19results about How to "Guaranteed reconstruction accuracy" patented technology

The invention discloses an SAR imaging method and system based on sparse and low-rank decomposition, and a readable storage medium. The imaging method comprises the following steps: collecting an SAR image and two-dimensional echo data, and preprocessing the two-dimensional echo data to obtain a target reflectivity; and based on the linear relationship between the two-dimensional echo data and the target reflectivity, decomposing the SAR image by using a preset method to obtain a sparse component and a low-rank component. A decomposition result is iteratively updated through a preset optimization algorithm, the precision is improved until errors converge, and a sparse and low-rank decomposition algorithm expression is obtained; and solving a preset SAR imaging model by using the sparse and low-rank decomposition algorithm expression, outputting an SAR imaging result, and finally outputting the SAR imaging result. According to the invention, the complexity of SAR imaging can be effectively reduced, and the imaging efficiency is improved. Even if the data sampling rate is reduced, the accuracy of image reconstruction can be ensured.

The invention relates to the technical field of intelligent chips, and discloses an integrated module-oriented data stream intelligent reconstruction method and device and an intelligent chip, and the method comprises the steps: carrying out the dual-flow feature decomposition of a chip integrated module data stream, and obtaining a first module stream feature extraction vector and a first data stream feature extraction vector; inputting an adversarial network for feature optimization to obtain a second module flow feature extraction vector and a second data flow feature extraction vector; constructing a directed causal graph based on the chip system operation parameters, and performing graph neural network coding to obtain an operation mode embedded vector; inputting a conditional variational self-encoding network for causal decoupling to obtain a decoupling feature vector; performing probability density calculation and boundary distance calculation on the decoupling feature vector to obtain a data stream security score; and carrying out multilayer Bayesian perception reconstruction to obtain a target reconstruction data stream. According to the method, the problem of data distribution change under different operation conditions is effectively solved, and the reliability of the reconstructed data stream is improved.

The invention discloses a rolling bearing fault detection method and system based on mechanism and data fusion, and relates to the technical field of equipment state monitoring and fault diagnosis, and the method comprises the steps: collecting a multi-source operation state signal and an operation condition parameter of a rolling bearing in real time; constructing and solving oscillatory differential equations under different fault types based on the signals and the parameters in combination with a rolling bearing dynamical model, and generating a mechanism feature library; denoising vibration signals in the multi-source operation state signals, and performing data reconstruction on the denoised vibration signals based on a gamma hybrid model; generating a high-dimensional data feature set according to the reconstructed vibration signal, and performing dimension reduction on the high-dimensional data feature set by adopting a matrix t-SNE algorithm to obtain low-dimensional data features; constructing a joint feature set according to the low-dimensional data features and the mechanism features, and calculating a health index; and the severity and position of the fault are judged based on the joint feature set and the health index, so that the precise detection requirement of the early-stage tiny fault of the rolling bearing under the complex working condition can be met.

The invention relates to the technical field of computer vision and coal mine intelligent mining, and discloses a lightweight working face three-dimensional reconstruction method and device fusing geometric prior constraints, and the method comprises the steps: firstly screening a dynamic effective region based on a projection relation and texture features, and removing static redundant scenes; performing sparse visual reasoning and depth regression only for the effective area to generate an initial depth map; carrying out physical structure correction on the depth map by utilizing geometric priori such as vertical upright posts and parallel top plates of the hydraulic support; and finally, constructing an optimization objective function containing deviation constraints of the vertical column perpendicularity and the top plate parallelism, and completing high-precision fusion of the incremental point cloud. According to the method, inference delay is reduced by reducing invalid calculation, structural distortion and registration drift caused by an underground severe environment are effectively inhibited by using a geometric law, and real-time and high-fidelity reconstruction of a working face scene is realized.

The invention relates to a three-dimensional reconstruction method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a structured light stripe image; the structured light stripe image comprises a first view and a second view; performing phase decomposition on the structured light stripes in the first view and the second view to obtain a first phase diagram and a second phase diagram; based on a depth and parallax mapping relation of the structured light stripe image, determining a parallax search range corresponding to a first target pixel point in the first phase image in the second phase image, and performing matching according to the parallax search range to obtain a second target pixel point in the second phase image; disparity maps of the first phase diagram and the second phase diagram are obtained according to the first target pixel point and the second target pixel point; and performing three-dimensional reconstruction on the structured light stripe image based on the disparity map to obtain point cloud data. By adopting the method, the three-dimensional reconstruction efficiency can be improved.

The present invention relates to the technical field of infrared image super-resolution reconstruction, and specifically relates to a multi-scale infrared image super-resolution reconstruction method, system and storage medium; the multi-scale infrared image super-resolution reconstruction method, system and storage medium comprise the following steps: photographing a natural scene with an infrared camera to obtain an infrared image FLS data set, and dividing the data set into a training set and a test set; a super-resolution reconstruction network is composed of three parts: a shallow feature extraction block, a deep feature extraction block and a reconstruction block; the network is first pre-trained with an ImageNet data set, then trained with an infrared image data set FLS, and finally a composite loss function is used to constrain the network training so that a high-resolution infrared image with clear texture and edge is reconstructed; the present invention enables infrared image super-resolution reconstruction to have a smaller amount of calculation, a smaller amount of parameters and a faster reconstruction speed while maintaining the super-resolution accuracy, which is more in line with practical applications.

The invention discloses a regeneration method of a molybdenum removal extraction agent for a waste acid system, and belongs to the technical field of extraction agent regeneration. The method comprises the following steps: adding 0.05-0.1 wt.% of polybasic carboxylate, organic phosphonate or hydroxy carboxylate complexing agent into a 5-8wt.% carbonate buffer system, mixing a water phase and an aging extraction agent according to a phase ratio O / A of 1: 1, reacting at 20-35 DEG C for 8-12 minutes, and separating an organic phase from a weak base phase. And washing the organic phase with 0.5-1.0 mol / L sulfuric acid according to a phase ratio O / A of 1: 1, separating, washing with water, and separating the organic phase from the water phase again. And dehydrating the organic phase until the water content is less than or equal to 150ppm, contacting the organic phase with a pre-equilibrium liquid containing sulfuric acid and / or sulfate according to a phase ratio O / A of 1: 1, so that the acid value of the organic phase is 0.10-0.35 mg KOH / g, and recovering the addition ratio of TBP.H2SO4 to 0.05-0.20 mol / mol. Adding 50-200ppm of hindered phenol antioxidant into the organic phase, filtering, and then sealing and aging at room temperature; the regenerated extraction agent has the advantages of low water content, stable acid value, improved interfacial tension, elimination of metal poisoning, good phase behavior, extraction capacity recovery degree of more than 90%, stable long-term cycle performance, and no generation of emulsification and third phase.

The application discloses a real-time reconstruction method of a dynamic human body, which comprises the following steps: connecting a human body measuring device; solving camera internal parameters and relative poses between two cameras; collecting a reference state image; dividing a speckle area; three-dimensionally reconstructing the reference state; collecting images on a time sequence; three-dimensionally reconstructing human body speckles on the time sequence; and quickly triangulating a dynamic three-dimension point cloud. On the premise of ensuring reconstruction accuracy, the point cloud density of the reconstructed human body data can be greatly improved, the reconstructed point cloud data can more fully express local details of the human body, real-time calculation of the dynamic human body can be completed, and online measurement and reconstruction of the human body can be realized.

The present application relates to a kind of image thick cloud removal pre-processing method for high-performance video coding application, it is related to image processing technical field, solve the technical problem that it is difficult to fully capture cross-scale complementary features, there is deficiency in the balance between global structure and local detail.The pre-processing method includes: based on the guided image estimation of non-subsampled shearlet transform and the cloud removal model of full connection tensor decomposition guided by non-subsampled shearlet transform to carry out multi-time remote sensing image thick cloud removal.The present application is an image thick cloud removal pre-processing method for high-performance video coding application, realizes the generation of high-quality reference image: to obtain the reference image containing more details, first, the multi-time remote sensing image is carried out gamma correction, to normalize radiation intensity;Subsequently, improved adaptive PCNN fusion algorithm is applied in NSST domain, thereby enhancing detail representation while maintaining image energy distribution, finally, high-quality reference image is obtained.

The invention provides a calibration method, a reconstruction method, a calibration system and a reconstruction system.The calibration method comprises the steps that a three-dimensional scanning device is controlled to scan a two-dimensional code on a calibration plate, and a real world coordinate set of a first mark graph and a second mark graph in a world coordinate system is obtained; controlling the three-dimensional scanning device to acquire a calibration image comprising the calibration plate, and identifying a first mark graph and a second mark graph on the calibration image to obtain image coordinates of the mark graphs in an image coordinate system; positioning the direction of the calibration plate by using the first mark graph, obtaining the first mark graph and the second mark graph in combination with the direction of the calibration plate, and obtaining real-time world coordinates from the real-world coordinate set; and calibrating the three-dimensional scanning device through the real-time world coordinates and the pixel coordinates of the first mark graph and the second mark graph to obtain calibration parameters of the three-dimensional scanning device. The calibration method provided by the invention is more convenient to operate and has higher calibration precision and reconstruction precision.

The invention discloses an environment and channel state information joint reconstruction method and system. Wireless channel measurement data and environment information are collected in a target area through downlink detection and perception, the environment information in the target area is defined through a binary representation method and serves as a mask to be coupled with channel state information, and a novel channel fingerprint coupling the environment information and the channel state information at the same time is obtained; by constructing a conditional variation auto-encoder network model, a high-resolution channel fingerprint is used as a reconstruction target, a low-resolution channel fingerprint is used as condition input, the high-resolution channel fingerprint is mapped to a potential space through an encoder, and the high-resolution channel fingerprint is reconstructed through a decoder under the given low-resolution channel fingerprint condition. According to the invention, on the premise of ensuring high fidelity of channel state information, accurate environment perception is realized, pilot frequency overhead and perception measurement overhead in an actual system are effectively reduced, and the method has good scene adaptability.

The invention provides a phenotype acquisition method and device based on a neural radiation field, and relates to the technical field of agricultural informationization, and the method comprises the steps: obtaining an image sequence of a to-be-reconstructed plant collected by shooting equipment in multiple view angle directions, generating internal and external parameters of the shooting equipment based on the image sequence through an image segmentation large model and a motion structure recovery algorithm, and obtaining a phenotype of the to-be-reconstructed plant; taking as a training data set; training the plant three-dimensional reconstruction model based on the neural radiation field network framework by using the training data set, and generating implicit three-dimensional scene representation based on the image sequence through the trained plant three-dimensional reconstruction model; the model comprises a multi-resolution hash coding module, a volume density prediction unit based on an ECA attention mechanism, a color prediction unit based on a multi-layer perceptron and a volume rendering module. And extracting phenotypic parameters corresponding to the plant to be reconstructed based on the implicit three-dimensional scene representation. The method has relatively high crop reconstruction precision, and meanwhile, the crop reconstruction time can be greatly shortened.

The application provides a global data reconstruction method and device based on position mapping and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: for each source data in at least two source data, a mapping relationship between global data and the source data is respectively constructed, and the mapping relationship is stored as index data independent of the source data, wherein the index data corresponding to different source data is different; in response to a reconstruction instruction, the positions of target data segments corresponding to each data point in the global data in the source data are determined based on the index data; and the target data segments are extracted from the source data for fusion to generate the global data. The application realizes the decoupling of the mapping logic and the source data, avoids the residence of full data in the memory, significantly reduces the memory consumption, and improves the calculation efficiency and data reusability.

The application discloses a big data-based equipment operation anomaly detection method and system, relates to the technical field of equipment detection, and comprises the following steps: obtaining historical operation data of a target equipment in a healthy state, and constructing a high-dimensional equipment operation big data pool; constructing and training a deep autoencoder, and establishing an equipment health baseline set for the target equipment; obtaining real-time operation data of the target equipment, and calculating an encoding residual error vector of the real-time operation data relative to the equipment health baseline set; taking the encoding residual error vector as an analysis target, inputting the encoding residual error vector into a pre-trained lightweight classifier for anomaly classification detection, and outputting operation anomaly information according to the anomaly classification detection result. The technical problems of low equipment anomaly detection precision, high false alarm rate and difficulty in adapting to complex working condition changes in the prior art are solved.

The present disclosure provides information feedback methods and apparatuses, a user equipment, a base station, a system model and a storage medium. The method includes: obtaining a channel state information (CSI) matrix; screening, based on self-information of the CSI matrix, elements in the CSI matrix to obtain a sparse CSI matrix; and determining, based on elements in the sparse CSI matrix, feedback information and sending the feedback information to a base station. In the methods of the present disclosure, the base station is higher in matrix reconstruction accuracy and lower in overhead.

The application discloses a single-view intraoperative blood vessel dynamic three-dimensional reconstruction method based on preoperative topology priori, which comprises the following steps: acquiring preoperative three-dimensional body data and constructing a three-dimensional blood vessel center line topology structure diagram, thereby providing strong constraint geometric prior information for subsequent cross-modal matching; acquiring intraoperative two-dimensional image data and constructing a two-dimensional blood vessel center line topology structure diagram; matching based on the structure consistency of the three-dimensional blood vessel center line topology structure diagram and the two-dimensional blood vessel center line topology structure diagram, thereby establishing a cross-modal matching relationship; performing adaptive motion phase estimation based on an anatomical site, thereby obtaining a motion estimation result; performing adaptive deformation compensation and model dynamic updating based on the anatomical site, adopting an implicit curved surface reconstruction technology based on a circular cross section or a fast mesh generation technology based on a convolution surface to perform dynamic three-dimensional blood vessel surface model reconstruction and rendering; performing dynamic three-dimensional blood vessel surface model reconstruction and rendering; and performing multi-mode navigation fusion display of the dynamic three-dimensional blood vessel surface model and a two-dimensional angiography image. Corresponding systems, devices, electronic equipment and computer readable storage media are also disclosed.

The invention relates to a mechanical structure response reconstruction method and system based on optimal modal response estimation, and belongs to the field of structure response reconstruction, and the method comprises the steps: constructing a response estimation signal according to a free vibration modal response form; calculating a residual error between the response estimation signal and the real measurement signal; constructing an optimization objective function according to the spectral characteristics of the residual signals; solving the optimization function by using a gradient descent algorithm to obtain an estimated modal response of each order; and structural response reconstruction is completed according to the modal transfer ratio equation. According to the method, a modal superposition method of a linear system is taken as a theoretical basis, an optimal modal response estimation signal is obtained by constructing a response estimation signal and optimizing the spectral characteristics of a residual signal, and finally, response reconstruction is performed through a modal transfer ratio equation, so that the optimal modal response estimation signal is obtained. According to the method, response reconstruction of a structure with dense modal characteristics can be realized based on single sensor data, and the accuracy of response reconstruction is ensured.

The invention relates to the field of atmospheric wind field modeling, and provides a full-scale wind field numerical reconstruction method for low-altitude torrent accompanying wind direction vertical shear in order to improve calculation efficiency, which directly takes target statistical characteristics as a driving target, concentrates calculation resources for a most critical structural wind field interaction area, and improves the calculation efficiency. By iteratively adjusting the parameter of a single control point of the entrance boundary, the wind field at the target position can be quickly converged to the predetermined feature, and high-resolution solution does not need to be performed on the whole atmospheric boundary layer, so that the order of magnitude improvement of the calculation efficiency is realized on the premise of ensuring the reconstruction precision of the wind field at the target position.

The present invention relates to the technical field of hazardous chemical safety monitoring methods, and more specifically, to a hazardous chemical safety monitoring method based on big data, comprising step 1. heterogeneous sensor data acquisition and preprocessing; step 2. associated spatiotemporal feature extraction; step 3. dual autoencoder dictionary learning: designing a dual sparse autoencoder network with joint local-global constraints, while minimizing data reconstruction error and dictionary size, and using an alternating iterative proximal gradient descent algorithm to solve the objective function; step 4. dictionary parameter optimization based on quantization coding; step 5. multi-level joint decoding; step 6. abnormality diagnosis based on enhanced reconstruction sequence, achieving multiple synergistic efficiencies in multimodal heterogeneous data compression, spatiotemporal association modeling, dictionary learning and encoding, reconstruction optimization, and abnormality diagnosis.