401 results about "Basis function" patented technology

An anomaly detection system may receive uniformly spaced digital data from a plurality of sensors connected to the monitored system and physical model parameters specific to components of the monitored system. An orthogonal function processor generates an initial set of orthogonal kernel basis functions to process the digital data to detect signature anomalies. An adaptive analysis is performed based on the number of feature components and the width of each feature component. When the number of feature components is greater than the basis functions, additional basis functions are used for reprocessing. When the feature width is less than the basis function, the resolution of the basis function is increased and reprocessing of digital data is performed. Based on the adaptive analysis of each feature component, signature anomalies are identified and displayed.

Methods and systems are disclosed for manipulating quantum data in a real quantum basis. The method comprises formulating one or more quantum circuits including an amplitude-based feature map comprising gate operations for encoding one or more input features in a Hilbert space of quantum elements of a quantum register. The gate operations are configured to encode the one or more classical input features in amplitudes of a set of orthogonal basis states of the quantum register, wherein the amplitudes are based on one or more basis functions. This also includes a method for executing a transform associated with the feature map on a quantum processor comprising a quantum register. The method comprises preparing the quantum register in a first state representing a computational basis state; formulating a transform circuit configured to map a computational basis state to a feature- map-associated basis state; and applying the transform circuit to the quantum register.

The invention discloses a high-precision granary volume calculation method and system, and relates to the technical field of laser radar technology and point cloud processing, and the method comprises the following steps: data collection; laser radar equipment is used for carrying out omnibearing scanning on a target object, and a laser point cloud data set is obtained; preprocessing the data; performing point cloud segmentation; object recognition; point cloud precision compensation: performing precision compensation on the point cloud data, the precision compensation including error compensation of laser radar precision along with distance and temperature changes; volume calculation: performing volume calculation on the recognized regular object by adopting a volume calculation formula; for the recognized irregular object, a three-dimensional grid model of the object is created through a three-dimensional model reconstruction algorithm, the grid model is adjusted, and then the volume of the grid model is calculated; according to the method, optimization is carried out from the two aspects of radar distance and temperature compensation, an error model is established, in the model, primary functions are designed from the two aspects of temperature and distance, and the precision is improved from the two aspects at the same time.

Methods and systems are disclosed for solving a differential problem using a hybrid computer system. The differential problem defines a differential equation, and a target function defines an approximate solution to the differential equation. The target function is defined as a sum of basis functions with parameterized coefficients. The method comprises receiving or determining a quantum circuit defining operations of a parameterized quantum model of the differential equation, and determining the solution to the differential problem based on the parameterized quantum model and, optionally, a loss function associated with the differential problem. The quantum circuit defines operations for encoding primitive elements (e.g., terms such as f(x), f(x) / dx, g(x), etc.) of the differential equation in a plurality of quantum states of quantum elements of the quantum processor. Each quantum state of the plurality of quantum states corresponds to a set of (parametrized or unparametrized) coefficients associated with the basis functions, of which at least one quantum state of the plurality of quantum states corresponds to a set of parametrized coefficients associated with the basis functions. The determination of the solution includes varying the variational parameters to determine a set of optimal parameters. The varying of the variational parameters includes: translating the operations of the quantum circuit into control signals for control and readout of the quantum elements of the quantum register; applying the operations of the quantum circuit to the quantum elements based on the control signals; determining classical measurement data by measuring one or more comparisons, e.g. overlaps, between pairs of quantum states from the plurality of quantum states; computing a loss value based on the loss function and the classical measurement data; and adjusting the variational parameters based on the loss value.

The invention relates to a thermal coupling over-reduced order prediction method and system for a multi-component assembled nonlinear system. The method comprises the following steps: collecting multi-scale physical field data; constructing an intrinsic orthogonal decomposition basis function space of a temperature field and a stress field, and establishing a double-field coupling constraint equation; constructing contact thermal resistance parameterized proxy models of a cylinder contact area, a bolt area and a free deformation area by adopting a domain discrete empirical interpolation method; constructing a parametric intrinsic mode tensor network to obtain a decline model which is used for realizing real-time reconstruction of a mode basis function through acquired tensor slices in an online stage; performing dynamic inversion based on a modal basis function reconstruction result, and outputting a predicted transient displacement field, a predicted temperature gradient field and a predicted contact stress field; and obtaining real-time parameters, and calculating a residual error with a corresponding prediction result so as to dynamically update the primary function and interpolation point distribution. Compared with the prior art, the real-time prediction of the transient thermal coupling of the multi-component contact system is realized on the premise of ensuring the precision.

The invention relates to the technical field of dynamic weighing, and discloses an intelligent load prediction and adjustment method in a dynamic weighing system, which comprises the following steps of: separating a weighing signal into a space domain static load characteristic (pressure point distribution sparsity and gravity center geometric vector) and a time domain dynamic inertial interference component (acceleration coupling noise) through space-time double-domain decoupling; the frequency spectrum aliasing problem of a traditional filtering algorithm is eliminated from a signal source; the future interference trend is predicted based on the real-time motion state of the carrier, a feed-forward compensation signal is generated through a pre-training dynamic basis function library, and dynamic errors are actively counteracted; in combination with sensor array space consistency detection and lightweight online learning, decoupling model parameters are dynamically corrected. According to the method, space geometric analysis and kinematics feedforward prediction are combined, a traditional signal processing framework is strided out, error source separation and real-time compensation are achieved, and the dynamic weighing precision of transient working conditions such as sudden stop and speed change is remarkably improved.

The invention provides an earth and rockfill dam seepage pressure prediction method based on mechanism and data dual drive, and the method comprises the following steps: generating multi-working-condition seepage pressure data, optimizing a radial basis function neural network through an aurora optimization algorithm, and constructing an efficient proxy model; on the basis of a proxy model prediction result, fitting is carried out by combining measured data, and a seepage pressure prediction mechanism model based on a hysteresis effect function is established; with the predicted value of the mechanism model as a label, supervising the training model to learn the physical law of the seepage field; and freezing the physical feature extraction layer in the trained model, and finely adjusting the time sequence modeling layer by using actually measured data to realize approximation of an actually measured value. According to the method, mechanism driving and data driving are organically combined, the interpretability of a physical rule is reserved, complex factors which are not considered by a mechanism model are complemented by utilizing actually measured data, and the generalization ability of a deep learning model under extreme working conditions is remarkably improved while the prediction precision of the model is ensured.

The invention provides a traffic flow spatio-temporal feature adaptive extraction method based on a dynamic Kolmogorov-Arnold network, and aims to solve the defects of a traditional model in the aspects of dynamic spatio-temporal modeling, structural adaptability and feature expression efficiency. According to the method, a network structure is dynamically adjusted through differential topology search and a parameterized primary function library, a double-flow coupling architecture is designed to extract space and time dependent features respectively, and adaptive fusion of spatial and temporal features is realized by using a gating mechanism. The method comprises the following specific steps: performing space-time normalization and graph structure coding to generate a node feature matrix; extracting multi-scale spatial-temporal features in parallel by the dynamic graph convolution KANs and the time sequence convolution KANs; feature alignment and cooperative enhancement are realized based on a local correlation matrix and a bidirectional interactive attention mechanism; and the lightweight KAN decoder is combined with the dynamic basis function library to output a prediction result. Experiments show that the MAE is reduced to 1.78 vehicles per minute and the reasoning speed is improved by 1.8 times in a traffic accident emergency scene on a PeMS08 data set, and the method is suitable for an intelligent traffic management and vehicle-road cooperation system.

The invention relates to the field of remote sensing image processing, and provides a remote sensing image terrain radiation correction method, device and equipment, and the method comprises the steps: obtaining a to-be-corrected remote sensing image, calculating the terrain parameter of each pixel, dividing the to-be-corrected remote sensing image into a shadow region and a non-shadow region, and determining the direction weighting function of each pixel; calculating a topographic modulation spherical harmonic primary function corresponding to each pixel; training samples are selected in the shadow area and the non-shadow area respectively, a linear model is constructed, and spherical harmonic illumination coefficients representing overall three-dimensional illumination distribution of the shadow area and the non-shadow area are solved; and based on the topographic modulation spherical harmonic primary function, combining the spherical harmonic illumination coefficient corresponding to the area to which each pixel belongs, reconstructing the average incident radiance of each pixel, and then performing inversion to obtain the real reflectivity of the earth surface. According to the method, the problem of inaccurate radiation distortion correction of the remote sensing image under the complex terrain in the prior art is solved, and accurate inversion of the real reflectivity of the earth surface is realized.

Examples described herein generally relate to alerting metric baseline behavior change. The examples include performing at least one of a radial basis function (RBF) kernel procedure and an autoencoding procedure for a time-series data; determining whether one or more change points occur in a seasonal pattern of the time-series data based on at least one of the RBF kernel procedure and the autoencoding procedure; and transmitting, to a user, an alert indicating the one or more change points based on a determination that the one or more change points occur in the seasonal pattern of the time-series data.

The invention belongs to the technical field related to structure optimization design, and discloses a macro-micro dual-scale structure topological optimization method and device capable of controlling the minimum size of a microstructure, and the method comprises the following steps: (1), carrying out the interpolation of an elastic matrix of an actual microstructure in an offline database and the corresponding density through employing radial basis function interpolation, and obtaining an elastic matrix of the actual microstructure; constructing a mapping model; (2) a flexibility minimization optimization model is constructed, design variables of the optimization model are cutting heights and topological variables of the two virtual microstructures in each unit, and constraint functions are volume fractions and topological variable constraints of the overall structure; (3) respectively calculating the sensitivity of the target function and the constraint function to the design variable, and further updating the design variable; (4) when the updated flexibility error of the dual-scale structure meets an optimization termination condition, completing optimization and obtaining the dual-scale structure meeting the requirement; otherwise, turning to the step (3). According to the method, the problems of low calculation efficiency and poor connectivity are solved.

The invention discloses a parameter optimization method for a rotary inertia double-tuned mass damper and a vibration reduction system. The parameter optimization method comprises the steps that S1, a main structure motion equation comprising the rotary inertia double-tuned mass damper is constructed; s2, selecting combined variables from a design variable space of the rotary inertia double-tuned mass damper, adopting a genetic simulated annealing algorithm for each combined variable, and optimizing parameters by taking minimization of the maximum acceleration frequency response of a main structure as an objective to obtain an optimal parameter data set; s3, cleaning an optimal parameter data set based on an IQR criterion; s4, constructing a correction factor, and establishing a parameter correction factor nonlinear mapping model through tensor basis function expansion; and S5, multiplying the theoretical closed solution by the parameter correction factor to obtain design parameters of the rotary inertia double-tuned mass damper. Compared with an analytical formula design, the RIDTMD designed by using the corrected formula has the advantages that the vibration reduction efficiency is improved by about 10% at most, and the asymmetric three-peak phenomenon of a core frequency band can be effectively eliminated.

The invention relates to the field of rotating machinery fault diagnosis, and discloses a bearing fault diagnosis method based on a physical perception KAM network, and the method comprises the steps: carrying out the discretization of a bearing vibration signal through a Gabor filter group based on physical prior initialization, and generating modal feature lexical elements with physical frequency band meanings; and inputting the lexical elements into a PC-KAM backbone network, calculating a hidden state vector by using a state space model branch, and dynamically adjusting the position of a primary function node of a Kolmogov-Arnod network branch to realize collaborative dynamic feature extraction. In the training stage, an orthogonal subspace constraint and physical perception low-rank adaptation fine tuning mechanism is introduced. And finally, searching a historical fault case, performing multi-modal fusion on the historical fault case and the deep feature sequence, mapping a fusion representation into a soft prompt, and inputting the soft prompt into a large language model to generate a diagnosis report. According to the method, the problems of poor physical interpretability of characteristics and few-sample diagnosis under variable working conditions are effectively solved, and the generalization and decision-making ability of a diagnosis system are improved.

The invention belongs to an electromagnetic simulation technology, and provides an integral equation grid adaptive encryption method based on non-conformal grid current discontinuity estimation, which is used for efficiently obtaining a high-precision simulation result. The method comprises the following steps: firstly, using a conformal or non-conformal triangular mesh discrete model surface, defining a MultiBranch-RWG primary function on a discrete mesh unit, establishing an electric field integral equation, and solving to obtain surface current distribution; then, according to the surface current distribution, a current discontinuity error estimation method is utilized to calculate and obtain a current discontinuity error of each grid unit; and finally, traversing current discontinuity errors of all grid units, when the current discontinuity error of any grid unit is greater than a preset threshold value, executing adaptive encryption, and executing iteration by taking the adaptively encrypted grid as input. Otherwise, outputting the grid information. According to the method, higher precision can be realized with lower calculation cost, grid generation is more flexible, and the method is more suitable for complex targets.

The invention belongs to the field of automatic control, and particularly relates to a nonlinear system dynamic gain global trajectory tracking control method based on a self-adaptive neural network observer, which comprises the following steps: converting an uncertain nonlinear system model with unknown interference according to the requirement of output feedback control to obtain a first nonlinear system model; enabling an unknown nonlinear continuous function in the first nonlinear system model to be expressed as a function only containing an output signal and other system state estimation values; performing approximate approximation on an unknown nonlinear continuous function in the first nonlinear system model by using the first radial basis function neural network vector to obtain a second nonlinear system model; designing a second radial basis function neural network vector to construct a dynamic gain state observer of the second nonlinear system model, and obtaining an estimated value of a system state and an estimated value of an unknown nonlinear continuous function in the model; according to an inversion control method, defining a dynamic system tracking error index containing an intermediate virtual control signal, designing a Lyapunov function, and obtaining a change rate of a dynamic gain, a weight adaptive update rate of a second radial basis function neural network vector, and a control rate of the intermediate virtual control signal and a system input signal; different from most existing nonlinear system control methods, the method combines a novel dynamic gain neural network observer with an inversion controller with dynamic gain, and provides a robust global trajectory tracking control solution for a dynamic system operating under uncertain conditions.

The invention discloses a heterogeneous parallel fast direct solution method for accelerating the solution of complex electromagnetic problems, comprising: an MPI process on a CPU side reads a model file and model parameters after three-dimensional geometric modeling of a target; all MPI processes are evenly divided into two groups, all basis functions obtained are divided into M groups, and M×M unit block matrices are generated by using the grouped basis functions to merge and form a block matrix Z; each MPI process obtains basis function information contained in the unit block matrix required by itself according to an acquisition rule related to the MPI process group; all MPI processes complete the LU decomposition process of the block matrix Z in a heterogeneous parallel decomposition method jointly used by a CPU side and a GPU side based on the basis function information obtained by themselves; a current coefficient matrix is ​​solved by back-substitution of a matrix equation group according to the decomposition result of the obtained block matrix Z; and the three-dimensional geometric model of the target is calculated according to the current coefficient matrix to obtain near-field data and far-field data.

The invention discloses a deformable electron beam lithography machine beam spot size calculation method and system, and the method comprises the steps: S1, employing a hyperbolic cosine function as a primary function according to a relation curve between the current intensity and the beam spot position, and constructing a composite function model through translation and superposition; s2, assuming that the observation value is a noise version of the composite function model, and establishing a likelihood function; a logarithm likelihood function is maximized and converted into a minimum residual sum of squares, partial derivatives of the minimum residual sum of squares about all parameters are solved, and a gradient equation set is obtained; s3, calculating the measurement signal, and extracting key feature points in the curve; calculating an initial parameter value according to the position of the feature point: taking the initial parameter as input, and iteratively solving a gradient equation set to obtain an exact solution of each parameter; and S4, calculating the actual size, the central position and the sharpness of the left and right edges of the electron beam spot according to the exact solutions of the parameters. The method has the advantages of high calculation precision and the like.

The invention discloses a trend Kriging-based ASF estimation method for improving the positioning and navigation precision of a Rowland system, and belongs to the field of positioning and navigation.The method is based on a Kriging model, an effective primary function is selected based on limited real ASF data to construct an optimal trend function, and the method comprises two nesting tasks; the optimal basis function is selected, and the optimal relevant parameters are searched; in order to overcome the challenge that the nesting property of the two tasks is easy to cause local optimum, the two tasks are integrated into an integral optimization framework; under the framework, the optimal solutions of the primary function and the residual related parameters are searched at the same time by adopting an NDE algorithm with high global search capability. In addition, residual covariance minimization is used as an optimization target of the two tasks, and geospatial correlation of sampling data is guaranteed.

The invention discloses a flight controller and method fusing PID control and a neural network, and a storage medium, and the method comprises the steps: obtaining the real-time online flight state data of an aircraft, inputting the real-time online flight state data into a basis function, and obtaining the basis function data irrelevant to a wind field, the basis function being a pre-trained aerodynamic neural network irrelevant to the wind field; obtaining a linear coefficient related to the wind field through real-time online self-adaptive adjustment; multiplying the basis function data irrelevant to the wind field obtained in real time and on line by the linear coefficient relevant to the wind field to obtain residual force; converting the residual force into the power of the aircraft and feeding back the power to a PID controller to realize the control of the aircraft; according to the method, various interference factors are decoupled into non-real-time and real-time factors, control over various uncertain nonlinear complex processes can be effectively achieved, the robustness and fault tolerance of a flight control system are improved, and better control performance is achieved.

Methods and systems are disclosed for solving a differential problem using a hybrid computer system. The differential problem defines a differential equation, and a target function defines an approximate solution to the differential equation. The target function is defined as a sum of basis functions with parameterized coefficients. The method comprises receiving or determining a quantum circuit defining operations of a parameterized quantum model of the differential equation, and determining the solution to the differential problem based on the parameterized quantum model and, optionally, a loss function associated with the differential problem. The quantum circuit defines operations for encoding primitive elements (e.g., terms such as f(x), f(x) / dx, g(x), etc.) of the differential equation in a plurality of quantum states of quantum elements of the quantum processor. Each quantum state of the plurality of quantum states corresponds to a set of (parametrized or unparametrized) coefficients associated with the basis functions, of which at least one quantum state of the plurality of quantum states corresponds to a set of parametrized coefficients associated with the basis functions. The determination of the solution includes varying the variational parameters to determine a set of optimal parameters. The varying of the variational parameters includes: translating the operations of the quantum circuit into control signals for control and readout of the quantum elements of the quantum register; applying the operations of the quantum circuit to the quantum elements based on the control signals; determining classical measurement data by measuring one or more comparisons, e.g. overlaps, between pairs of quantum states from the plurality of quantum states; computing a loss value based on the loss function and the classical measurement data; and adjusting the variational parameters based on the loss value.

The invention relates to the technical field of water supply pipe networks, and discloses a multi-pump-room collaborative intelligent water supply frequency conversion regulation and control method, which comprises the following steps: initializing sensor calibration and data acquisition, generating a standardized data packet by using Kalman filtering, constructing a differential algebraic equation model, and online updating a pipe network topological characteristic matrix in combination with a mixed basis function observer. Layered optimization control and frequency conversion cooperative regulation and control are driven, 30-100ms-level real-time solution of a differential equation is realized by means of a hardware acceleration module, a three-dimensional pressure cloud picture is finally generated, and early warning is triggered based on a current harmonic threshold value. The transient fault capturing capability is enhanced by dynamically adjusting the data acquisition frequency, and the problem of signal distortion caused by traditional fixed sampling is solved; resource allocation is optimized and calculated in combination with a pipe network topology model dimension reduction technology, and calculation power waste in large-scale monitoring is reduced; and dynamic reconstruction of the model is driven by abnormal data to realize system self-adaption, so that manual parameter adjustment deviation is avoided, and the prediction precision and the diagnosis reliability are remarkably improved.

The invention discloses a fracture pressure prediction and internal friction angle joint inversion method for a heterogeneous stratum. The invention belongs to the technical field of petroleum engineering and artificial intelligence cross application, well depth is used as an independent variable, the minimum / maximum horizontal stress, pore pressure and cohesion are combined to construct well depth mixed input, only multi-scale periodic basis function transformation is carried out on the well depth, and a linear well depth channel is reserved; establishing a rupture pressure depth mapping model, introducing an augmented Lagrange physical constraint containing dual variables and a dynamic threshold, adaptively adjusting a physical weight according to a physical residual error and a threshold difference value, realizing dynamic balance of a rupture criterion and data fitting, and searching and optimizing a model topology by using a multi-target network structure; under a limited sample, a fracture pressure profile and a physically reasonable internal friction angle can be obtained at the same time, and the prediction precision and the physical consistency are improved.

The invention discloses a split type three-box girder nonlinear vortex vibration steady-state amplitude intelligent prediction method, belongs to the field of bridge vortex-induced vibration control, and aims to solve the problem of low accuracy of three-box girder vortex vibration nonlinear modeling and amplitude prediction. The method comprises the following steps: acquiring displacement time sequence data through a reduced scale model wind tunnel test, preprocessing to obtain displacement, speed and acceleration dimensionless data, and dividing a data set; constructing a candidate function library containing high-order polynomial terms of the primary function; performing nonlinear system sparse recognition by taking acceleration data as a target item and combining an improved algorithm to obtain a preliminary feature set; a final feature set is obtained through energy-statistics-self-adaption three-stage screening; and finally, constructing a control equation to predict the steady-state amplitude. The method can accurately predict amplitude, coincide test and real bridge observation results, and is less in computing resource occupation and high in numerical value precision.

The invention belongs to an electromagnetic simulation technology, and particularly provides an electromagnetic scattering simulation method based on a skeleton decomposition precondition, which is used for completing electromagnetic scattering simulation calculation of an electrically large complex target, improving the ill-conditioned problem of a complex target generation impedance matrix while ensuring the simulation calculation precision, and improving the convergence and reliability of an iteration method. According to the method, firstly, a bone element primary function is selected by using an interpolation decomposition technology and a proxy surface method, and an approximate inverse matrix of an impedance matrix is obtained by using a skeleton decomposition method; then, the approximate inverse matrix is adopted as a pre-processor, a current coefficient is obtained through a Krylov subspace iteration method, and finally electromagnetic scattering of the metal target is obtained through calculation according to the current coefficient. The invention creatively provides a brand-new pre-processor, and compared with the traditional pre-processor, the pre-processor can further improve the matrix behavior, greatly reduce the iteration time and improve the convergence.

The invention relates to the technical field of information technology, in particular to a self-adaptive control method based on reinforcement learning and a radial basis function neural network, which comprises the following steps: establishing a state equation of a nonlinear system with unknown time-varying uncertainty, and defining a system state variable and a control target; under a reinforcement learning framework, a control strategy and a value function are initialized, initial network parameters are set, and the upper definite bound of uncertainty is preliminarily estimated; estimating the uncertainty of the system in real time by using the radial basis function neural network, and approaching the upper definite bound of the uncertainty by adjusting the parameters of the radial basis function neural network; based on the uncertainty of estimation, performing iterative updating on the control strategy, and in each iteration, optimizing the control strategy by using a reinforcement learning algorithm; and the control strategy is adjusted in real time, control strategy parameters are optimized based on the latest uncertainty estimation value, and a final stable control strategy is obtained through convergence analysis. According to the method, the problem that the upper bound of uncertainty needs to be known in advance is effectively solved, the robustness and the control precision of the system are improved, the dynamic adaptive capacity is high, the control strategy design is simplified, and the stability and the response efficiency of the system are enhanced.

The invention provides a temperature distribution inversion method based on a multi-vertex grid and a high-robustness error functional, and the method comprises the steps: 1, obtaining a temperature gradient change rule of a measured structure under a typical thermal load condition, and determining a three-dimensional multi-vertex grid division scheme and a temperature sensor layout form of the measured structure; step 2, establishing an m vertex grid basis function model, and constructing a conversion matrix between a regular grid and an irregular grid; 3, establishing a high-robustness error functional between the theoretical temperature of the centroid position of each surface of the m-vertex grid and the actually measured temperature of the sensor; 4, a temperature matrix equation containing a temperature coefficient matrix and a temperature amplitude matrix is constructed, temperature vectors of all grid vertexes are solved, the m-vertex grid basis function model is combined, three-dimensional temperature distribution in the structure space is inverted, and according to the method, only few sensors need to be adopted, the global temperature response of the structure can be obtained, and the inversion precision is high; the method is simple, convenient and high in real-time performance.

The invention belongs to the field of magnetic target body positioning, and relates to a measuring line vertical gradient-based magnetic multi-target positioning and magnetic moment inversion method, which comprises the following steps of: obtaining a unit orthogonal primary function and a primary function coefficient corresponding to each component of magnetic anomaly gradient data vertical to a measuring line, and calculating an energy curved surface of the magnetic anomaly gradient data vertical to the measuring line; detecting a local energy peak value of the energy curved surface, obtaining an energy peak value ratio sequence, obtaining a target number and horizontal position estimation corresponding to the target according to an energy sudden change value in the energy ratio sequence, and calculating a target depth corresponding to a measuring line vertical magnetic anomaly gradient energy extreme value by using a dichotomy method, and constructing a linear equation set about the target magnetic moment by using the measuring line vertical magnetic anomaly gradient data and the unit orthogonal basis function, and obtaining target magnetic moment parameter estimation through a least square method. According to the invention, the anisotropy difference of the estimation precision of the horizontal position of the target is effectively reduced, and the problem of target detection omission caused by large difference between the target burial depth and the magnetic moment module value in multi-target positioning is improved.

The invention relates to the technical field of circuit breaker on-off, in particular to a short-circuit current zero crossing point prediction method based on a least square method, and aims to solve the problems that short-circuit current waveforms cannot be decomposed in a self-adaptive mode, model adaptability is reduced, accurate parameter estimation and rapid convergence cannot be achieved, and short-circuit current zero crossing points cannot be predicted in the prior art. The waveform description capability and the zero crossing point prediction reliability are reduced; the EMD algorithm is adopted to adaptively decompose the short-circuit current waveform, a preset primary function is avoided, the model adaptability is enhanced, sine function superposition is used for high frequency, a polynomial model is used for low frequency, wavelet function linear combination is used for intermediate frequency, the least square method and the Levenberg-Marquardt algorithm are combined, accurate parameter estimation and rapid convergence are achieved, and the final model is high in fitting precision and high in precision. Each IMF component has clear physical significance, and the waveform description capability and the zero crossing point prediction reliability are significantly improved.

The invention discloses a new energy power prediction method and system based on a particle swarm optimization neural network. The method comprises the steps of collecting new energy power generation historical power data and historical meteorological data of corresponding time; training a wavelet neural network based on a multi-scale Gaussian basis function according to the collected data, and optimizing network parameters by adopting a particle swarm algorithm to obtain a new energy power prediction model; and acquiring real-time meteorological data, and inputting the real-time meteorological data into the new energy power prediction model to obtain a new energy power prediction value. According to the method, the Gaussian wavelet function is improved, the multi-scale analysis capability of the Gaussian wavelet function is enhanced, the improved wavelet function is organically combined with the neural network, and the wavelet neural network is optimized by using the particle swarm optimization, so that the convergence speed and the power prediction precision can be remarkably increased.

The invention provides a frequency dispersion curve calculation method based on a decoupling type Legendre-Gauss-Labatto spectral element method, and belongs to the technical field of discrete solution of guided wave propagation in a thin plate, and the method comprises the following steps: S1, constructing a wave equation which comprises a Cartesian space coordinate system based on an orthotropic plate, and combining a constitutive equation, a geometrical relationship under the coordinate system and guided wave displacement to obtain a wave dispersion curve; substituting into a control equation when the volume force is zero to obtain an improved three-dimensional elastic wave equation; s2, a differential matrix is constructed, wherein discretization and decoupling are carried out on a wave equation on the basis of a Lagrange interpolation basis function of a Legendre-Gauss-Labatto node; s3, explicitly embedding the stress free boundary condition into the solving process through matrix line transformation, and solving to obtain a frequency dispersion curve, the derivation process is simplified, the boundary and the interior of the material are decoupled, and the physical significance is clearer. And meanwhile, the high-precision advantage of the spectrum method is maintained, and the calculation rate is remarkably improved.