120 results about "Radial basis function" patented technology

The invention relates to the technical field of ship energy consumption prediction, and discloses a ship energy consumption interval prediction method and system based on a Gaussian quantile regression model.The method comprises the steps that firstly, a ship navigation historical data set is preprocessed, a model input feature set is screened, a Gaussian process quantile regression model with a radial basis function as a kernel is constructed, and hyper-parameters are optimized; and after the prediction performance of the subset evaluation point is verified through the model, an upper quantile prediction model and a lower quantile prediction model are respectively established according to a target confidence level, and finally a prediction interval is synchronously output and an evaluation report is generated. According to the method, through combination of Gaussian process processing nonlinear relation and quantile regression estimation condition distribution, high-precision point prediction is provided, meanwhile, prediction uncertainty can be quantized, an energy consumption prediction interval corresponding to a target confidence level is output, and more comprehensive and reliable information support is provided for ship energy efficiency management and operation decision making.

The invention discloses a J-A model parameter identification method, system, equipment and medium based on RBF and an improved brownish bear algorithm, and belongs to the technical field of power system optimization, and the method comprises the steps: building a Jiles-Atherton hysteresis reverse model of a current transformer, determining a to-be-identified parameter vector, and building a model with a root-mean-square error between actually measured magnetic field intensity and simulated magnetic field intensity as a target function, training a radial basis function neural network model, expanding data through linear interpolation processing, obtaining a predicted magnetic induction intensity value, inputting an objective function and radial basis function prediction data into an improved brownish bear optimization algorithm, and iteratively optimizing model parameters through hierarchical population position updating and fitness evaluation until convergence conditions are met. And outputting an optimal parameter identification result. According to the method, high-precision and high-efficiency identification of hysteresis model parameters is realized, the generalization capability and robustness of the system are improved, and reliable technical support is provided for hysteresis characteristic analysis of a complex physical system.

The invention discloses a machine learning driven vehicle frame lightweight design method, which comprises the following steps: (1) performing vehicle frame parametric modeling in CATIA, constructing a vehicle frame modal analysis model and a stiffness analysis model in ABAQUS, and deducing a mathematical model of vehicle frame lightweight design; (2) performing Latin hypercube sampling and discrete Gaussian sampling on continuous and discrete parameters to generate a frame population, and establishing a database; (3) designing a variable type driven hybrid variation mechanism to obtain a candidate frame population; (4) constructing a radial basis function machine learning model, and deducing a comprehensive minimum statistical lower limit function to screen an optimal candidate frame; and (5) performing parametric modeling and simulation analysis on the optimal candidate frame, updating the frame population and the database, returning to the step (3) until the optimal frame meets the design requirement, and outputting the optimal frame. According to the method, adaptive variation strategies and screening functions are designed according to the vehicle frame parameter characteristics, and the vehicle frame lightweight design effect is good.

The invention discloses a front subframe lightweight design method and device based on RMNN, a medium and a program product, and the method comprises the steps: (1) constructing three simulation models of weight, stress and modal of a front subframe, and deducing a lightweight design model containing stress and modal constraints; (2) generating a population based on a uniform experimental design and a correlation criterion, performing simulation evaluation on the population, and constructing a database; (3) constructing a network architecture and training an RMNN; (4) generating a candidate frame set according to a random preferential strategy and the RMNN; and (5) establishing a radial basis function model, screening an optimal frame, performing simulation evaluation, updating the database and the population, returning to the step (3) until the optimization target reaches the standard, and outputting an optimal solution of the optimization parameter. According to the method, the feasible region is quickly searched through the RMNN, the problems that the high-dimensional design space search efficiency is low and the feasible region is difficult to recognize under multiple constraints are further solved in combination with the radial basis function model, and quick optimization can be carried out for the lightweight design of the front subframe.

The invention provides an urban storm surge forecasting method based on multi-source information coupling, and the method comprises the steps: taking multi-source typhoon driving element data as the input information of a preset typhoon structure reconstruction model, and outputting typhoon key physical parameters through the typhoon structure reconstruction model; based on a Kriging interpolation and radial basis function mixing method, carrying out fusion processing on the multi-source topographic data, and constructing topographic grid information; extracting tide harmonic constants of a specified number of partial tides from the multi-source tide factor data; based on the typhoon key physical parameters, the terrain grid information and the tide harmonic constants, determining input information of a predetermined urban storm surge forecasting model; and outputting a storm surge level abnormal value and a horizontal flow velocity component corresponding to the multi-source prediction associated data through the urban storm surge prediction model. According to the scheme, the precision of urban storm surge forecasting is improved.

The invention belongs to the technical field of geological three-dimensional modeling, and provides a line-plane boundary constraint promotion modeling method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the modeling through employing a line-plane boundary constraint promotion model, and obtaining a three-dimensional geologic body model; the modeling method comprises the steps that a point set of a three-dimensional geologic body is obtained, optimization is conducted through a minimization energy function when radial basis function implicit modeling processing is conducted according to the point set, and a normal vector and an implicit function are obtained; the method comprises the following steps: introducing information of a planar geologic structure into an implicit modeling framework in an integral form, performing line-plane constraint on an implicit function, processing the line-plane constrained implicit function through a solution matrix to obtain an implicit function parameter, and determining a line-plane boundary constraint promotion model according to the implicit function and the implicit function parameter. The planar feature information of a geologic body can be effectively utilized, accurate constraint and continuous expression of a geological interface or a stratigraphic structure are achieved, and the spatial precision and reliability of three-dimensional geological modeling are improved.

The invention discloses an electric arc surfacing repair trajectory planning method based on point cloud information. The method comprises the steps that defect point cloud data of a to-be-repaired area and intact point cloud data of an intact area adjacent to the to-be-repaired area are acquired; carrying out reconstruction fitting on the obtained defect point cloud data by adopting Poisson reconstruction to obtain a defect feature surface; fitting the obtained intact point cloud data by using a high-order function and a radial basis function to obtain an intact feature surface; and offsetting the intact feature surface for a specific distance along the defect depth direction to obtain an offset curved surface, and obtaining a path planning target area according to the offset curved surface. And the defect morphology is obtained on the premise of no original model, so that the applicability of the welding robot is enhanced.

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.

The invention discloses a battery health state prediction method based on variational feature extraction and composite kernel function optimization Gaussian process regression. The method comprises the following steps: firstly, collecting battery cycle data, extracting features by utilizing VAE, screening out an optimal feature set by adopting PCC, and jointly dividing the optimal feature set and SOH data into a training set and a test set; in the training process, the optimal modal number K of the VMD is determined through the envelope entropy, a composite kernel function CKFGPR model fusing a linear kernel, a radial basis kernel and a periodic kernel is constructed, and hyper-parameters are optimized through maximum likelihood estimation. In the test stage, the features are decomposed into K intrinsic mode components by using VMD, the K intrinsic mode components are respectively input into the CKFGPR model for prediction and reconstruction, and finally the CKFGPR model is input again to realize SOH prediction. According to the method, the feature robustness is enhanced through cooperative application of the VAE and the VMD, the fitting capability of the GPR to the battery degradation behavior is improved in combination with the composite kernel function, and the SOH prediction precision is effectively improved.

The invention discloses a parameterization design method for a speed brake mechanism applied to a wide-area aircraft. The parameterization design method comprises the steps that the maximum resistance increment and the target resistance increment of the speed brake mechanism are determined; determining constraint conditions of reference design parameters and dynamic design parameters, and constructing a resistance increment mathematical model based on a radial basis function; the maximum resistance increment is substituted into the mathematical model, the extension length of the auxiliary board and the opening angle of the main board are made to be maximum values of constraint conditions, an optimization equation is built, and an optimal reference design parameter combination meeting the maximum resistance increment requirement is obtained; the target resistance increment is substituted into the mathematical model, an optimization equation is constructed by taking the reference design parameters as optimal reference design parameters, and an optimal dynamic design parameter combination meeting the target resistance increment requirement is obtained; and performing simulation verification on the optimal design parameter combination until the design is verified to be qualified, and outputting final design parameters. Wide-range adjustment of resistance can be achieved, accurate control over resistance errors is achieved, and the wide-range aircraft resistance increasing requirement is met.

The invention discloses a CVM-based airfoil profile multi-objective optimization method, equipment, a medium and a program product. The method comprises the following steps: (1) respectively constructing an airfoil profile lift coefficient simulation model, an airfoil profile resistance coefficient simulation model and an airfoil profile rigidity simulation model in SU2 and Ansys; (2) establishing a population and a database by using Latin hypercube sampling and Pearson product-moment correlation coefficients; (3) constructing a global radial basis function prediction model; (4) generating candidate offspring individuals for each sub-problem by using elite-guided co-evolution operation, and constructing a sub-population; (5) evolving the sub-population in the characteristic space after covariance matrix mapping; and (6) based on a Chebyshev fitness function, screening an airfoil node parameter vector solution set, updating a population and a database, and turning to the step (3) until a design cycle is reached. According to the method, the comprehensive performance of the airfoil structure is improved through elite-guided co-evolution and characteristic space efficient evolution after covariance matrix mapping.

The present application relates to the technical field of aquaculture, and particularly relates to an improved PSO-based water quality parameter prediction method and system for aquaculture, which comprises collecting water quality parameters at different positions and depths in a breeding pond; training an improved radial basis function (RBF) neural network using training set data; and optimizing the parameters of the improved RBF neural network model using an improved particle swarm optimization (PSO) algorithm. The present application introduces a mixed Gaussian function and an abnormal S-shaped function into the radial basis function of the traditional RBF neural network, thereby solving the problem of weak capability of the model in nonlinear data modeling. Furthermore, the present application improves the inertia factor and the learning factor in the traditional PSO algorithm, thereby solving the problems of slow parameter convergence speed and poor global search capability in the RBF neural network.

The invention discloses a crane main beam non-probability reliability assessment method based on a convex set model, and the method comprises the steps: collecting the working condition and key section multi-source data of a crane, and carrying out the time alignment, missing measurement compensation and time-frequency feature extraction; selecting a parameter vector influencing the safety of the main beam, and constructing a combined convex set uncertain domain consisting of an interval box, an ellipsoid set and polyhedron linear constraints; based on a finite element and radial basis function substitution model, main beam key section response mapping is established, the proportion of equivalent stress and deflection relative to an allowable value is obtained, and an overall limit criterion is formed; executing worst case search in the uncertain domain, and calculating an opposite number of a maximum value of a limit criterion as a non-probability reliability index; and reliability indexes are further calculated and summarized at the section layer and the main beam layer, and the reliable state grading of the main beam is realized according to a preset threshold value. The method does not need to depend on probability distribution hypothesis, and rapid assessment of the safety of the crane girder can be realized under small sample and multi-source uncertain conditions.

The invention belongs to the technical field of magnetic suspension control, and provides a multi-point cross-coupling suspension control method for a sliding-mode-driven RBF network, and the method comprises the steps: constructing a sliding-mode surface and a radial basis function neural network according to the suspension gap error of a single-point electromagnet, and obtaining a sliding-mode-driven RBF neural network control method for the single-point electromagnet; independent sliding mode driving RBF neural network control is respectively carried out at four electromagnets, gap and speed dual cross coupling terms are designed according to errors among points in a four-point suspension frame, error terms are superposed in control input, and finally a sliding mode surface driving RBF controller combined with cross coupling control is formed. According to the method, the tracking performance of the target gap and the synchronization performance between the suspension points are improved, stable suspension of the suspension frame is achieved, and it is verified that the method still has a remarkable control effect under the operation conditions that the track is not smooth and faults occur.

The invention provides a numerical feature embedding method based on a scale perception radial basis function and a situation awareness method for the health degree of power equipment, and the method comprises the steps: obtaining a logarithm of to-be-coded numerical data x, and obtaining a base number D and an index L; performing RBF (Radial Basis Function) expansion on the base number D to obtain the expression of the base number D; carrying out soft sub-bucket distribution on the index L to obtain a distance from the index L to each soft sub-bucket, taking the distance as a coefficient of the soft sub-bucket, and weighting each soft sub-bucket according to the coefficient of the soft sub-bucket to obtain representation of the index L; and converting the representation of the index L into two numbers by adopting a gated linear layer network, and scaling the representation of the base number D to obtain the coded representation of the numerical data x.

The application belongs to the technical field of load identification of structural health monitoring, and provides a concentrated load positioning method based on principal stress constraint and strain gradient trajectory identification, which comprises the following steps: arranging strain sensors in a monitoring area of a planar structure plate and collecting strain data of measuring points, constructing a strain field inversion function based on a radial basis function and establishing an error optimization function containing a fitting error term and a smooth constraint, introducing a principal stress direction constraint and a gradient projection smooth constraint to form a comprehensive error function and solving to obtain an optimal strain field distribution, then calculating strain gradient vectors of each grid node and tracking a gradient trajectory through a gradient descent method, and finally performing an iterative clustering analysis based on distance statistics on a trajectory end point to output a cluster center as a concentrated load application position identification result. The application can improve the concentrated load positioning accuracy, enhance the anti-noise capability, and realize fast, stable and large-sample-free load position identification.

The application discloses a method for determining the control range of bolt connection pre-tightening force, which comprises the following steps: firstly, establishing a finite element model of bolt connection under the condition of applying pre-tightening force; calculating the maximum equivalent stress of the connection area under different pre-tightening force conditions; constructing a radial basis function proxy model based on the pre-tightening force and the equivalent stress; considering the uncertainty of the pre-tightening force, obtaining a plurality of groups of sample points of the pre-tightening force, and calculating the corresponding equivalent stress through the radial basis function proxy model; calculating the reliability confidence factor of the equivalent stress, and taking the bolt pre-tightening force when the reliability confidence factor is greater than 1 as the control applied pre-tightening force range. Starting from the essence of the uncertainty of the pre-tightening force, the uncertainty of the pre-tightening force is described through the mean value and the multiple relationship of the pre-tightening force, the safe working range of the structure is determined according to the reliability confidence factor of the equivalent stress, the pre-tightening force control range is obtained, the traditional pre-tightening force control is expanded from a single deterministic value to a range interval, and the reliability requirement of the structure in the range is ensured.

The embodiment of the application provides a marine environment field reconstruction method based on a multi-stage interpolation method. The method is used for multi-source data fusion of low-resolution grid environment field output by a marine dynamic model and high-precision data observed by a mobile node, and a high-resolution and high-precision marine environment field is obtained through multi-stage interpolation reconstruction. The multi-source data fusion is always carried out in a uniform grid, first, the mobile node observation data is fused into the low-resolution grid field output by the marine dynamic model through a mean substitution method to obtain a fused grid field. Then, a radial basis function (RBF) grid interpolation model is constructed to extract grid features, and the fused grid field is expanded and the grid interpolation model is used to fill in the grid attribute values to obtain an expanded grid field. Finally, the foregoing steps are repeated, and the high-resolution reconstruction of the marine environment field is completed through multi-stage iteration. The application has potential application value in improving the resolution and precision of the environment field in the multi-source environment field data fusion scene in the marine information field.

The invention discloses a Delta robot trajectory tracking method based on fuzzy-RBF (Radial Basis Function) control. The Delta robot trajectory tracking method comprises the following steps: firstly, establishing a conventional dynamic model of a Delta robot; a virtual control signal is introduced, a sliding mode surface is constructed, and a control law of a dynamic surface sliding mode controller is designed to ensure the stability and convergence of the system. And constructing a specific fuzzy logic system for adjusting the control gain. A radial basis function (RBF) structure is embedded into a fuzzy membership function, and the center and width of the membership function are iteratively updated by adopting an online adaptive optimization algorithm based on gradient descent, so that dynamic self-learning and parameter adaptation of a fuzzy system are realized. Finally, the output of the designed fuzzy-RBF self-adaptive dynamic surface sliding mode controller acts on a robot joint driving system, and high-precision trajectory tracking control of the Delta robot end effector is successfully achieved. A simulation result verifies the effectiveness and robustness of the provided control method in the aspects of processing parameter uncertainty and external disturbance.

The application discloses an OTR tire pattern heat dissipation hole parameter design and heat dissipation strengthening method, and belongs to the tire structure design field.The method firstly establishes a tire parameterized model containing a heat dissipation hole, determines the length, width, depth and inclination angle of the heat dissipation hole as key design variables; subsequently, sample points are generated in the design space by using Latin hypercube sampling, and the average convective heat transfer coefficient corresponding to each sample is calculated through numerical simulation; then, a radial basis function proxy model with the design variables as input and the heat transfer coefficient as response is constructed, and the accuracy thereof is verified; thereafter, the design variables are globally optimized by using an adaptive simulated annealing algorithm with the maximization of the average convective heat transfer coefficient as the target, and the optimal structure parameters are obtained; finally, the optimization scheme is modeled and numerically verified.The application significantly improves the heat dissipation efficiency of the OTR tire through multi-parameter collaborative optimization, has the advantages of short design cycle, strong optimization ability and good versatility, and is helpful to prolong the tire life.

This invention relates to a practical laser spot localization method for array detectors based on radial basis function (RBF) neural networks, belonging to the field of detector technology. It utilizes RBF neural networks to achieve high-precision localization of laser spots. The RBF neural network is designed and trained, using the training set as the input layer and the output layer as the predicted spot position. The parameters and number of neurons are continuously trained and adjusted. The test set is input into the trained neural network to predict the spot position, and the effectiveness of the RBF neural network is tested. This invention has fewer parameters, lower computational load, higher accuracy, and is applicable to n*n multi-module array detectors. Furthermore, the experimental acquisition process in this application uses a quadrant detector, making the operation relatively simple.

The application discloses a direct digital frequency synthesizer based on RBF neural network of phased optimization, which comprises a phase accumulator responsible for generating a phase signal, an RBF neural network module responsible for converting the phase signal into a digital amplitude signal, a digital-analog converter responsible for converting the digital amplitude signal into a low-frequency sawtooth signal and a low-pass filter responsible for converting the sawtooth signal into a smooth analog signal. The RBF neural network module comprises a hidden layer and an output layer, each node in the hidden layer has a corresponding radial basis function, so as to map a corresponding characteristic value for each input phase information, multiply the corresponding characteristic value of each node with the corresponding weight in the output layer, and then sum up to obtain an amplitude value corresponding to the phase information. Compared with the prior art, the application introduces the RBF neural network, so that the direct digital frequency synthesizer has stronger scalability and adaptability when processing complex and nonlinear frequency synthesis tasks.

The application discloses a multi-constraint geometric nonlinear level set topology optimization method, device and medium, and aims to solve the problem that the current linear condition level set method is difficult to well optimize the large deformation of a to-be-optimized component. The method divides a design domain corresponding to the to-be-optimized component into a plurality of finite element units, and establishes corresponding level set radial basis functions based on volume constraints; according to the relationship between the element strain increment and the node displacement increment of the finite element unit, displacement constraint terms and frequency constraint terms are introduced to extend the Lagrange function; according to the nonlinear element unbalanced force, the element thermal load and the overall thermal load are calculated; based on the extended Lagrange function and the overall thermal load of the to-be-optimized component under the set temperature, the design domain is topologically optimized. The application considers the displacement constraint and the frequency constraint under the nonlinearity, is suitable for the to-be-optimized component under the large deformation condition, and can achieve an optimal topology result.

The invention belongs to the technical field of petroleum geophysical exploration, and relates to an overlapped meshless undulating surface seismic wave field numerical simulation method and system. The method comprises the following steps: establishing an undulating surface seismic wave speed model and an absorption attenuation Q model based on geological parameters of an exploration area; based on the undulating surface seismic wave speed model and the absorption attenuation Q model, adopting an overlapping meshless node subdivision method to obtain a node data set of an overlapping region; taking the node data set as input, expanding a radial basis function based on a Chebyshev polynomial and forming a difference matrix, and solving to obtain a node difference coefficient set; and taking the node data set and the node difference coefficient set as input, performing finite difference discretization on a Kelvin-Voigt model-based viscous-acoustic wave equation based on a radial basis function, implementing forward continuation of the wave field, and completing the seismic wave field numerical simulation of the overlapped meshless undulating surface. According to the method, the constant Q model is accurately described, amplitude-phase decoupling is realized, and the absorption attenuation effect of the medium can be effectively described.

The invention discloses a radial point interpolation finite element method for linear elastic solid harmonic response and application, and belongs to the technical field of solid mechanics and engineering numerical simulation. The method comprises the following steps: discretizing a linear elastic solid problem domain into a standard finite element grid, deriving a nodal function through a radial basis function, selecting a local enhanced interpolation basis function to construct a newly added degree of freedom and complete local numerical approximation, and constructing a global interpolation format of a displacement vector field in an element in combination with unit decomposition characteristics; based on the interpolation format and a Galerkin weak form of an elastic dynamic problem, applying a displacement boundary condition to obtain a system forced vibration equation; acquiring steady-state dynamic response and frequency domain response characteristics under different frequency harmonic excitation; according to the method, high-order interpolation can be realized without increasing nodes, the numerical precision is improved, the computing resource and time consumption is reduced and a more reliable numerical solution is provided under the condition that the grid density is relatively small, and the method has important application value in engineering practice.

An automatic detection method for CBCT cephalometric landmarks based on multi-geometric guidance and specific perceptual coding includes the following steps: Step S1, downsampling the 3D CBCT image and obtaining a preliminary coordinate set through a coarse localization network; Step S2, cropping image blocks centered on the coordinates and extracting local features through a visual encoder containing a shared basic encoder and a low-rank adapter; Step S3, calculating the relative position matrix of the landmarks, encoding spatial relationships using radial basis functions, and constructing a multi-anatomical heterogeneous map; Step S4, inputting visual and edge features into a multi-geometric guidance Transformer, fusing global constraints and updating features using an attention mechanism; Step S5, extracting directional geometric relationships using spherical harmonic functions to construct higher-order update terms, and dynamically updating the heterogeneous map using a gated residual mechanism; Step S6, predicting coordinate offsets through a multi-layer network and performing iterative optimization to output high-precision 3D coordinates. This method significantly improves detection accuracy and robustness.

The invention provides a seismic parameter inversion method based on an RBF proxy model and multi-algorithm collaborative optimization, and relates to the technical field of seismic services, and the method comprises the steps: building an RBF proxy model, carrying out the local real evaluation and proxy verification, and dynamically dividing a global exploration stage and a local refinement stage according to the optimal fitness change rate, smooth transition is carried out by adopting a PSO particle swarm inertia weight linear decline strategy; in the global and local stages, the annealing algorithm inferior solution acceptance rate and the cross entropy sampling distribution covariance are continuously monitored, and dynamic adjustment is carried out. According to the method, rapid prediction of complex stratum parameters and observation waveforms can be achieved, misleading search caused by agent failure is avoided, wide global exploration can be conducted in the early stage through multi-algorithm collaboration and dynamic division of the optimization stage, local refinement can be achieved in the later stage, search activity and diversity are effectively maintained, and the search efficiency is improved. The search efficiency and the inversion reliability are remarkably improved, and meanwhile, the dependence on expensive forward calculation is reduced.

This invention provides a comprehensive evaluation method for the mid-frequency performance of a filter field grounding system, belonging to the field of power grid equipment technology. The invention extracts the resonant frequency distribution by inputting a wideband impedance spectrum into an artificial intelligence frequency domain response identification model based on a coarse spatiotemporal attention mechanism. It then dynamically adjusts the spatial receptive field parameters according to the resonant frequency offset function. A three-dimensional electromagnetic field simulation model is established based on soil frequency domain parameters, and the ground potential and current density distribution are calculated using the radial basis function collocation method and the fast multipole algorithm. The entropy value of current distribution uniformity and the Gini coefficient are calculated to identify current concentration areas and assess local overheating risk. Finally, the analytic hierarchy process (AHP) is used to dynamically weight and comprehensively score indicators such as impedance peak value, resonant frequency, ground potential gradient, current uniformity, and overheating risk. This invention solves the technical problem of inaccurate performance evaluation of filter field grounding systems under mid-frequency impulse conditions.

The application relates to a development language disorder early identification method and system fusing neural activity features and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring original neurophysiological signals and matched clinical information, and constructing a data set; power features and coherence features of the signals are normalized to generate enhanced feature vectors; attention weighted feature vectors are obtained through mutual information based attention mechanism weighting; the radial basis function is used to nonlinearly map the feature vectors to a high-dimensional space to obtain extended feature vectors; a development language disorder early identification model is constructed, brain region level feature aggregation and modulation, clinical factor fusion and gate modulation are adopted, a decision function fusing nonlinear interaction items is introduced, and a binary cross entropy loss function is used to optimize the model; after model training is completed by using a training set, signals of a subject to be evaluated are input into the model after the same preprocessing, and an identification result is output. The application can improve the accuracy of development language disorder early identification.

The present application belongs to the technical field of three-dimensional geological modeling, and provides a kind of implicit modeling method, device, electronic equipment and storage medium of three-dimensional geological body, wherein the method comprises: using line boundary constraint generalization model to carry out modeling processing, to obtain three-dimensional geological body model;Line boundary constraint generalization model is obtained by the following steps: when carrying out radial basis function implicit modeling processing according to point set, optimization is carried out by minimizing energy function, to obtain normal vector and implicit function;Line constraint is carried out to implicit function, and the implicit function is processed by solving matrix to line constraint, to obtain implicit function parameter, and the line boundary constraint generalization model is determined according to implicit function and implicit function parameter.The beneficial effects of the present application are: the modeling precision of spatial data of geological exploration is improved.