103 results about "Hermite functions" patented technology

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 relates to a mobile robot preset performance safety tracking control method with state constraint and input time delay, and belongs to the technical field of mobile robot intelligent control. Aiming at the problems of control singularity, state overrun risk, time delay sensitivity and uncontrollable tracking performance in the prior art, a dynamic modeling method based on a car-like coordinate system is provided, and a kinematics-dynamics hierarchical controller is constructed by combining a constraint safety reference signal generation mechanism and a Hermite interpolation smoothing technology. Error dynamic constraint is realized by designing a preset performance function and a barrier Lyapunov function, an auxiliary signal is adopted to compensate an input time delay effect, and reinforcement learning is fused to optimize an anti-interference strategy. The method effectively eliminates the control singularity, strictly guarantees the state safety boundary, improves the time delay robustness, achieves the high-precision tracking error convergence, and is suitable for the safe and stable control of the mobile robot in a complex environment.

The invention relates to the technical field of fault diagnosis, and discloses a variable speed bearing fault diagnosis method based on Pareto leading edge, and the method comprises the steps: obtaining a basic data set, carrying out the improved calculation order analysis based on segmented cubic Hermite interpolation on the basic data set, and obtaining an angular domain vibration signal; for a domain vibration signal, creating an initial population by using a chaotic mapping initialization method; performing variational mode decomposition based on the angular domain vibration signal, the sampling frequency and the initial population, calculating an envelope spectrum peak factor and an average envelope spectrum kurtosis of each IMF component, and selecting the IMF component corresponding to the weighted highest value as an optimal mode to establish a target function; performing iterative optimization on the target function by using a multi-target grey wolf algorithm to determine an optimal variational mode decomposition parameter; and for the angular domain vibration signal, variational mode decomposition is carried out based on the corresponding optimal variational mode decomposition parameter to obtain an optimal mode, so that iterative training is carried out on the variable-speed bearing fault diagnosis model.

The invention provides a power distribution network voltage out-of-limit probability prediction method considering load fluctuation and photovoltaic output, and relates to the technical field of power distribution network safety. The method comprises the following steps: constructing a time-varying Gumbel-Copula joint distribution function according to a Spearman rank correlation coefficient, obtained by calculation, of a load and photovoltaic output; based on a time-varying Gumbel-Copula joint distribution function, probability distribution of the load and probability distribution of the photovoltaic output are reconstructed through a Hermite polynomial, and parallel probabilistic power flow solving is carried out; and calculating the voltage out-of-limit probability of each node according to the result of the parallelization probabilistic power flow solution. According to the method, dynamic coupling characteristics of different time periods are considered, a time-varying Gumbel-Copula joint distribution function is introduced, probability distribution of loads and probability distribution of photovoltaic output are reconstructed through a Hermite polynomial, the influence of intermittent superposition of load fluctuation and photovoltaic output can be overcome, and real-time prediction of the voltage out-of-limit probability can be accurately completed in extreme weather.

The invention discloses a non-linear constraint coupling driven implicit modeling optimization method and system and a memory. The method comprises the steps of firstly obtaining nonlinear constraint data of a target geological interface, then generating initial implicit function representation through a Hermite radial basis function based on an initial control point of the target geological interface, and then obtaining a nonlinear coupling driving optimization objective function about implicit function representation of the target geological interface according to the data type of nonlinear constraint, and optimizing through an implicit gradient flow algorithm to obtain a geological interface reconstruction model and a global optimization target geological interface model. The invention further provides a computer readable memory, the memory contains a computer program for realizing the implicit modeling optimization method for nonlinear constraint coupling driving, the computer program can be read and executed, and the computer program, a programmable logic controller and a processor form an optimization system. According to the system, nonlinear constraint data of a large number of geological interfaces are fully coupled, an implicit modeling framework is constrained, and therefore high-precision modeling of the three-dimensional geological interfaces is achieved.

The invention discloses a complex equipment fault diagnosis method based on a polynomial improved graph convolutional network. Comprising the following steps: (1) collecting vibration signals of complex equipment operation; (2) constructing a multi-target fitness function by using a cross correlation coefficient, and optimizing the selection of variational mode decomposition parameters in combination with a particle swarm algorithm; (3) carrying out variational mode decomposition on the vibration signal according to the optimized parameters to obtain a plurality of mode components, and reconstructing to obtain a de-noised signal; (4) dividing the reconstructed signal to carry out graph construction; and (5) inputting the graph structure into the graph convolution model after Hermitian polynomial improvement to complete fault diagnosis. The method is used for fault diagnosis of complex equipment, denoising is carried out on the signals through modal decomposition, the graph convolution model is improved, the diagnosis accuracy is improved, and the method is suitable for the technical field of fault diagnosis of the complex equipment.

The invention discloses a method for generating a hollow array light source. The method comprises the following steps: selecting a vortex phase which has Gaussian intensity distribution and carries topological charges in an initial light field; introducing a Hermite-Gaussian correlation function, and constructing a cross spectral density function of the Hermite-Gaussian correlation vortex light source based on a vortex phase having Gaussian intensity distribution and carrying a topological charge and the Hermite-Gaussian correlation function; an ABCD optical system is introduced, a transmission model is constructed based on a cross spectral density function of Hermitian associated vortex and the ABCD optical system, parameters in the transmission model are adjusted, a plurality of hollow array light beams are formed, and then a hollow array light source is formed through the plurality of hollow array light beams; through combined regulation and control of the Hermitian correlation structure and the vortex topological charge, effective control of array light intensity distribution can be realized, preparation of the hollow array light beam is realized, the array light source is formed by adjusting the array light beam, and the method is simple to operate and easy to realize.

The invention discloses a Riemannian manifold-based space-time joint anti-interference direct positioning method. The method comprises the following steps of: acquiring signals emitted by an interference source and a radiation source by using a plurality of distributed antenna arrays; segmenting a received signal, constructing a sample covariance sequence, and establishing a model covariance matrix at each frequency; determining a Riemannian distance expression between the sample covariance sequences by constructing a Riemannian manifold on the basis of the characteristic that the sample covariance sequences are located on an Ermitt positive definite matrix manifold, and solving a Riemannian mean value of the sample covariance sequences; utilizing Riemannian geometric characteristics of a sample covariance matrix, and taking a Riemannian distance on an HPD matrix manifold as a judgment criterion of fitting accuracy; replacing an Euclidean distance with a Riemannian distance, performing approximate processing on the Riemannian distance by using a logarithm Euclidean distance, and constructing a direct positioning cost function based on the logarithm Euclidean distance; and two-step spectrum peak searching is carried out through the direct positioning power spectrum, and a direct positioning result of the direct positioning cost function is determined.

The present invention discloses a method for constructing a Golomb Costas sequence based on a high-order spread domain, which belongs to the field of wireless communication technology. The method comprises the following steps: determining the order N of the Golomb Costas sequence to be constructed; and determining the order N of the Golomb Costas sequence to be constructed according to N=p. n ‑2, determine p, n, where p is a prime number and n is a positive integer, and explicitly state the extended field GF (p n ); select an irreducible polynomial of degree n with a leading coefficient of 1 on the prime field GF(p) as f(x), and clearly expand the field GF(p n ) in the form of elements, define the extended field GF(p n ) two binary operations on elements in the GF(p n ) primitive elements and their powers, and list the powers of primitive elements; in the extended field GF(p n ) selects two primitive elements; calculates the placement coordinates and completes the construction of the Golomb Costas sequence. The present invention calculates the primitive elements of the extended domain and their powers by calling functions such as get_combins(p,n), modBKY(p,n,BY,BKY,res) and calc(p,n,BKY,res_product), and according to the construction formula of the Golomb Costas sequence and by searching the extended domain GF(p n ) list of powers of primitive elements, quickly calculate the placement coordinates of the Golomb Costas sequence, reduce the workload of manual calculations, and improve the efficiency of constructing high-order Golomb Costas sequences based on extended domains. At the same time, it ensures the correctness of the calculation of the placement coordinates of the Golomb Costas sequence, solving the difficult problem of constructing Golomb Costas sequences based on high-order extended domains.

A method for modeling a wafer profile by a function is provided, in which the function is used to calculate a displacement z in a thickness direction of a wafer and is a sum of multiple functions including a first function g(r), a second function Ar×h(Nθ), and a third function Br×i(M(θ-φ)). The first function g(r) indicates a first-order or higher-order polynomial with a distance r from the center of the wafer as a variable. The second function Ar×h(Nθ) indicates multiplying a sine or cosine function h(Nθ) by a first angle θ with respect to a predetermined position in a circumferential direction of the wafer as a variable and an integer N as a constant with a coefficient A and the distance r.The third function Br×i(M(θ-φ)) indicates multiplying a sine or cosine function i(M(θ-φ)) with the first angle θ as a variable, a second angle φ with respect to the predetermined position as a constant, and an integer M as a constant with a coefficient B and the distance r.

The invention relates to a universal two-phase flow phase field model mesoscopic design method, device and equipment and a storage medium. The method comprises the steps that a two-phase flow control equation based on a phase field model is established; presetting an equilibrium state of the Boltzmann distribution function, calculating a low-order moment, and associating the low-order moment to a macroscopic quantity; performing analysis by using Chapman-Enskog to obtain an integral constraint condition required by a source item; utilizing a Hermite expansion method to obtain an expression of a source item; and outputting a mesoscopic design result. According to the universal two-phase flow phase field model mesoscopic design method provided by the invention, when the phase field model is used for two-phase flow simulation in the future, a mesoscopic format can be quickly obtained even if a new phase field equation is encountered, so that the mesoscopic method can be directly used for simulating the two-phase flow, and mesoscopic design does not need to be carried out again.

The invention discloses a quantum state coding method of a polynomial function and a related device, and belongs to the technical field of quantum computing, and the method comprises the steps: determining the scale of a unitary matrix based on the highest order of the polynomial function, and constructing a target matrix based on a plurality of function values of the polynomial function; wherein the quantum logic gate corresponding to the unitary matrix is used for constructing a quantum circuit of a quantum state of a coding polynomial function, and the line number of the target matrix is the same as that of the unitary matrix; performing singular value decomposition on the target matrix to obtain a plurality of unitary matrixes; and determining a quantum logic gate corresponding to each unitary matrix, and acting each quantum logic gate on the corresponding quantum bit to obtain the quantum state of the polynomial function, thereby realizing coding from the classical data form to the quantum state of the polynomial function.

The invention provides a random point collocation method for solving global sensitivity of an augmented failure probability of a turbine blade structure, and belongs to the technical field of reliability design. Comprising the following steps: constructing an expected density function of an n-dimensional random input variable of a turbine blade structure to obtain an n-dimensional expected density function; calculating an unconditional augmentation failure probability of the turbine blade structure based on the n-dimensional expected density function and the performance function; a Gauss-Hermite integral quadrature node of the n-dimensional expected density function is determined; constructing an expected density function of (n-1)-dimensional random input variables except the i-th dimension of the turbine blade structure to obtain an (n-1)-dimensional expected density function except the i-th dimension; based on the (n-1)-dimensional expected density function and the conditional performance function, the conditional augmentation failure probability of the turbine blade structure corresponding to the Gauss-Hermite integral quadrature node is calculated; and determining the augmented failure probability global sensitivity of the turbine blade structure. According to the invention, the calculation efficiency of the global sensitivity of the augmentation failure probability of the turbine blade structure can be improved.

The application discloses a kind of single-area PI type load frequency control time-delay power system stability analysis method, comprising the following steps: 1) establish single-area PI type load frequency control power system mathematical model;2) considering the influence of time delay on power system, on the basis of power system mathematical model, establish time-delay power system mathematical model;3) construct Lyapunov functional;4) the derivation of Lyapunov functional;5) the integral term of the Lyapunov functional after derivation is estimated using the first lemma, and the stability of the time-delay power system mathematical model is analyzed using a two-parameter quadratic polynomial function and the Schur complement theorem, to obtain a less conservative stability criterion.The application has the advantages of less conservative, lower analysis process complexity, etc.

A method for optimizing a system that includes a set of degrees of freedom (DOF) and a set of constraints on the set of DOF is disclosed. Each constraint is associated with a subset of the set of DOF. The method includes determining a first polynomial function. A set of qubits of a quantum computing system (QCS) is prepared in a first quantum state Subsequent to preparing the set of qubits in the first quantum state, the QCS performs a set of unitary operations on the set of qubits. Subsequent to performing the set of unitary operations on the set of qubits, the QCS generates a first set of observables by performing a first measurement operation on each qubit of the set of qubits. A set of values is determined based on the first set of observables.

The invention discloses a Hermite windward interpolation-based five-order WCNS format construction method, which comprises the following steps of: setting a computational domain according to a hyperbolic conservation law equation to be solved; dividing a computational domain into solution points and flux points in each coordinate direction by adopting a uniform grid distribution mode; dividing a calculation interval into a plurality of units by utilizing flux points at equal intervals, and arranging solving points in the centers of the units; calculating left and right windward values of a flux point based on a Hermite three-point five-order windward interpolation format; calculating a numerical flux at a corresponding flux point by using a numerical flux function; according to the numerical flux, adopting a preset node and half node mixed implicit difference format to obtain an approximate derivative of the flux in each coordinate direction; and carrying out numerical simulation by adopting a time-marching mode to complete the solution of the partial differential equation. An implicit format is adopted, the calculation stability is guaranteed, high-order precision is kept in a smooth area, numerical errors are reduced, and the flow field calculation efficiency is effectively improved.

The invention discloses a reliability analysis method for static irregularity of rail tracks on a road-rail dual-use bridge based on a high-order moment method, including establishing a finite element model of a real bridge structure; constructing a structural function function; generating fitting values ​​of random variables in the structural function function according to measured data; converting the fitting values ​​of random variables to the same standard normal space by Rosenblatt inverse transformation, and obtaining the estimated point values ​​and weight values ​​of the fitting values ​​of random variables by Gauss-Hermite quadrature formula; substituting the estimated point values ​​and weight values ​​of the fitting values ​​of each random variable into the finite element model of the real bridge structure, and taking the mean of other random variables to obtain the univariate structural function function corresponding to each random variable; solving the first four moments according to the fourth-order moment theory, and calculating the structural failure probability and reliability through all the first four moments. The scheme of the invention realizes the quantitative analysis of the reliability of static irregularity of rail tracks on road-rail dual-use bridges based on the high-order moment method.

[Problem] To provide an optical secure communication system with higher safety by carrying a QNSC signal using a coherent Hermite-Gaussian pulse, multiplexing the QNSC signal using coherent Hermite-Gaussian pulses of different orders, and adding a masking effect in a time domain. [Solution] An optical secure communication system for a quantum noise stream cipher (QNSC) signal for masking the phase and / or the amplitude level of an optical signal using quantum noise, wherein carrier light in a transmission unit and locally oscillated light in a reception unit of the QNSC signal comprise a coherent Hermite-Gaussian pulse with an amplitude given by an Hermite-Gaussian function.

The present invention discloses a method and system for implementing sine and cosine functions based on transcendental function acceleration instructions. The method of the present invention includes reducing each element of the incoming vector operand vd to the interval [-π / 4, π / 4] to obtain the corresponding integer vector vql and the floating-point vector vdr within the interval [-π / 4, π / 4]; taking the remainder of the integer vector vql to obtain the integer vector vqln; according to the Taylor series expansion method, using transcendental function acceleration instructions to perform polynomial approximation calculations on the floating-point vector vdr and the integer vector vqln to obtain the calculation result vr of the vector sine function or the vector cosine function. For an instruction set architecture encoded with fixed-length instructions, the present invention does not require additional address calculation instructions and vector load instructions to obtain the coefficient constants of polynomial approximation from the constant pool, thereby greatly improving the performance of the vector sine function and the vector cosine function.

The purpose of this invention is to provide a method for DOA estimation of acoustic vector arrays based on quaternion matrix dimensionality reduction, comprising the following steps: constructing a CAACIS acoustic vector coprime array at the receiver; establishing a quaternion model of the received signal based on the array output; estimating the quaternion covariance matrix; performing dimensionality reduction on the quaternion covariance matrix to obtain a virtual array received signal model of the acoustic vector coprime array; reconstructing the Hermitian-Toeplitz matrix; performing eigenvalue decomposition on the Hermitian-Toeplitz matrix and constructing a spatial spectral function; and obtaining the DOA estimation result through spectral peak search. This invention addresses the problem of how to achieve multi-objective DOA estimation with both low complexity and good performance based on a quaternion framework under acoustic vector coprime arrays.

This invention relates to a method and system for simulating stationary non-Gaussian wind pressure based on a machine learning-enhanced Hermite multinomial model. First, a multi-input, single-output machine learning model is constructed to determine the statistical moments required in the transformation function of the proposed method. Then, based on this, a neural network transformation model is established from a non-Gaussian power spectrum to a potential Gaussian power spectrum using a multi-input, multi-output model. Finally, an ML-EHPM model constructed based on dual machine learning models predicts the statistical moments required for the transformation function and the potential Gaussian power spectrum predicted by the Gaussian PSD prediction model, respectively. This potential Gaussian power spectrum representation is then used to simulate non-Gaussian wind pressure. Under conditions of strong non-Gaussian wind pressure, wind pressure exceeding the monotonic region, and bimodal wind pressure distribution, the ML-EHPM method can more realistically reproduce the target probability density function and power spectral density, significantly outperforming the traditional HPM method in simulation accuracy, especially in strong non-Gaussian samples.

The invention discloses an isogeometric robust topological optimization method of a composite material structure considering thermal coupling, and the method comprises the steps: describing the internal temperature of the structure as an uncertain variable meeting the non-precise probability distribution through building an isogeometric robust topological optimization model of the composite material structure considering thermal coupling; a corresponding worst target robust performance value is solved through a gradient search algorithm and Gauss-Hermite integral, a constraint performance reliability analysis method considering worst limit state fluctuation is provided, the sensitivity of a worst target performance function, the sensitivity of a worst limit state function and the sensitivity of a volume constraint function are solved, and an optimal topological structure is obtained through a moving asymptote method. The built composite material structure robust topological optimization model truly reflects the internal temperature fluctuation of the structure under the actual working condition, the optimization result can be efficiently obtained, and the method has good engineering application value.

This invention discloses an efficient simulation method for non-Gaussian processes based on a hybrid HPM and JTM model, belonging to the field of structural dynamics technology. This invention achieves rapid simulation of non-Gaussian processes through parameter estimation using Hermitian polynomial and Johnson transformation models based on machine learning, explicit transformation from non-Gaussian correlation functions to Gaussian correlation functions, and fast simulation of non-Gaussian processes based on linear filtering. While ensuring accuracy, this method effectively improves the efficiency of non-Gaussian process simulation and is applicable to non-Gaussian process simulation in various engineering fields.

A method for optimizing a system that includes a set of degrees of freedom (DOF) and a set of constraints on the set of DOF is disclosed. Each constraint is associated with a subset of the set of DOF. The method includes determining a first polynomial function. A set of qubits of a quantum computing system (QCS) is prepared in a first quantum state Subsequent to preparing the set of qubits in the first quantum state, the QCS performs a set of unitary operations on the set of qubits. Subsequent to performing the set of unitary operations on the set of qubits, the QCS generates a first set of observables by performing a first measurement operation on each qubit of the set of qubits. A set of values is determined based on the first set of observables.

This invention discloses a method for generating a hollow array light source, comprising: selecting a vortex phase with Gaussian intensity distribution and carrying topological charge in an initial light field; introducing a Hermitian-Gaussian correlation function, and constructing a cross-spectral density function of the Hermitian-Gaussian correlated vortex light source based on the vortex phase with Gaussian intensity distribution and carrying topological charge and the Hermitian-Gaussian correlation function; introducing an ABCD optical system, constructing a transmission model based on the cross-spectral density function of the Hermitian-Gaussian correlated vortex and the ABCD optical system, adjusting various parameters in the transmission model to form multiple hollow array beams, and then forming a hollow array light source through multiple hollow array beams; this invention, through the joint modulation of the Hermitian-Gaussian correlation structure and the vortex topological charge, can achieve effective control of the array light intensity distribution, realize the preparation of hollow array beams, and form an array light source by adjusting the array beams. The method is simple to operate and easy to implement.

The invention discloses an anti-quantum function commitment method based on a chain function commitment, and the method specifically comprises the steps: carrying out the system initialization, generating a system public key parameter through a trusted third party according to a function scale parameter and a safety parameter, and distributing the system public key parameter to a commitment end and a receiving end; the commitment end generates a commitment value for the function input value based on the public key parameter and sends the commitment value to the receiving end; the commitment end represents an objective function as a directed acyclic graph and splits the directed acyclic graph into a plurality of multivariate quadratic polynomials, and the objective function comprises a function capable of being represented as a finite-scale arithmetic circuit; on the basis of the function input value and the split multivariate quadratic polynomial, the commitment end generates an output proof that the target function takes the input value as the input; and the receiving end verifies the validity of the function value and the corresponding commitment information by using the public key parameter and the proof, if the verification is passed, the commitment value and the function value are valid, otherwise, the commitment value and the function value are invalid. According to the scheme of the invention, the quantum collapse attack can be effectively prevented, and the node storage and synchronization overhead can be greatly reduced.

The application provides a CHF prediction model determination method, a medium and an electronic device, and relates to the technical field of data processing. The method comprises the following steps: introducing a first norm penalty term and a second norm penalty term into a composite loss function simultaneously, the first norm penalty term being associated with the sum of the absolute values of each coefficient included in a coefficient set in a CHF initial prediction model, and the second norm penalty term being associated with the sum of squares of each coefficient in the coefficient set. In the process of optimizing the CHF initial prediction model with the optimization target of minimizing the composite loss function, the first norm penalty term is used to induce the sparsity of the corresponding polynomial function of the model, and tends to accurately compress the coefficients corresponding to the working condition parameters with weak contribution to the predicted CHF to zero, which is equivalent to automatically removing the redundant terms in the polynomial function. The second norm penalty is used to control the complexity of the model, prevent the value of a certain coefficient in the coefficient set from becoming too large, and effectively improve the stability and anti-overfitting ability of the model.

The invention discloses a priori knowledge-guided universal inverter power supply model non-mechanism analysis method, which can realize accurate prediction of impedance in a relatively large frequency band range by only needing a small amount of data and working condition points, meets the generalization of network tracking and network construction, and has strong generalization ability. The method is characterized in that 1-1000 Hz coarse frequency sweeping is carried out on an unknown inverter-based power supply (IBPS), frequency bands are divided according to the impedance change slope, actual measurement points are reserved in the rapid change frequency bands, data are enhanced by segmented cubic Hermite interpolation in the smooth frequency bands, and the test points are greatly reduced while the accuracy of the key frequency bands is guaranteed; and a Bayesian optimization neural network structure, L2 regularization, an EI sampling function and Z-score standardization are further introduced, so that the robustness and generalization ability of the model are improved. The method is suitable for limited working conditions and small sample conditions, and is good in applicability and strong in generalization ability under the control of a network tracking type and a network constructing type.

This invention discloses a design method for a long-short split-flow impeller based on S-shaped short blades and a water pump. The long-short split-flow impeller includes a hub, several long blades, and several short blades. Each short blade includes an inlet section, a middle section, and an outlet section. The inlet section is identical to the original inlet section, while the outlet section rotates slightly relative to the original outlet section. The shape of the middle section is determined by a cubic Hermite basis function. One end of the middle section smoothly transitions to the inlet section, and the other end smoothly transitions to the outlet section. This invention achieves continuous, smooth, and controllable curvature geometry for the short blade profile throughout the entire design range, enabling controlled geometric adjustment of the short blade in the middle section. This allows for curvature control of the short blade in localized areas, thereby improving the pressure and velocity distribution in the flow field, enhancing the flow stability of the blade channel, and significantly optimizing the pump's hydrodynamic performance and operating efficiency.

The present invention discloses a nonlinear constraint coupling-driven implicit modeling optimization method, system, and memory. The method first obtains nonlinear constraint data for a target geological interface. Then, based on the initial control points of the target geological interface, an initial implicit function representation is generated using a Hermite radial basis function. Next, based on the data type of the nonlinear constraint, a nonlinear coupling-driven optimization objective function for the implicit function representation of the target geological interface is obtained. This objective function is optimized using an implicit gradient flow algorithm to obtain a geological interface reconstruction model and a globally optimized target geological interface model. The present invention also provides a computer-readable memory containing a computer program that implements the aforementioned nonlinear constraint coupling-driven implicit modeling optimization method. The memory is readable and executable, and together with a programmable logic controller and a processor, forms an optimization system. This system achieves high-precision modeling of three-dimensional geological interfaces by fully coupling a large amount of nonlinear constraint data for the geological interface and constraining the implicit modeling framework.