46 results about "Linear reconstruction" patented technology

The invention discloses an electricity larceny identification method based on graph calculation, and particularly relates to the technical field of electricity utilization anomaly detection of an electric power system. Historical power consumption data and a power supply topological relation of power consumers are collected, and a multi-dimensional behavior graph model fusing behavior characteristics and structural information is constructed; performing structure disturbance analysis on each node in the graph, calculating information entropy change before and after node removal, performing attention fusion on a time sequence behavior feature of the node and a structure disturbance vector, constructing a joint feature vector, and enhancing feature expression through spectral clustering and linear reconstruction; a behavior propagation field and a disturbance adjustment mechanism are introduced into the graph to form a disturbance response graph, and an abnormal gathering area is identified through path energy analysis and focusing area fitting; calculating confidence scores of the nodes and outputting a suspicious user list; the method can realize efficient identification of electricity stealing behaviors with strong concealment and complex transmissibility, and has the advantages of high precision, strong interpretability and wide application scene adaptability.

The invention provides a hyperspectral snapshot compression imaging method and system based on a space-spectrum prior decoupling model, high-quality reconstruction is realized through decoupling optimization and a deep expansion network, and the method comprises the following steps: constructing a training data set containing a compression measurement image and a corresponding reconstruction spectrum; establishing an objective function fusing space and spectrum prior, converting the objective function into constrained optimization, and converting the constrained optimization into three sub-problems of linear reconstruction, space prior and spectrum prior by adopting a semi-quadratic splitting method; a deep expansion network is designed to alternately solve sub-problems: a linear sub-problem is solved through analysis, and a space / spectrum sub-problem is subjected to implicit prior modeling through a private network, so that end-to-end reconstruction is realized; a mixed loss function is adopted to optimize model parameters, and images can be reconstructed in real time after training is completed. Space and spectrum prior decoupling is carried out, space structure details and spectrum features are respectively captured through an independent network architecture, the problem of mutual interference of joint modeling in a traditional method is solved, and high-quality spectrum image reconstruction is realized.

The invention relates to a bridge modal parameter automatic identification method and system considering multi-channel information, and the method comprises the following steps: S1, directly analyzing a multi-channel monitoring signal through COV-SSI, generating a stability diagram, and automatically extracting a stability axis in combination with a DBSCAN clustering algorithm, thereby achieving the automatic identification of a modal frequency; s2, performing signal decomposition on the multi-channel monitoring data by adopting MvFIF to generate an intrinsic mode function (IMF) group with a mode alignment characteristic; s3, the instantaneous frequency and bandwidth of the IMF in the step S2 are calculated through HHT, IMF components containing target modal frequency are screened out, and linear reconstruction is carried out; s4, taking the multi-channel monitoring data reconstructed in the step S3 as system input of a COV-SSI algorithm, calculating a system matrix and an output matrix, and calculating a modal damping ratio by utilizing eigenvalue decomposition; according to the method, the spatial-temporal correlation among multi-channel data can be considered, and synchronous decomposition of multi-channel monitoring data is realized; and automatic and accurate identification of the structural modal parameters under different noise levels can be realized.

The invention provides an effective wave height real-time prediction method and device based on dynamic residual correction, and relates to the field of ocean engineering. The method comprises the following specific steps: acquiring wave height data and performing multi-dimensional feature screening; constructing an integrated filter fusing L1 trend filtering and variational mode decomposition, optimizing parameters by using a sea image optimization algorithm, introducing a causal sliding window to extract features so as to construct a time sequence input tensor, and inputting the time sequence input tensor into a stacked bidirectional long-short-term memory network based on an attention mechanism after noise addition standardization so as to obtain a basic predicted value; calculating a manifold coherent structure, PID dynamics and physical statistical characteristics, and cascading with the basic prediction characteristics to construct a comprehensive element characteristic vector; a LightGBM architecture is constructed, and a prediction residual error is fitted after optimization is carried out through a sea image optimization algorithm; and finally, executing linear reconstruction based on the dynamic safety threshold constraint, and outputting a real-time correction result. According to the method, the error evolution rule is deeply mined by using manifold geometric features, and the real-time precision and robustness of significant wave height prediction are remarkably improved.

A photovoltaic power generation power prediction method based on VMD-ISSA-BiGRU relates to the technical field of new energy power generation, and comprises the following steps: obtaining a photovoltaic power sequence and a meteorological data sequence; variational mode decomposition is carried out on the photovoltaic power sequence; respectively extracting time sequence characteristics of each intrinsic mode component and the meteorological data sequence, and analyzing the correlation between each time sequence characteristic and power output; a hyper-parameter of the BiGRU-Transform model is optimized by adopting an ISSA (International Standard Standard Architecture); on the basis of the optimized hyper-parameters and the reserved time sequence characteristics, a BiGRU-Transform model is constructed; respectively inputting the intrinsic mode components into a BiGRU-Transform model for calculation, and obtaining a predicted value of each component; performing linear reconstruction on prediction results of all components to obtain a photovoltaic power prediction sequence; the method is used for solving the problems of low traditional prediction precision and poor dynamic processing capability.

The invention discloses a precipitation forecast correction method combining EOF projection and a U-Net network, and the method comprises the steps: extracting a main spatial mode of a precipitation abnormal field based on historical observation data, and carrying out the projection reconstruction of a mode forecast abnormal field on this basis, and obtaining a spatial structure with physical significance; an observation climate state is introduced to replace a mode climate state, so that systematic deviation of the mode is effectively eliminated; for residual terms which are not explained in the reconstruction process, U-Net is adopted for modeling and prediction so as to capture complex nonlinear error components in the mode; and finally, superposing a linear reconstruction result and a residual term of deep learning prediction to form a final rainfall forecast correction result. According to the method, a correction framework combining physical driving and data driving is constructed, and on the basis of keeping the physical interpretability of a traditional EOF method, a deep learning model is introduced to model a residual term, so that dual capture and correction of linear errors and nonlinear errors in mode forecasting are realized.

The invention discloses a cascade hydropower decoupling optimization complementary scheduling method under multi-source uncertainty requirements, and belongs to the field of hydropower scheduling. According to the method, a new decoupling scheduling normal form of'interval commitment-autonomous response 'is provided for multi-source uncertainty requirements of new energy output fluctuation, power spot market clearing power fluctuation and the like faced by a power system, so that the problems of response lag, low decision-making efficiency and limited adjustment capability existing in a traditional'plan reporting-negotiation adjustment' scheduling mode are solved. The method comprises the following steps: firstly, establishing a cascade hydropower decoupling scheduling framework, enabling each power station to autonomously calculate and report a dynamic adjustable electric quantity interval based on own complex hydraulic constraint, and constructing a cascade hydropower decoupling optimization complementary scheduling model under a multi-source uncertainty demand on this basis; therefore, the regulation capability boundary of the water and electricity in the multi-source uncertainty environment can be accurately quantified. And finally, converting an original problem into a mixed integer linear programming model through a two-stage solving algorithm combining linear reconstruction and a strong duality theory to realize efficient solving.

The invention discloses a high-definition image real-time lightweight compressed sensing method based on a convolutional neural network. The method comprises the following steps: step 1, obtaining a compressed sensing measurement value y with the size of rCB2 * W / B * H / B; 2, setting two initial linear reconstruction branches with different reconstruction scales for up-sampling; step 3, completing depth nonlinear reconstruction with more downscaling; step 4, sending to two Residual modules which are connected in series and are improved by SE attention modules; step 5, sending to an SE attention module; step 6, obtaining a feature map after the two improved Residual modules are connected in series; step 7, executing three rounds of feature maps obtained after series processing of the DAP modules in the steps 4 to 6; and 8, carrying out adaptive weighted merging reconstruction on the down-sampling reconstructed image and the reconstructed image. The high-definition image real-time lightweight compressed sensing method based on the convolutional neural network can effectively improve the calculation speed and reduce the occupation of calculation resources.

The application discloses a structure nonlinear deformation reconstruction method and system based on isogeometric and reduced basis, and the method comprises the following steps: measuring initial strain data of a ship plate structure surface, constructing a plurality of four-node inverse shell elements of the ship plate based on the initial strain data; assembling the plurality of four-node inverse shell elements to obtain a whole stiffness matrix; reconstructing the whole stiffness matrix based on the measured strain data of the ship plate to obtain a linear reconstruction result of the ship plate; correcting the linear reconstruction result based on the reduced basis, and fusing the correction increment to the linear reconstruction result for nonlinear iteration to complete nonlinear deformation reconstruction. On the basis of the principle of isogeometric analysis, the application combines the inverse finite element method to establish a four-node inverse shell element, realizes high-precision ship structure displacement reconstruction through a small number of strain measuring points, and helps designers to intuitively understand and deeply understand the mechanical properties of the structure and to monitor the safety of the structure in real time.

The application provides a temperature and concentration reconstruction method based on a normalized second harmonic linear model, belongs to the technical field of tunable diode laser absorption spectroscopy, and is used for two-dimensional temperature and concentration field reconstruction. The reconstruction system comprises a laser control and generation module, a fiber beam splitter, a sensor, a Mach-Zehnder interferometer, a photodetector, a data acquisition system and a computer. The reconstruction method comprises the following steps: after laser is split, one path is connected to the Mach-Zehnder interferometer and is received by the photodetector, the remaining paths pass through the to-be-detected area after being expanded and are received by a photodetector plate, and normalized second harmonic spectra of transmitted light intensities on each light path are obtained; a normalized second harmonic base matrix is calculated according to discrete temperature and concentration; a linear reconstruction model is established according to the sensor light path arrangement, and an iterative algorithm is used to obtain two-dimensional temperature and concentration distribution. The application effectively utilizes the normalized second harmonic spectrum, increases the number of independent equations, and improves the speed and accuracy of two-dimensional temperature and concentration distribution reconstruction.

The invention relates to the technical field of crossing of artificial intelligence and machine vision, discloses an AI-vision-based machine tool frame casting defect detection method and system, and aims to solve the technical problems that the image quality is reduced and the defect detection accuracy is influenced due to sudden change of instantaneous illumination in an industrial field. According to the method, illumination time sequence data are collected in real time by integrating a main imaging sensor and a high-frequency auxiliary light field sensor array, and future light field distribution is predicted based on a hardware Kalman filter; and generating a pixel-level dynamic gain matrix according to the main image, performing regional adaptive exposure compensation on the main image in a simulation domain, performing linear reconstruction and gain adaptive denoising, and outputting an illumination balanced image for an AI model to detect microcracks and pores. The system realizes microsecond-level illumination abrupt change response and local accurate compensation, and obviously improves the imaging consistency and defect detection rate.

The application discloses a CPU-GPU-based group target RCS high-efficiency parallel calculation method, which converts the linear reconstruction of group target characteristic current from a cycle-level calculation mode to a matrix-level calculation mode of a GPU platform, adopts a multi-layer fast multipole method, and accelerates the calculation of electromagnetic coupling between targets under a CPU-GPU heterogeneous parallel framework, wherein the calculation task division of aggregation and configuration matrix filling is based on the target and facet dimension, the calculation task division of transfer matrix filling is based on the grouping box pair and beam dimension, and the calculation and superposition of group target far-field scattering are based on the parallel processing mode of the target, facet and angle dimension. Through the cooperative calculation of CPU and GPU, the application can significantly improve the RCS calculation efficiency and scalability of large-scale group targets.

The application relates to a bridge modal parameter automatic identification method and system considering multi-channel information, which comprises the following steps: S1, generating a stability diagram by directly analyzing multi-channel monitoring signals by using COV-SSI, automatically extracting stable axes by combining a DBSCAN clustering algorithm, and realizing automatic identification of modal frequencies; S2, decomposing multi-channel monitoring data by using MvFIF to generate an intrinsic modal function (IMF) group with modal alignment characteristics; S3, calculating the instantaneous frequency and bandwidth of the IMF in step S2 by using HHT, screening out an IMF component containing a target modal frequency, and performing linear reconstruction; and S4, taking the multi-channel monitoring data after the reconstruction in step S3 as system input of the COV-SSI algorithm, calculating a system matrix and an output matrix, and calculating modal damping ratios by using eigenvalue decomposition; the method can consider the space-time correlation between multi-channel data, realize synchronous decomposition of multi-channel monitoring data, and realize automatic and accurate identification of structural modal parameters under different noise levels.

The application provides a hyperspectral snapshot compression imaging method and system based on a space-spectrum prior decoupling model, high-quality reconstruction is realized through decoupling optimization and a deep unfolding network, and the method comprises the following steps: constructing a training data set containing a compression measurement image and corresponding reconstructed spectrum; establishing a target function fusing space and spectrum priors and converting the target function into a constraint optimization; adopting a semi-quadratic splitting method to convert the constraint optimization into three sub-problems of linear reconstruction, space prior and spectrum prior; designing a deep unfolding network to alternately solve the sub-problems; the linear sub-problem is solved analytically, the space / spectrum sub-problems are respectively modeled by special networks to implicitly model the priors, and end-to-end reconstruction is realized; a hybrid loss function is adopted to optimize model parameters, and after training, the image can be reconstructed in real time. The space and spectrum priors are decoupled, space structure details and spectrum characteristics are respectively captured through independent network architectures, the mutual interference problem in the joint modeling of traditional methods is overcome, and high-quality spectrum image reconstruction is realized.

The application discloses a kind of based on visual measurement's abutment deformation monitoring method, equipment and medium, it is related to optical measurement technical field, comprising: by real horizontal displacement sequence and real height interval sequence, construct height slope distribution and extract slope variation, combine abutment measurement total height to calculate top and bottom corner amount, according to slope variation to top and bottom corner amount is consistent correction, form abutment overall corner amount;According to abutment overall corner amount to real horizontal displacement sequence is linearly reconstructed, obtain linearly reconstructed real horizontal displacement sequence, utilize the difference of linearly reconstructed real horizontal displacement sequence and real horizontal displacement sequence and extract consistency residual quantity, and from real horizontal displacement sequence separate abutment overall translation, form abutment deformation monitoring quantity.The application is linearly reconstructed according to abutment overall corner amount and calculates consistency residual quantity, realizes the separation representation of abutment overall translation, abutment overall corner amount and consistency residual quantity.

A photovoltaic power prediction method based on VMD-ISSA-BiGRU relates to the technical field of new energy power generation, comprising: acquiring a photovoltaic power sequence and a meteorological data sequence; performing variational mode decomposition on the photovoltaic power sequence; extracting time sequence features from each intrinsic mode component and the meteorological data sequence, and analyzing the correlation between each time sequence feature and power output; optimizing the hyperparameters of the BiGRU-Transformer model using ISSA; constructing the BiGRU-Transformer model based on the optimized hyperparameters and the retained time sequence features; inputting the intrinsic mode components into the BiGRU-Transformer model for calculation to obtain the prediction values of each component; and performing linear reconstruction on the prediction results of all components to obtain a photovoltaic power prediction sequence; which is used to solve the problems of low traditional prediction accuracy and poor dynamic processing capability.

Some aspects of the present disclosure provide a computer-implemented method for training a nonlinear reconstruction algorithm to reconstruct an image. The method includes accessing magnetic resonance (MR) image data with a computer system. The MR image data include previously reconstructed images that were generating by reconstructing k-space data acquiring with an MRI system. The method further includes using the computer system to train a physics-driven nonlinear reconstruction algorithm having an objective function. The objective function includes a regularization unit and a data consistency unit, which is defined relative to the previously reconstructed images of the MR image data. The method further includes storing the reconstruction algorithm using the computer system.

The embodiment of the invention discloses a sea wave numerical mode second-order space advection method based on an SCVT unstructured grid, and the method comprises the steps: constructing an SCVT unstructured grid system, building an index mapping relation between a unit and an edge, configuring a wave action quantity at the center of the unit, and configuring an advection speed at the midpoint of the edge of the grid; spatial discretization is carried out on geographic space advection items based on a finite volume method; for flux calculation on each discrete grid edge, a second-order windward format is adopted, an upstream unit is determined according to the flow velocity direction, the wave action quantity and gradient of the upstream unit are obtained, and a high-precision wave action quantity value at the midpoint of the grid edge is obtained through linear reconstruction; and finally, combining spectrum space advection and a source convergence item to finish time integration and sea wave forecasting. According to the method, sea wave advection calculation of the second-order windward format is achieved in the SCVT grid, the precision of sea wave simulation is remarkably improved, numerical dissipation is effectively restrained, meanwhile, variable-resolution grid configuration is supported, and the calculation efficiency is improved on the premise that the simulation precision of key areas such as a near-shore area is guaranteed.

The present invention provides a magnetic anomaly perception method and related device based on improved RLMD and support vector machine, including: acquiring original data s(t), decomposing the original data s(t) by using the RLMD algorithm to obtain a plurality of PF components; calculating the permutation entropy for each of the plurality of PF components respectively, arranging all the permutation entropy values from large to small, screening out the latter n PF components, where n is adaptively determined by the three - quartile, performing linear reconstruction on the PF components to obtain a linearly reconstructed signal, and removing the trend term from the linearly reconstructed signal to obtain the processed static magnetic signal m(t); constructing a corresponding feature vector group [D, K, P]; establishing a training sample data set; inputting the feature vector group corresponding to the signal into the trained support vector machine to determine whether there is a target magnetic anomaly. Applying the technical solution of the present invention can solve the technical problem in the prior art that when performing signal recognition, usually by artificially setting a screening threshold, this method is difficult to fully suppress noise, resulting in low target recognition accuracy.

This invention relates to the field of image processing, specifically to a method and system for color difference detection of automotive injection molded parts based on image processing. First, an industrial camera is controlled to acquire multiple frames of images with different exposure times. The grayscale values ​​are mapped to relative irradiance using the inverse function of the camera's photoelectric response function. Then, a linear validity factor is constructed based on the sensor's photoelectric response characteristics to filter data in the optimal linear response region and suppress noise and saturation. Simultaneously, a chromaticity consistency factor across the exposure image sequence is calculated to eliminate color shift caused by single-channel nonlinear response. Finally, the two factors are combined and weighted to generate a high-fidelity radiance map, which is then converted to the CIELAB color space to calculate the color difference. This invention effectively eliminates the purple fringing effect at highlight edges, ensures linear reconstruction of physical reflection energy, and significantly improves the color difference detection accuracy of complex curvature high-gloss trim panels.

The invention relates to the technical field of railway design, in particular to a railway longitudinal section linear reconstruction method based on a converter model, a medium and equipment. The method comprises the following steps: acquiring original pile point data of a railway line and inputting the original pile point data into a variable slope point section division model to obtain pile point data input into an improved converter model; and inputting the pile point data into the improved converter model, and outputting a complete adjusted line. According to the method, geometric attribution identification of the measuring points in the longitudinal section line shape and data section division of an input improved converter model are considered, and field measuring point data are input and finally directly output a reconstructed longitudinal section line, so that data processing and manual intervention are greatly reduced; and the linear reconstruction adjustment speed of the existing railway longitudinal section is obviously improved. Through standardization processing and deep learning, the learning precision of the model is remarkably improved, and high-precision and high-accuracy reconstruction of the existing railway longitudinal section line shape is realized.

The invention provides a flow field reconstruction method and system based on an orthogonal Hermite polynomial, and the method comprises the following steps: S1, carrying out the standardization processing of original high-dimensional flow field data, and carrying out the initial centralization, and obtaining centralized data; s2, performing principal component analysis on the centralized data to obtain low-dimensional representation and a linear basis matrix of the data, and performing calculation to obtain a linear reconstruction flow field; s3, Hermite orthogonal polynomial characteristics are generated; s4, solving a polynomial basis matrix through an elastic network by using the Hermite polynomial feature matrix obtained in the S3, so that the product of the polynomial basis matrix and the Hermite polynomial feature matrix can approximate a residual error; and S5, reconstructing the flow field by using the mean value of the centralized data, the linear reconstruction flow field and the polynomial basis matrix solved in the step S4. The method not only can reconstruct the flow field with high precision, but also can provide representativeness and interpretability of spatial-temporal characteristics of the flow field through sorting and analysis of the linear mode and the nonlinear mode.

The application discloses an edge data sharing method and system with white-box tracking based on ciphertext policy attribute-based encryption, and relates to the technical field of information security and edge computing. The method comprises the following steps: a certification agency generates public parameters and a global identifier on a bilinear group; each authorized agency selects three random numbers to issue a public key; the authorized agency embeds the global identifier value and a hash element into an attribute key to generate an outsourcing label and issues the outsourcing label to a user; a data owner generates outsourcing ciphertext by using an access matrix and linear secret sharing, and stores the outsourcing ciphertext in the cloud through an edge server; the user takes local random numbers and inverse elements thereof, performs key component operation with the inverse elements as exponents, combines an identity and a tracking label to obtain a conversion key, and submits the conversion key to the edge server; the edge server performs bilinear pairing and linear reconstruction to generate intermediate ciphertext and encapsulated components and returns the intermediate ciphertext and the encapsulated components to the user; and the user performs exponent operation on the intermediate ciphertext with the local random numbers to restore a session key and plaintext.

The application belongs to the technical field of image processing. A kind of image data dimension reduction method, system and computer equipment are provided, and the nearest neighbor image sample is found based on Euclidean distance for each image sample in image sample set to establish the nearest neighbor connection graph;The shortest path between two image samples is calculated on the nearest neighbor connection graph to determine the spatial distance matrix;According to the spatial distance matrix, the inner product matrix after the dimension reduction of image sample set is determined;Eigenvalue decomposition is carried out to the inner product matrix, and the largest set number of eigenvalues selected from the eigenvalues obtained from decomposition form a diagonal matrix, the characteristic vector matrix is determined according to the diagonal matrix, and the adjusted inner product matrix is determined according to the diagonal matrix and the characteristic vector matrix;According to the adjusted inner product matrix, the low-dimensional coordinate of each image sample after dimension reduction is determined.The application not only retains the linear reconstruction relationship of image sample in local neighborhood, but also can retain the global geometric structure of high-dimensional manifold.

The application discloses an image compression sensing reconstruction method based on a residual self-encoding network enhanced by a Transform, and is characterized in that the method comprises the following steps: 1) performing initial linear reconstruction on image observation values y; 2) designing and adopting a residual self-encoding network based on a Transform for feature enhancement to perform deep reconstruction; and 3) performing network training based on a global-local joint loss function. The method can better capture local and global features, effectively enhance feature information, and accurately reconstruct an original image.

The invention relates to the field of image processing, in particular to an automobile injection molded part chromatic aberration detection method and system based on image processing, and the method comprises the steps: firstly controlling an industrial camera to collect multiple frames of image sequences with different exposure times, and mapping a gray value into relative irradiance by using an inverse function of a camera photoelectric response function; then, linear validity is constructed based on photoelectric response characteristics of the sensor, so that optimal linear response region data is screened, and noise and saturation are suppressed; and meanwhile, a chromaticity consistency factor of the cross-exposure image sequence is calculated, and color cast caused by single-channel nonlinear response is eliminated. And finally, combined weighted fusion is carried out by combining the two factors to generate a high-fidelity radiance graph, and the high-fidelity radiance graph is converted to a CIELAB space to calculate chromatic aberration. According to the method, the purple edge effect of the highlight edge can be effectively eliminated, linear reconstruction of physical reflection energy is ensured, and the chromatic aberration detection precision of the highlight decorative plate with the complex curvature is remarkably improved.

This invention discloses a precipitation forecast correction method combining EOF projection and U-Net network. Based on historical observation data, it extracts the main spatial modes of the precipitation anomaly field and then projects and reconstructs the model's forecast anomaly field to obtain a physically meaningful spatial structure. By introducing observed climatological states to replace model climatological states, it effectively eliminates the model's systematic bias. For residual terms that cannot be explained during the reconstruction process, U-Net is used for modeling and prediction to capture complex nonlinear error components in the model. Finally, the linear reconstruction result is superimposed with the residual terms predicted by deep learning to form the final precipitation forecast correction result. This invention constructs a correction framework that combines physical and data-driven approaches. While retaining the physical interpretability of the traditional EOF method, it introduces a deep learning model to model the residual terms, thereby achieving dual capture and correction of linear and nonlinear errors in model forecasts.

The application discloses a kind of feature point projection transformation guide linear reconstruction asymmetric lens distortion compensation method.The application is by minimum reference grid traversal the coordinate matrix of the world coordinate of the feature point of calibration image after projection transformation, the projection transformation comprehensive error of all minimum reference grid is calculated, and projection transformation comprehensive error is obtained by the weighted calculation of straight line constraint, cross ratio constraint and parallel line constraint;It is obtained after the secondary screening that the reference grid area of projection transformation comprehensive error minimum.Then, with the minimum comprehensive error as the target, the best reference grid is optimized;Finally, the optimized feature point coordinates and corresponding world coordinates are solved, to obtain homography transformation matrix, and then the coordinates of the entire calibration image feature point projection transformation are linearly reconstructed, and the parameter optimization of lens asymmetric distortion model is carried out with the coordinates before reconstruction, so that high-precision lens asymmetric distortion model can be obtained, and lens asymmetric distortion is compensated and corrected.

The present invention discloses a noise reduction method, system and device based on adaptive cascaded empirical mode decomposition, which relates to the field of optical fiber communication. The method mainly includes determining the linear reconstruction coefficient of each signal component based on the signal components obtained through empirical mode decomposition and the adaptive multi-segment linear reconstruction coefficient function, thereby obtaining an initial noise reduction signal; determining the parameter update amount of the adaptive multi-segment linear reconstruction coefficient function based on the error value of the initial noise reduction signal; determining the linear reconstruction coefficient of each phase component based on the initial noise reduction signal and the adaptive phase linear reconstruction coefficient function, thereby obtaining complex-valued signals under different amplitude numbers; determining the parameter update amount of the adaptive phase linear reconstruction coefficient function based on the error of each complex-valued signal; when all parameter update amounts are less than a set threshold, combining and outputting the complex-valued signals under different amplitude numbers, otherwise returning to the initial step. The present invention can solve the noise problem faced by optical transmission signals at the receiving end.

The application belongs to the technical field of signal processing and network data analysis, and discloses a graph signal processing method and system based on non-uniform sampling, which comprises the following steps: constructing a graph model and calculating a graph Laplacian matrix; selecting a non-uniform sampling strategy such as periodic sampling, random sampling, local dense sampling, spectral domain pseudo-random sampling, adaptive sampling or spatial correlation sampling according to an application scenario to obtain an observation signal; using linear reconstruction, iterative projection or learning type reconstruction to recover the graph signal; and jointly optimizing the sampling and reconstruction process through greedy adaptive selection or reinforcement learning. The application also discloses a corresponding graph signal processing system. The application can adapt to various non-uniform sampling conditions, realize efficient and stable graph signal reconstruction in combination with graph structure characteristics, and is suitable for complex network environments such as social networks and sensor networks.