295 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.

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 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 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 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 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 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 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.

The invention discloses an electromagnetic simulation method and system based on a variational finite element method. A calculation area is set; defining two auxiliary vector fields on an artificial truncation boundary; performing projection on the two auxiliary vector fields in each far-field direction by using a finite element basis function, and calculating an expansion coefficient in each far-field direction; constructing a finite element equation stiffness matrix and a boundary matrix; and according to the near-field electric field, the stiffness matrix, the boundary matrix and the expansion coefficients of the two auxiliary vector fields in the current far-field direction, calculating an electromagnetic far-field component in each far-field direction. By introducing a variational finite element method, precision loss caused by geometric boundary complexity or medium heterogeneity in a traditional integration method is eliminated. A phase factor is directly embedded into the auxiliary vector field, electromagnetic wave propagation characteristics are accurately represented, calculation reliability is ensured, and electromagnetic simulation requirements of a large-scale simulation domain can be met by improving the conversion process from a near field to a far field.

The invention discloses a shear wave velocity prediction method, device and equipment and a readable storage medium, and is applied to the technical field of geophysics, and the method comprises the steps: constructing a shear wave velocity prediction model in advance based on a KAN network and a sequence displacer; an activation function of the shear wave velocity prediction model is a B-spline primary function; obtaining logging data of the target area; and inputting the logging data into the shear wave velocity prediction model to obtain a prediction result. The shear wave velocity prediction model based on the KAN network has robustness and accuracy, the model is combined with a sequence displacer architecture to be fused with the KAN network to perform feature extraction, efficient modeling of logging data is realized through a dynamic weight fusion mechanism and a learnable B-spline basis function, and the prediction accuracy is improved. The model can be used in various large oil fields in a large scale, deals with various complex geological conditions and accurately predicts the shear wave velocity, so that the exploration and development risk is reduced, and the benefit development of each large oil field is guaranteed.

The invention aims to provide an electromagnetic parameter extraction method based on a non-conformal partial element equivalent circuit method, and belongs to the technical field of computational electromagnetism. According to the method, the multi-scale electromagnetic structure is divided into different areas according to the size difference, each area is subdivided by adopting a triangular prism grid with the corresponding size, and meanwhile, an h-SPV primary function is defined at the non-conformal junction, so that the continuity of current at the non-conformal junction is ensured. According to the method, in the calculation process of the multi-scale structure problem, a large number of transition grid units at the interfaces of different areas are avoided through non-conformal grid subdivision, and the calculation complexity is reduced while the calculation precision is guaranteed by establishing the current continuity boundary conditions at the interface ports.

This disclosure describes a set of advanced video coding technologies and is particular related to an intra warp prediction of a sample in a video block where the sample is intra predicted using a weighted sum of basis functions of pixel coordinate position of the sample. A set of weighting coefficients for the weighted sum may be predefined or may be derived by an encoder and signaled in a bitstream containing the video block at various coding levels.

The invention discloses application of a high-precision enhanced radial point interpolation meshless calculation method in acoustic problem calculation, and relates to application of a grid calculation method in the field of acoustic problem calculation, firstly, a wave propagation problem domain is dispersed into a series of scatter points, and an interpolation format of a general scalar field is constructed based on a radial basis function; deducing to obtain an interpolation primary function of each scatter point; in order to improve the interpolation precision of the obtained interpolation format, a new node degree of freedom is constructed at each scatter point, and the node degree of freedom is a local numerical value approximation composed of a local enhanced interpolation primary function and a coefficient to be solved. And based on the obtained local numerical approximation, constructing and obtaining a global interpolation format of a general scalar field on the premise of meeting unit decomposition characteristics. And obtaining a final discrete system matrix equation, and solving the matrix equation to obtain the sound pressure of any point in the whole acoustic problem computational domain. According to the method, high-order interpolation is realized under the condition of not increasing unit nodes, and numerical calculation errors can be reduced.

Various embodiments of this disclosure relate to generation and reception of signals comprising cyclically shifted basis functions. A device may obtain a set of modulation symbols and modulate a plurality of basis functions based on the set of modulation symbols to generate a signal. The plurality of basis functions may comprise cyclically shifted versions of a basis function and a periodic autocorrelation function of the basis function may comprise an ideal (e.g., perfect) periodic autocorrelation function.

The invention belongs to the technical field of hydraulic engineering digital twinning, and provides an arch dam static response rapid calculation method which comprises the following steps: sampling and simulating based on a finite element model in an offline stage to obtain a displacement field snapshot matrix, extracting a dominant mode through intrinsic orthogonal decomposition to construct a reduced-order subspace, and calculating a mode coefficient; and training a radial basis function neural network to establish a nonlinear mapping relation by taking the working condition parameters as input and the modal coefficient as output, and finally, inputting the target working condition parameters into the trained network to predict the modal coefficient in an online stage, and quickly reconstructing a complete displacement field response through linear combination with the POD modal. The method achieves the quick and accurate prediction of the static physical field of the arch dam, and remarkably improves the calculation efficiency of the displacement field of the arch dam.

The invention belongs to the field of power equipment simulation calculation, and discloses a two-dimensional power equipment high-fidelity efficient transient flow field solving method under rectangular coordinates and a related device, and the method comprises the steps: solving an established power equipment two-dimensional model through a flow field full-order model, obtaining flow field horizontal velocity components and vertical velocity components of the fluid in the power equipment at multiple moments; forming a sample matrix by the horizontal velocity components and the vertical velocity components of the flow field at the multiple moments; performing singular value decomposition on the sample matrix to obtain a velocity basis function and a velocity spectrum coefficient at an initial moment; calculating velocity spectrum coefficients at different moments by using an established two-dimensional power equipment flow field low-order transient calculation model, the velocity primary function and the velocity spectrum coefficients at the initial moment; and carrying out linear superposition on the velocity basis function and the velocity spectrum coefficients at different moments to obtain an approximate field of the flow field. According to the method, the transient flow field of the power equipment can be efficiently solved on the premise of ensuring the accuracy of a calculation result.

The invention discloses a method and device for compensating the dispensing height through curved surface fitting. The method comprises the steps that three-dimensional coordinate point information of a plurality of discrete sampling points on the surface of a to-be-dispensed target is collected; building a polynomial of a curved surface model for compensating dispensing according to the number of the sampling points; constructing a matrix, and constructing a linear equation set based on the primary function; and the weight coefficient of the linear equation set is solved through a Gaussian elimination method, a fitted quadric surface equation after interpolation calculation is obtained, and the dispensing height is compensated based on the quadric surface equation. According to the method, the polynomial is fitted through the three-dimensional coordinate point information, the linear equation set is constructed, the predicted compensation value is obtained after interpolation calculation is carried out according to the fitting result so as to compensate the dispensing height, the high-precision continuous curved surface model can be constructed by collecting discrete sampling points, real-time interpolation calculation of any coordinate is supported, and the precision of the dispensing height is improved. The method can solve the problem of insufficient fitting precision of dispensing height compensation based on a single plane.

The invention discloses a front subframe structure MaOP method and device based on TPT, a medium and a program product, and the method comprises the steps: (1) constructing a MaOP design model capable of optimizing the weight, modal, strength and rigidity at the same time based on front subframe longitudinal beam and load analysis; (2) generating an elite population based on a diversity criterion and Latin hypercube, obtaining target values of the elite population, establishing a data set, and constructing a radial basis function model; (3) designing co-evolution operation driven by TPT to generate a candidate frame set; (4) an individual potential evaluation criterion is constructed based on the advantage and disadvantage target sets, and an optimal candidate frame is screened; and (5) obtaining each target value of the optimal candidate frame, updating the data set and the radial basis function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, a TPT-driven coevolution mechanism is adopted, radial basis function model prediction is combined, and the multi-performance index optimization process for the front auxiliary frame can be effectively balanced.