41 results about "Newton's method" patented technology

The invention relates to a high-precision multi-surface wavelength tuning interference measurement signal processing method. The core of the method is to solve the problem of dissociation of superposed interference signals and overcome the frequency spectrum fence effect. The patent process comprises the following steps: constructing a dissociation framework based on an iteration vector and a time delay virtual detection point; optimizing an iteration process by using an approximate Newton method; designing a double judgment dissociation criterion and orthogonally outputting a dissociation result; performing frequency spectrum fine segmentation analysis and phase demodulation function extraction on the dissociation signal to realize high-precision solution of harmonic frequency and phase; and the dissociation quality can be evaluated by using a correlation coefficient quantification method. The method can effectively separate multi-surface interference superposition signals and accurately reconstruct key parameters, is obviously superior to a traditional method (such as FFT), and improves the performance by about two orders of magnitude. Experiments prove that the surface shape error is controlled to be 10e <-3 > magnitude, the frequency precision reaches 10e <-4 > magnitude, and the method has excellent robustness and wide applicability and is suitable for high-precision optical element detection.

The invention relates to the technical field of flood control optimal scheduling, in particular to a reservoir group flood control optimal scheduling method and system based on quadratic programming, and the method comprises the following steps: systematically combing the spatial distribution and hydraulic connection of key nodes according to hydraulic engineering facilities and hydrological elements in a drainage basin, constructing a flood control scheduling topology network, and constructing a flood control scheduling topology network; a reservoir group flood control optimization scheduling topological graph is obtained; establishing a reservoir group flood control optimal scheduling quadratic programming model according to the reservoir group flood control optimal scheduling topological graph; and on the basis of the MINOS library of the GAMS, performing optimization solution on the reservoir group flood control optimization scheduling quadratic programming model by using an approximate gradient method and a quasi-Newton method. According to the method, propagation of the flood among the reservoir groups is coupled into the mathematical equation set, complex simulation sequence coding is avoided, modeling is easy, universality is good, the model is easy to seek an optimal solution, the flood control capacity and the peak clipping effect of the reservoir and reasonable scheduling of the water storage capacity of the reservoir can be balanced, and the flood control precision and efficiency are improved.

The invention belongs to the field of periodic signal parameter estimation, and discloses an off-grid-based signal periodic estimation method and device and a medium, and the method comprises the steps: 1, constructing a cost function; step 2, performing p = 0 iteration, and initializing the fundamental frequency f of the periodic signal; step 3, performing the (p + 1) th iteration, substituting the estimated value of the p th iteration f into J (f, u), and optimizing u to obtain the estimated value of u; 4, substituting the estimated value of the (p + 1) th iteration u into J (f, u), and optimizing the optimized fundamental frequency f by adopting a Newton method; step 5, obtaining a period estimation value of the (p + 1) th iteration; and step 6, repeating the steps 3-5 until the number of iterations P. The method can be applied to an actual communication or signal processing hardware system, the problem of inaccurate period estimation caused by crystal oscillator frequency drift of the hardware system is solved, and the performance of a subsequent communication or signal processing algorithm is improved.

The present invention relates to a method and device for determining parameters influencing the infrared characteristics of a ship. The method comprises: pre-establishing a relationship equation between effective infrared radiation data of each facet on a ship target, multiple environmental parameters, and multiple parameters to be determined; obtaining n*m groups of test data for the corresponding facet of the ship target in an actual scenario; inputting each m groups of test data into the relationship equation to obtain a group of nonlinear constraint equations with multiple parameters to be determined as variables; solving each group of nonlinear constraint equations using the Newton method to obtain a corresponding group of estimated values ​​of the parameters to be determined; calculating a confidence interval for each parameter to be determined based on the n groups of estimated values ​​of the parameters to be determined, determining the estimated value of each parameter to be determined that falls within the corresponding confidence interval among the n estimated values ​​of each parameter to be determined, and determining the target value corresponding to the parameter to be determined based on the estimated value of each parameter to be determined that falls within the corresponding confidence interval among the n estimated values ​​of the parameter to be determined. The present invention can improve the accuracy of the parameters to be determined.

The invention discloses an auxiliary power device modeling method based on fuzzy set intelligent optimization. The method comprises the following steps: firstly, establishing models of all parts of the APU according to an aerodynamic thermodynamic law, and constructing a common working equation set; then, the particle swarm optimization process is divided into four typical states of exploration, depth, convergence and local optimum, and the current state is identified in real time by using a fuzzy estimator. And on the basis, optimizing hyper-parameters such as inertia weight, cognitive coefficient and social coefficient are adaptively adjusted according to the recognition state. And finally, taking a residual weighted least square function with a quadratic penalty term as a fitness index, and driving a particle swarm to solve an equation set. According to the method, through the state recognition and parameter self-adaption mechanism, the defects that a traditional Newton method is high in dependency on an initial value and a basic particle swarm algorithm is prone to falling into local optimum and slow in convergence are effectively overcome, and efficient and steady solution of the APU model is achieved.

The application belongs to the technical field of power systems, and discloses an EIGD time-delay power system stability analysis method and device, wherein the method comprises the following steps: a linear model of the time-delay power system is established, and the differential equation in the model is converted into an abstract Cauchy problem by using an infinitesimal generator; a group of discrete points is selected for each time-delay interval of the time-delay power system, a discrete function space is established according to the discrete points, and the time-delay variable is discretized to generate a low-order partial infinitesimal generator discretization matrix; displacement and inverse transformation are performed on the discretization matrix to obtain an inverse matrix, and the characteristic value of the system is obtained according to the inverse matrix; the characteristic value is checked by using the Newton method to obtain the accurate characteristic value and the characteristic vector, and the stability of the time-delay power system is analyzed and judged. The method solves the problem of large matrix LU decomposition calculation amount in the existing EIGD time-delay power system characteristic value sparse calculation method based on the DDAE model.

The invention discloses a traffic flow prediction method based on THK-BLO-SVR (THK-BLO-Support Vector Register). The method comprises the following operation steps: firstly, loading original traffic flow data, generating a feature matrix and a target vector based on a lag time window, and carrying out standardization processing; then dynamically generating a hybrid kernel matrix, introducing MCP regularization, constraining dual variables of the SVR model, and screening key samples; the outer layer of double-layer Bayesian optimization (BLO) adaptively adjusts noise parameters and prior parameters through Bayesian posteriori distribution; the inner layer adopts a semi-smooth Newton method and a dynamic switching strategy to update dual variables, accelerates convergence and avoids local optimum; and finally, calculating a predicted value based on the test set kernel matrix, the dual variable and the bias item, and performing destandardization to restore the predicted value to an original dimension to obtain a traffic flow prediction result corresponding to the historical traffic flow data. According to the method, the precision and robustness of traffic flow prediction are remarkably improved, and the method is suitable for an urban intelligent traffic management system.

The invention belongs to the field of machinery, and particularly relates to a tooth surface friction calculation method based on a mathematical programming algorithm. The objective of the invention is to solve the problem of low calculation efficiency of the existing numerical analysis technology for the gear contact problem. According to the tooth surface friction calculation method based on the mathematical programming algorithm, the problem scale is greatly reduced through substructure static condensation, and the high efficiency of the mathematical programming algorithm (the smooth Newton method) is combined, so that the leap of the calculation speed is achieved. In addition, according to the method, a mechanical problem is creatively converted into a mathematical programming problem, and a brand new technical path is provided. The method is not only suitable for straight teeth, helical teeth and herringbone gears, but also can be popularized to friction contact analysis of other mechanical connection pairs (such as splines and bearing raceways) in theoretical framework. The whole process from parametric modeling, data exporting to model solving can achieve high automation through scripts, human intervention is reduced, and the repeatability of results is guaranteed.

The invention discloses a load flow calculation method, system and device based on quantum singular value transformation and a medium, and belongs to the technical field of load flow calculation, and the method comprises the steps: obtaining the initial power parameter of each node of a power system and the given power of each node, and determining the power unbalance amount; constructing a node admittance matrix according to the initial power parameters, constructing a correction equation of Newton method load flow calculation, and converting the correction equation into a linear equation set for expression; coding the coefficient matrix into a unitary matrix by using block coding, and executing polynomial transformation based on quantum singular value transformation to construct a quantum circuit representing an inverse matrix of the unitary matrix; encoding the power unbalance amount into a quantum initial state, and enabling a quantum circuit to act on the quantum initial state and performing measurement; and determining the correction of the node voltage based on the measurement result, judging whether to iterate based on the convergence condition, and outputting a load flow calculation result. By utilizing the characteristics of quantum parallelism and quantum singular value transformation, the time consumption of large-scale power system load flow calculation is remarkably reduced.

The present invention discloses a polar coordinate Newton method for power flow calculation suitable for research purposes, and provides a Newton method for power flow calculation that is easy to modify and maintain for scientific researchers who conduct further research based on polar coordinate Newton method for power flow calculation. For an n-node power system, the node number is independent of the node type, and the Jacobian matrix of the power flow calculation is stored as (2n)×(2n). The row elements corresponding to the PV node ΔQ and the column elements corresponding to the ΔU are all 0, and the elements of the rows and columns related to the balance node are also all 0, but the diagonal elements are not cleared to zero, and the original calculated values are retained. In this way, the linear equation solving function with excellent performance provided by the programming language can be used to solve the correction equation of the power flow calculation, which reduces the programming difficulty, improves the calculation speed, and ensures the reliability of the algorithm. The implementation example shows that the present invention can use the linear equation solving function provided by the programming language to solve the correction equation, improves the speed of the power flow calculation, and reduces the programming difficulty.

The present invention belongs to the field of ray tracing technology, and in particular relates to a high-precision NURBS surface tracing method based on a surface modeling optical system. The method comprises: S1: establishing a NURBS surface model and parametrically expressing the NURBS surface model; S2: constructing each subdivision grid of the NURBS surface model based on node vectors in two directions of the NURBS surface model; S3: obtaining the position coordinates of the center points of each valid facet of the NURBS surface model; and S4: using the tracing ray passing through the valid facet as the effective tracing ray, and using the position coordinates of the center point of the valid facet as the iterative starting coordinates, and performing ray tracing iterations on the NURBS surface model using the Newton method to obtain a final high-precision numerical solution of the effective tracing ray and the NURBS surface model. The present invention can improve the computational efficiency and robustness of the high-precision NURBS surface tracing method.

This patent discloses a method for solving corridors in short vertical propulsion systems. Addressing the convergence problem caused by improper initial value selection during corridor solving, this method selects a known feasible control plan as the initial value, ensuring model convergence within this plan. Then, it gradually expands the search range to avoid non-convergence due to initial value selection. To address the slow solution speed of traversal and optimization methods, this scheme transforms the optimization problem into an equation-solving problem, utilizing the second-order convergence property of Newton's method to achieve high-precision and rapid solution of corridor boundaries.

The application discloses an OTFS sensing integrated signal super-resolution target parameter estimation method for a synchronization error problem, which firstly establishes an OTFS time delay-Doppler domain sensing signal model considering timing offset and carrier frequency offset, and constructs a sparse observation model considering synchronization error; subsequently, a rough estimation of target time delay-Doppler parameters is completed through an ADMM algorithm, and the system synchronization error parameters are iteratively updated simultaneously by adopting an alternating minimization framework; then, multi-target interference cancellation is carried out based on the rough estimation result, and a Newton method is used to perform super-resolution fine estimation on target off-grid offset and synchronization error residual, so as to finally output fine parameters of target distance and velocity and a synchronization error estimation value. The application effectively solves the parameter estimation deviation problem caused by the synchronization error in the OTFS sensing system, and improves the estimation performance in the multi-target and closely adjacent target scene.

The application relates to a time-optimal three-axis reorientation method and device for an inertia-symmetrical rigid spacecraft, which determines an angular velocity analytical expression according to inertia symmetry of the spacecraft and a time-optimal control switch structure set in advance; through introduction of a new inertia axis coordinate system, the symmetry characteristic of a reorientation track is explicitly derived; a Radau pseudo-spectrum discrete method is used to discretize a quaternion dynamic equation, so that the time-optimal reorientation problem is converted into a nonlinear programming problem; through given two parameter values, the discretized quaternion dynamic equation is converted into a linear equation set and is solved, a discrete quaternion value sequence obtained through decomposition is used, a gradient method or a Newton method is used to optimize the two parameters, and a time-optimal reorientation track is determined according to an optimization result. The method does not need to perform numerical integration or solve a large-scale nonlinear programming problem, and the calculation efficiency is significantly improved, so that fast and accurate track generation of an inertial rigid spacecraft is realized.

The invention belongs to the technical field of aero-engine modeling, and particularly relates to an engine model solving method adopting Newton-Raphson and feedforward neural network methods. According to the method, a mixed algorithm of a feed-forward neural network agent model and a Newton-Raphson method is combined, and a rough value of an iteration independent variable is directly predicted by adopting a feed-forward neural network, so that meaningless and local extremal regions on an iteration path can be avoided to a great extent, and high-quality iteration variables are quickly provided for the Newton-Raphson method; the convergence capability of the Newton method is greatly improved, the iteration step number is reduced, and the calculation speed of the Newton method is improved. And a normalized training set processing mode is adopted, so that the training set scale and the network scale are greatly reduced, the network estimation precision is improved, and the application complexity of the hybrid solving method is reduced. According to the hybrid solving method, the simulation solving convergence of the violent dynamic process of the dynamic real-time model of the engine is remarkably improved, and compared with a gradient method, the calculation speed is increased.

The present application relates to the technical field of flood control optimal scheduling, in particular to a reservoir group flood control optimal scheduling method and system based on quadratic programming, the method comprising the following steps: according to the water conservancy facilities and hydrological elements in the basin, the system sorts out the spatial distribution of key nodes and their hydraulic connection, constructs a flood control scheduling topological network, and obtains a reservoir group flood control optimal scheduling topological graph; a reservoir group flood control optimal scheduling quadratic programming model is established according to the reservoir group flood control optimal scheduling topological graph; based on the MINOS library of GAMS, the least gradient method and quasi-Newton method are used to optimize and solve the reservoir group flood control optimal scheduling quadratic programming model. The present application couples the propagation of flood between reservoir groups in the mathematical equation set, avoids complex simulation sequential coding, is easy to model, has good universality, and the model is easy to seek an optimal solution, can balance the reasonable scheduling of the flood control capacity, peak shaving effect and reservoir storage capacity of the reservoir, and improve the accuracy and efficiency of flood regulation.

Disclosed in the present invention are an iterative initial value determination method and apparatus for device simulation, and a device and a storage medium. The method comprises: on the basis of Newton's method, establishing an iterative calculation model for device simulation; on the basis of a change step length of an external voltage, determining a boundary-value condition ψi for an i-th instance of simulation; inputting into the iterative calculation model an iterative initial value yi for the i-th instance of simulation and the boundary-value condition ψi for the i-th instance of simulation, and determining an iterative optimal solution xi for the i-th instance of simulation; on the basis of the iterative optimal solution xi for the i-th instance of simulation, performing conversion to obtain a quasi-Fermi potential, performing Jacobian extrapolation on the quasi-Fermi potential obtained by performing conversion on the basis of the iterative optimal solution xi for the i-th instance of simulation, and determining an iterative initial value yi+1 for an (i+1)th instance of simulation; and repeating the execution of determining the boundary-value condition ψi for the i-th instance of simulation, so as to perform the next simulation process. The technical solution in the embodiments of the present invention can effectively improve the convergence speed of performing solving on the basis of Newton's method in each simulation process.

The invention discloses a multi-body virtual power plant collaborative optimization scheduling method, system and device and a medium. The method comprises the steps that related parameters of a virtual power plant system are acquired to construct a multi-body virtual power plant model; taking each subject in the virtual power plant system as an independent decision-making player through a state game potential algorithm, obtaining a state space, an action variable and a state transfer function of each subject, and constructing a state potential game model; and solving the state potential game model through a modified Newton method and a particle swarm algorithm, and carrying out continuous interactive iteration to obtain an optimal cooperative scheduling strategy of each main body of the final virtual power plant system. According to the method, multi-subject decision-making equality and privacy protection are realized, flexible load optimization is realized, unified optimization modeling of multiple types of subjects is supported, the algorithm is distinct in hierarchy and high in convergence, the collaborative regulation and control level of multi-type subject resources of the virtual power plant is effectively improved, double improvement of system economic benefits and scheduling fairness between subjects is realized, and the method is suitable for large-scale popularization and application. And technical support is provided for actual operation of the virtual power plant.

The application relates to an intelligent power regulation method and device based on temperature stability, wherein the intelligent power regulation method comprises the following steps: collecting temperature data of a device to be monitored at equal intervals in real time, calculating a stable temperature data proportion in a detection period, and if the stable temperature data proportion is less than a preset proportion, performing regression calculation on all power-temperature data in the detection period to obtain a power-temperature function F; obtaining a target temperature value T target constructing an iterative function f; according to the current power P N iterative root seeking is performed on the iterative function f to obtain a target power, and the device output power is adjusted according to the target power; based on the iterative idea of the Newton method, the application realizes fast approximation of the target power in the power regulation process, realizes high-precision control of temperature stability and power output, does not need manual intervention, and improves experimental efficiency and result stability.

The invention relates to the technical field of geophysics, in particular to a high-speed rail seismic source wavelet estimation method and system for full waveform inversion. The method comprises the steps that unit impulse response data and high-speed rail seismic source vibration data are acquired, and forward modeling calculation is conducted on the unit impulse response data and the high-speed rail seismic source vibration data through a finite difference method to obtain unit impulse response signals and high-speed rail seismic source vibration signals. According to the method, simplified modeling is carried out on a high-speed rail vibration source running on a viaduct, a train is equivalent to a plurality of moving point force sources excited at piers, a vibration source function formed by delay superposition of a plurality of axle load functions is constructed, and the spatial and temporal variation characteristics of high-speed rail vibration are accurately described. In the frequency domain, the Newton method is utilized to directly solve the real part and the imaginary part of the seismic source wavelet, and an objective function has a linear characteristic, so that an analytical solution can be obtained in one step, the iterative convergence problem is avoided, and the calculation cost is remarkably reduced.

The invention relates to a structure damage identification method of a quasi-Newton iteration nonlinear RCD class algorithm, and the method comprises the following steps: S1, extracting modal parameters of a structure before and after damage, and building a mathematical model of the structure according to the extracted modal parameters; s2, performing second-order sensitivity analysis on the structural model, analyzing the influence of structural parameter change on structural response, and establishing a nonlinear sensitivity equation based on a sensitivity analysis result; s3, constructing a linear optimization model for damage identification by utilizing the nonlinear sensitivity equation obtained in the step S2 and combining a sparse regularization method; on the basis of the constructed optimization model, proposing an optimization algorithm for solving the model; s4, solving an NIT omega-RCD theta algorithm based on the nonlinear optimization model in the step S3 in combination with a Broyden rank-1 correction Newton method and an RCD type rapid optimization algorithm; the problems that an existing linear model cannot fully consider the nonlinear characteristics of the structure, the recognition result is likely to be inaccurate, and the damage recognition reliability is affected are solved.

This invention relates to the field of evaporative cooling technology and discloses a method for calculating the circulating flow rate of a wind turbine evaporative cooling system. The method includes: simplifying the geometric structure of the wind turbine evaporative cooling system by equivalent means, reducing the multi-channel evaporative cooling loop to a closed loop composed of n parallel heating channels; establishing a two-phase fluid dynamics model based on a one-dimensional homogeneous equilibrium model to obtain the calculated relationships of pressure drop, frictional resistance, and two-phase flow parameters for each heating channel; expressing the pressure drop equilibrium condition of each heating channel as an implicit nonlinear equation set in the two-phase fluid dynamics model, and obtaining the mass flow rate solution using an iterative solution method; and employing a hybrid solution strategy combining particle swarm optimization (PSO) and Newton's method to calculate the circulating flow rate. This invention combines PSO and Newton's method to form a two-stage hybrid solution strategy, which is an effective way to solve the problem of multiple solutions for the circulating flow rate of evaporative cooling systems.

The present invention discloses a fast decomposition method for power flow calculation suitable for research purposes, and provides a Newton method for power flow calculation that is easy to modify and maintain for scientific researchers who conduct further research based on the fast decomposition method for power flow calculation. For an n-node power system, the node number is independent of the node type and can be arbitrarily numbered. The coefficient matrices B′ and B″ of the power flow calculation are stored as n×n. The elements of the rows corresponding to the balance node ΔP and the columns corresponding to Δθ in B′ are all 0, and the elements of the rows corresponding to the PV node or the balance node ΔQ and the columns corresponding to ΔU in B″ are also all 0, but the diagonal elements are not cleared to zero, and the original calculated values ​​are retained. In this way, the linear equation triangular decomposition method with excellent performance provided by the programming language can be used to solve the modified equation for power flow calculation, which reduces the programming difficulty, improves the calculation speed, and ensures the reliability of the algorithm.

The application discloses a multi-region power system scheduling method based on Newton method approximate dynamic programming, and comprises the following steps: S1, a multi-region power system scheduling model is constructed, a power system scheduling cost minimization is taken as an objective function, the multi-region power system scheduling model is divided into a plurality of regional sub-models, and a value function of each regional sub-model is determined; S2, the value function of each regional sub-model is subjected to second-order Taylor expansion based on the Newton method, and an approximate value function of each regional sub-model is obtained; S3, derivatives of the approximate value function of each regional sub-model in the multi-region power system scheduling model are calculated, and a second-order derivative matrix and a first-order derivative matrix of the approximate value function of each regional sub-model are obtained; S4, an approximate dynamic programming method based on the Newton method is adopted to update the approximate value function of each regional sub-model, an optimal scheduling strategy of the multi-region power system scheduling model is obtained, and the power system is scheduled according to the optimal scheduling strategy. The solving speed and accuracy are improved.

The invention discloses an L0 factor analysis method based on a Newton interior point algorithm, and the method comprises the steps: generating a random sample based on a first factor model with noise; obtaining a covariance matrix of the random sample; according to the covariance matrix, combining KL divergence and L0 norm to construct a first optimization problem with positive semidefinite constraint; converting the first optimization problem into an unconstrained second optimization problem according to a logarithmic barrier function; vectorizing the second optimization problem to obtain a third optimization problem with unconstrained vector variables; and through a Newton method, according to the third optimization problem, obtaining estimation of the number of load factors and a heterogeneous noise covariance matrix. The method can significantly improve the efficiency of data analysis and optimization, and can be widely applied to the technical field of data optimization.

The application relates to an overhead line stress calculation method based on a hyperbolic function method, which comprises the following steps: constructing an overhead line stress state equation; calculating the value of an intermediate parameter through the overhead line stress state equation; when the intermediate parameter is greater than or equal to 0, converting the stress state equation into a hyperbolic sine function, further converting the hyperbolic sine function and obtaining a stress initial value, and then according to the stress initial value, using Newton's method to iteratively obtain a to-be-solved working condition stress value; when the intermediate parameter is less than 0, converting the stress state equation into a hyperbolic cosine function, further converting the hyperbolic cosine function and obtaining a stress initial value, and then according to the stress initial value, using Newton's method to iteratively obtain a to-be-solved working condition stress value.

The application provides a multi-point approximation method based on an approximate Hessian matrix for structure optimization, comprising the following steps: step 1, establishing a finite element model of a spacecraft based on an initial design of a spacecraft structure; step 2, performing mechanical prediction of the spacecraft, and the analysis mode comprises static analysis, modal analysis, buckling analysis and transient response; step 3, establishing a structure optimization mathematical model, namely, defining an optimization target, a constraint function and a design variable; step 4, establishing a sequence approximation problem considering second-order information to approximate the original problem; step 5, estimating the Hessian matrix of the multi-point approximation function by using a quasi-Newton method; step 6, selecting known points by using a neighborhood selection strategy based on the Euclidean distance; step 7, establishing convergence discrimination and an automatic optimization system; step 8, method comparison, analysis and iteration explicitness; and setting an optimization example to illustrate the flow from spacecraft design to analysis and then to optimization, and the effectiveness is verified by comparing the results of the optimization method with those of a commercial software.

The invention discloses a power system load flow calculation method and system based on GPU parallel acceleration, and belongs to the field of power systems, and the method comprises the steps: building a power system load flow model, and carrying out the compression coding of the power system load flow model, and obtaining a power system load flow mathematical model; executing parallel acceleration nonlinear load flow calculation by utilizing a GPU, specifically, transmitting an admittance matrix, an initial voltage and a node power parameter to a GPU video memory, and performing parallel calculation on a node power mismatch amount by utilizing the GPU based on a Newton method iteration framework; calculating a power voltage partial derivative matrix based on the admittance matrix, and slicing the power voltage partial derivative matrix through sparse matrix multiplication to generate a Jacobian matrix; combining the Jacobian matrix with the vector of the node power mismatch quantity to form a linear equation set, calling a GPU linear equation set solver to solve the linear equation set to obtain a voltage correction quantity, and updating a node voltage amplitude and a phase angle; and iteratively executing until the node power mismatch amount converges to a preset tolerance, and outputting the voltage phase angle of each node.

The application discloses a multi-focus image fusion method and system based on a semi-smooth Newton method, and belongs to the technical field of image processing. The semi-smooth Newton algorithm is used for solving, a proximal operator and a generalized Jacobian matrix are introduced, and the optimization solving process is expanded into a deep network. An adaptive feature iterative reconstruction unit is designed, auxiliary variable updating, a learnable edge convolution, focus gradient perception and a multi-scale frequency enhancement structure are integrated, and iterative optimization is realized in combination with a feature reconstruction block, a multiplier updating module and a matrix updating module. The second-order optimization algorithm is introduced into multi-focus image fusion for the first time, has physical interpretability and strong feature learning capability, and has better fusion effect than existing methods, and has important application value in high-precision imaging fields such as microscopy and remote sensing.

A model prediction-based control method for grid forming of multi-port autonomous reconfigurable solar plants includes: estimating and predicting system parameters by establishing a multi-port autonomous reconfigurable solar plant and a dynamic model of a synchronous generator model; converting an objective function into an unconstrained optimization problem, using Newton's method to achieve minimum computational burden within each calculation time step to find a solution in real time for obtaining an optimal angular frequency; based on results of the optimal angular frequency, updating an output voltage and a dq-axis current of the multi-port autonomous reconfigurable solar plant, and changing an arm modulation index of the plant, thereby realizing the plant's inertia and primary frequency modulation support. The model prediction-based control method provided by the present invention achieves rapid prediction during operation and improves frequency response, rapidity and system stability.