. The platform employs a virtual management layer to receive, organize, and route input data to corresponding compression subsystems. Multiple compression methods, including homomorphic encryption-based techniques, are utilized to compress data sets while maintaining data privacy. A data manager associates and manages related data sets throughout the compression and decompression processes. Compressed data is routed to appropriate decompression subsystems, where it is decompressed and reconstructed using advanced techniques, such as neural upsampling, to recover lost information and enhance data quality. The platform supports various data types and compression methods, enabling efficient and secure compression and decompression of data. By integrating homomorphic encryption and data reconstruction techniques, the platform provides a comprehensive solution for data compression and decompression while preserving data privacy and enhancing data quality.

A system and method are provided for creating magnetic resonance (MR) images with reduced motion artifacts from the MR data from which the images are produced. The method includes selecting a candidate image from a plurality of candidate images reconstructed from the MR data. The method also includes registering the candidate image to a reference image, comparing the candidate image to a consistency map, and, based on comparing the candidate image using the consistency map, selecting a blending algorithm. The method also includes generating a blended image using the blending algorithm and the candidate image and repeating these steps for each candidate image. The method also includes performing a Fourier aggregation to generate a combined image and displaying the combined image with reduced motion artifacts compared to the plurality of candidate images.

The invention provides a power big data-based sparsity feature adaptive compression method, which comprises the steps of adaptively optimizing a sensing matrix through a dictionary learning method aiming at a screened multi-granularity feature subset, introducing a sparse regularization item to enhance the sparsity of data reconstruction, and according to a missing mode of a missing value and context information, carrying out adaptive compression on the sparse feature of the power big data. Establishing a mapping relationship among the compression ratio, the reconstruction error and the feature subset, and determining a self-adaptive compression strategy; based on a self-adaptive compression strategy, a compressed sensing method is adopted to carry out self-adaptive compression on the multi-time-granularity power data, the compression ratio is dynamically adjusted according to reconstruction error feedback, the data compression and reconstruction quality is balanced, and the reconstructed multi-granularity power data is synchronized according to timestamp precision.

The invention relates to the technical field of marine environment, in particular to a marine environment robust data reconstruction method and system based on a graph neural network. The method comprises the following steps: acquiring marine environment data; performing data preprocessing on the acquired marine environment data; constructing a graph structure based on geographic position information in the marine environment data; establishing a marine environment robust data reconstruction model based on the graph neural network; and identifying the marine environment data by using the marine environment robust data reconstruction model to obtain an identification result. According to the invention, by introducing a structure of combining a feature completion network and a denoising network, the model can automatically learn a deep association relationship in ocean data with high noise and feature deficiency, and capture key node features through a dynamic attention mechanism, so that the accuracy and robustness of feature representation are effectively improved.

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 embodiment of the invention provides an offset scanning offset determination method and device, equipment and a storage medium, and belongs to the technical field of offset scanning. Electronic equipment can obtain projection data obtained by performing offset scanning on a target object; based on the pixel value difference of the projection data pair in each column in the projection data, determining the predicted position of the rotation center shaft mapped in the detector; determining the offset range of the detector according to the pixel value difference of the projection data pair in the preset range; according to the pixel value fluctuation condition of the reconstructed image, the offset of offset scanning is determined, the preset range is a preset number of column data including the predicted position in the projection data, and the reconstructed image is obtained by reconstructing the projection data based on the offset. Due to the fact that the projection data are directly analyzed and processed, the accurate offset amount of offset scanning is determined, a correction die body does not need to be used, the process of determining the offset amount of offset scanning can be simplified, and the efficiency of determining the offset amount of offset scanning can be improved.

In various examples, a language model may be trained and used as part of an interface for a simulation system. For instance, user inputs may be applied to the language model and the language model may be trained to generate code, make API calls, or perform any other operations to interact with and / or control various aspects of the simulation. In some examples, the language model may generate code for, among other things, creating and / or customizing a virtual environment associated with the simulation. For instance, the generated code may include, but is not limited to, code for rendering the virtual environment, code for rendering and simulating behaviors of virtual agents (e.g., pedestrians, vehicles, animals, etc.) and / or any other objects (e.g., road signs, buildings, trees, etc.) within the virtual environment, code for recreating and simulating real-world events from recorded sensor data, etc.

A magnetic resonance image reconstruction device according to an embodiment is a magnetic resonance image reconstruction device that reconstructs magnetic resonance image data in which an artifact due to undersampling is removed or reduced based on undersampled k-space data, and includes a reconstruction unit reconstructing the magnetic resonance image data using a reconstruction network having a correction module. The correction module includes a regularization block generating second image data by performing a regularization process on first image data using a first neural network, and a data consistency block generating third image data by performing a data consistency process so that k-space data corresponding to the second image data approaches the undersampled k-space data. The correction module further includes at least one of a data consistency adjustment block adjusting the data consistency process and a regularization adjustment block adjusting the regularization process.

Various embodiments of the present technology relate to synthetic aperture radar image capturing and scene reconstruction using a cluster of multi-static satellite receivers. More specifically, in some embodiments, the cluster of receivers retrieves a flight path over a communication network. Flying in-formation, a lead or main satellite sends radio or wave pulses to a target area and additional satellites capture an echo of the wave pulses after the waves bounce off the target area to capture SAR radar data. The radar data can be transmitted from the cluster of satellites to a radar stations or to the main satellite. The radar stations or the main satellite receive the SAR radar data and mapping to reconstruct the data as SAR image data that can be reconstructed as 2D or 3D landscapes and scenes using the SAR image data.

The invention discloses a PCIe cross-version link capability mapping and transaction data reconstruction method, which comprises the following steps: link negotiation and physical layer opposite end simulation: a cross-generation PCIe bridging device simulates high-version equipment behavior training and establishes a first link between the cross-generation PCIe bridging device and a host, the cross-generation PCIe bridging device simulates behavior training of low-version equipment and establishes a second link between the cross-generation PCIe bridging device and the low-version equipment; data processing and transmission: the cross-generation PCIe bridging device processes data received from the host to adapt to and transmit the data to the low-version device, the cross-generation PCIe bridging device processes data received from the low-version device to adapt to and transmit the data to the host, the high version is a PCIe Gen6 protocol, and the low version is a PCIe Gen6 protocol. The low version is a PCIe (Peripheral Component Interconnect Express) Gen5 protocol or a PCIe Gen4 protocol.