130 results about "Conjugate gradient method" patented technology

The invention relates to the technical field of geophysics, and particularly discloses a comprehensive inversion method and system based on bidirectional magnetic susceptibility weighted constraint, and the method comprises the steps: dividing an underground three-dimensional space, and building two different cuboid models; determining a total magnetic field observed on the ground and gradient anomaly thereof based on the observation point coordinates and the cuboid model position, and establishing a kernel function matrix; determining an inversion objective function reflecting the relationship between the total magnetic field and the gradient anomaly thereof and the physical property parameters; adding the bidirectional magnetic susceptibility weighted constraint function into an inversion objective function; the optimal solution of the comprehensive inversion target function is solved based on the conjugate gradient method, the high-level resolution capability of the magnetic gradient data and the deep detection advantage of the total magnetic field data are fused, the precision of fine depiction of the superficial anomalous body boundary and the reconstruction precision of the deep field source structure are remarkably improved, a vector cross multiplication regularization constraint mechanism is introduced, and the method has the advantages of high resolution and high resolution. The inversion multiplicity under the complex geological condition is greatly inhibited, and the reliability of the model result and the geological rationality are enhanced.

The invention discloses a semiconductor film performance cooperative control method and system based on multi-parameter coupling, and belongs to the technical field of semiconductor material manufacturing, and the method specifically comprises the steps: determining a control parameter set containing plasma power density and other parameters, and building a multi-parameter coupling response model; generating sample points by using Latin hypercube sampling, quantifying a parameter interaction effect, and forming a dynamic weight matrix in combination with a graph model; monitoring data such as plasma emission spectrum intensity ratio in real time, comparing the data with a model predicted value, and correcting the model; a multi-objective optimization problem is constructed based on the correction model and the dynamic weight matrix, and optimal parameter configuration is solved by using a conjugate gradient method; alternately applying a high-frequency pulse and a relaxation stage according to the optimal configuration, and modulating the pulse frequency according to a Fibonacci sequence; and finally, based on the substrate temperature, planning a temperature rise path by using an S-shaped curve, superimposing cosine modulation, and introducing oscillation compensation gas to compensate the reactant concentration deviation, thereby realizing accurate optimization of the material preparation process.

The invention provides an electricity-hydrogen fusion time sequence production simulation method, which comprises the following steps of: constructing an energy supply framework model of a new energy station, decomposing an original hydrogen load sequence through a variational mode decomposition method, and establishing an electricity-hydrogen fusion operation model of which the target is operation cost through the energy supply framework model, sparse dimensionality reduction and linear programming solving are carried out on the time sequence production simulation result, and a conjugate gradient asynchronous parallel computing method is used for solving and outputting the time sequence production simulation result. According to the method, an electricity-hydrogen fusion time sequence production simulation model of a mixed time scale is constructed, and the self-adaptive step length of a low-frequency component planned hydrogen separation energy storage scheduling period solved by decomposing a hydrogen load is utilized, so that the stability of external output power of a long-time-scale upper field station is guaranteed through a synergistic effect of the self-adaptive step length and real-time balanced electric energy; the operation economy is improved; and then aiming at electro-hydrogen large-scale fusion matrix sparse dimensionality reduction, parallel calculation of a conjugate gradient method is utilized, so that the solving speed is increased, and the accuracy of a simulation result can be ensured.

The invention discloses a coal seam gas pressure while-drilling flow inversion method based on a conjugate gradient method. The method comprises the steps that firstly, a while-drilling gas flow mathematical model is constructed, and model parameters are determined; screening the field measured value of the gas flow while drilling; carrying out finite difference method forward modeling on the model, and calculating the drilling gas flow; constructing an objective function and calculating the gradient of the objective function by combining the calculated gas flow and the field measured value of the gas flow; and finally, on the basis of a regularization nonlinear conjugate gradient method, continuous iteration processing is carried out on the target function in combination with the search direction and the search step length, and an original coal seam gas pressure value is obtained through inversion output of the finally-iterated target function. The method is simple in implementation step, the inversion result is determined through test verification and has good accuracy and reliability, the pressure measuring time is shortened, the pressure measuring cost is reduced, the coal seam gas pressure measuring accuracy is improved, and the actual requirement of a coal mine for rapidly and accurately measuring coal seam gas parameters is met.

The invention discloses a least square reverse time migration method and device for a three-dimensional anisotropic medium, and the method comprises the steps: building a forward equation of the three-dimensional anisotropic medium for a seismic block to be subjected to reverse time migration, and obtaining a background wave field under the three-dimensional anisotropic medium through regional forward modeling; determining a three-dimensional anisotropic medium adjoint wave field by using a pre-established wave equation of the adjoint wave field; according to the adjoint wave field and the background wave field, determining an initial gradient by using a pre-established least square reverse time migration cross-correlation equation, and obtaining a migration result according to the initial gradient; and updating an imaging result through a conjugate gradient method according to the offset result to obtain a reflected wave record.

The invention provides a sparse system identification method and system based on an improved adaptive filter, and belongs to the technical field of adaptive system identification, and the method comprises the steps: obtaining an input signal at a current moment; updating a correlation estimation matrix and a cross-correlation estimation vector based on a conjugate gradient method; a nonlinear gradient is introduced, and the search direction of the filter is updated; introducing a regularization parameter to obtain a dynamic step length of the filter; updating the tap weight coefficient of the filter; and updating the moment to the next moment, updating the weight vector coefficient of the adaptive filter, and when the weight vector coefficient of the filter is equal to the pulse response of the unknown system, ending the identification of the unknown system. According to the method, the sparse unknown system can be identified more efficiently by modifying nonlinear gradient updating, regularization parameters and momentum enhancement updating.

The invention discloses a main lobe gain maximization method for an analog-digital hybrid array of a radar communication system. According to the method, the ratio of the maximum minimum main lobe level to the peak side lobe level is used as a target function, and under the main lobe ripple constraint, the simulation weight constant modulus constraint and the digital weight power constraint, the ADMM (Alternating Direction of Multiplexing) is used for carrying out joint design on the simulation weight and the digital weight. In the ADMM framework, a Riemann conjugate gradient method is adopted to solve a simulation weight subproblem with constant modulus constraint; and solving a digital weight by adopting a Lagrange multiplier method. A numerical result shows that the method can generate better main lobe ripples and lower peak side lobe level, and has good beam directional diagram accurate control performance.

The invention discloses a large-scale MIMO multi-satellite positioning and direction estimation method and system based on angle information. The method comprises the following steps: firstly, obtaining channel matrix estimation between each satellite and a user by sending a pilot signal; then, obtaining estimated values of channel parameters by using a tensor-ESPRIT algorithm, wherein the estimated values comprise an angle of departure and an angle of arrival; according to the estimated departure angles, direction vectors between the user and all satellites are obtained, an optimization problem on spherical manifold is established, altitude constraints are considered, and a Riemann conjugate gradient method is used for solving and obtaining a user position estimation value; and finally, according to the estimated user position and arrival angle, establishing an optimization problem on the SO (3) manifold, and solving by using a Riemannian gradient descent method to obtain an estimated value of a user array rotation matrix. According to the method, positioning and direction estimation are performed by combining channel information between a plurality of satellites and users, and altitude constraints are considered during positioning, so that compared with single-satellite and non-constraint conditions, the positioning performance can be remarkably improved, and meanwhile, the method has good direction estimation performance.

The invention relates to the technical field of structure optimization, in particular to a topological optimization method and device for a GPU acceleration fiber reinforced composite material structure, and the method comprises the steps: obtaining parameters such as material attribute parameters and design domain size; 21 basic stiffness matrixes are calculated; respectively filtering the density variables and the angle variables; a geometric multi-grid preprocessing conjugate gradient method iteration solver is adopted to calculate the current displacement of each node of the densest grid level; and calculating an objective function according to the current design variable and the node displacement, performing sensitivity analysis on the objective function and the constraint condition to update the density and angle variables, judging whether a topological optimization iteration convergence condition is met or not, and outputting an optimization result if the topological optimization iteration convergence condition is met. Therefore, the problems that the stiffness matrix of each element in the topological optimization problem of the large-scale fiber reinforced composite material structure cannot be stored due to the limited memory of the global memory of the GPU, and the calculation efficiency of the stiffness matrix of the dynamic calculation element in the finite element solving process is low are solved.

The invention discloses a rapid and direct solving method for a metal medium composite target based on a block sparse row matrix. The method comprises the following steps: approximately representing a low-rank non-diagonal block matrix as multiplication of two decomposition matrixes U and V by adopting a self-adaptive crossover algorithm; cascading the decomposition matrix of each sub-block to obtain an integral decomposition matrix; carrying out compression storage on the decomposition matrixes U and V by adopting a block sparse row matrix, and removing zero blocks generated by matrix cascading; performing inversion on the block diagonal matrix of the finest layer through a conjugate gradient method, and solving an inverse matrix of the block diagonal matrix of the non-finest layer according to an SMW formula; and finally, obtaining a current solving target RCS through vector multiplication of an inverse matrix of a series of block diagonal matrixes and a matrix of a right vector. According to the method, the electromagnetic scattering characteristics of the complex target are solved, and the calculation efficiency of analyzing the electromagnetic single-station scattering characteristics of the complex target can be improved.

The embodiment of the invention relates to the technical field of silicon wafer processing, and discloses an intelligent self-adaptive silicon wafer polishing damage improvement method, system and equipment. Comprising the following steps: capturing a multi-physical field signal in a silicon wafer polishing process in real time, extracting a feature vector strongly related to mechanical damage, and outputting to obtain a fusion feature tensor; mapping the fusion feature tensor into three-dimensional damage field distribution, quantitatively describing a spatio-temporal evolution law of silicon wafer subsurface mechanical damage, and completing real-time reconstruction of a damage field; performing conjugate gradient optimization control on the generated damage field, performing damage minimization and processing efficiency balance processing, and generating an optimization process parameter adjustment instruction vector; and performing grinding wheel adjustment based on the technological parameter adjustment instruction vector, and performing damage inhibition in the silicon wafer polishing process in real time. In this way, silicon wafer technical parameters are obtained through multi-source sensing, silicon wafer damage improvement is carried out through the conjugate gradient method, and the requirements for silicon wafer damage suppression and mechanical system stability are met.

The invention relates to the technical field of interface thermal resistance calculation, and discloses a method for measuring and calculating interface thermal resistance based on an infrared imaging technology, which comprises the following steps: fixing and aligning paired upper and lower test pieces to form a to-be-measured contact interface, respectively preheating to a preset initial temperature difference and stabilizing, and monitoring and recording temperature change and test piece thermophysical parameters; enabling the test piece to be in contact, starting infrared imaging equipment to record full-field transient temperature data in a temperature equalization process, and forming a three-dimensional data matrix; building a thermal resistance correlation model, constructing a heat conduction equation in combination with the parameters to solve a simulated temperature field, and inverting related heat conduction coefficients by using a conjugate gradient method; and calculating microscopic and total contact thermal resistance according to the inversion coefficient and the geometric parameters, and deducing macroscopic thermal resistance. According to the method, non-intrusive transient infrared temperature measurement is combined with a reverse heat transfer algorithm, so that the microscopic thermal resistance, the macroscopic thermal resistance and the total contact thermal resistance are accurately distinguished and quantified.

The invention discloses a two-dimensional variational data assimilation method based on comprehensive advection constraint, and belongs to the technical field of data assimilation processing, and the method comprises the following steps: obtaining target quantity background field data and satellite or buoy scatter observation data at the target depth of a target sea area under the corresponding target time, and carrying out the horizontal processing of the obtained data; constructing a cost function, specifically, introducing an advection constraint term into a traditional cost function; the cost function is minimized through a conjugate gradient method, the obtained analysis result is a flat field, the flat field is superposed with an original background field, and a finally reconstructed target quantity analysis field is obtained and serves as a forecast initial field. According to the method, firstly, the obtained data is subjected to distance leveling processing, so that the influence of a long-term average state in the data is eliminated, and the assimilation accuracy is improved; and meanwhile, an advection constraint term is introduced into the cost function, so that physical consistency of assimilated physical quantities can be kept when the assimilated physical quantities move along with the fluid, and the precision of data forecasting is improved.

The invention discloses a power transmission line three-dimensional meteorological field reconstruction method and system considering terrain influence, and the method comprises the steps: constructing a physical constraint projection function according to a terrain normal vector and an actual measurement value of each station in a power transmission line; constructing a computational physical operator system oriented to sparse sites; constructing a variational optimization objective function of each station according to the physical constraint projection function and the computational physical operator system; constructing a POD basis matrix according to historical data, and performing iterative solution by adopting a preprocessing conjugate gradient method according to the POD basis matrix to obtain a modal coefficient vector enabling the variational optimization objective function to be optimal, namely an optimal modal coefficient vector; and reconstructing a three-dimensional field according to the optimal modal coefficient vector, and synchronously calculating a space gradient. Reconstruction accuracy, physical consistency and reliability can be improved, and the requirements for a high-precision three-dimensional meteorological field in multiple aspects such as severe convection weather monitoring and early warning of a power transmission line and weather forecast verification can be met.

The present application relates to a kind of based on position dynamics adaptive anisotropic cloth simulation method, system, electronic equipment and storage medium, belong to computer graphics physics simulation field.The method includes setting cloth grid;Topology relationship of grid is calculated, and the set of point edge surface is formed;According to Newton's law, the linear system of external force is constructed, and change coordinate is solved using conjugate gradient method;Using graph coloring method, data dependence is decoupled to grid, using the method of position dynamics, strain constraint iteration parallel computing is carried out to change coordinate, and the coordinate correction amount after cloth motion deformation is obtained;According to the revised coordinate, grid tensor field is calculated, grid dynamic refinement and coarsening are carried out, and non-uniform anisotropic cloth grid is formed.The present application uses position dynamics method to calculate dynamic non-uniform grid, simulates the anisotropy of cloth deformation, retains the dynamic behavior of cloth wrinkle under fine scale, is quickly solved on the basis of not losing simulation details, satisfies real-time.

This invention discloses an online super-resolution imaging method for scanning radar based on the minimization criterion. By establishing an azimuth echo convolution model and discretizing it, the super-resolution problem is transformed. Under a regularization framework, the minimization criterion is used to construct an analytically optimizeable cost function containing sparse prior information, transforming the forward-looking imaging resolution improvement problem into an optimization estimation problem. By dividing the antenna measurement matrix into rows and blocks, matrix operations are degraded into the continuous accumulation of vector operations, enabling real-time online updates of targets in the forward-looking region. Finally, the conjugate gradient method is used to degrade and invert the matrix, outputting the super-resolution result. This method can achieve rapid imaging with limited system memory through an online conjugate gradient super-resolution algorithm based on the minimization criterion. Compared with existing batch processing regularization methods, it eliminates false targets, significantly reduces computational complexity, and is beneficial for rapid imaging of scanning radar.

The invention relates to the technical field of structure optimization, in particular to a topological optimization method and device for a large-scale fiber reinforced composite material structure, and the method comprises the steps: obtaining material attribute parameters and the like; calculating a unit stiffness matrix corresponding to each group of sampling angles according to a preset sampling interval and attribute parameters of a preset fiber reinforced composite material, calculating a template stiffness matrix of each grid level, and calculating the current displacement of each node of the densest grid level by adopting a geometric multi-grid preprocessing conjugate gradient method; and calculating a target function according to the current design variable and the current displacement of each node of the densest grid level, so as to update the design variable until a preset number of iterations is reached, and outputting a new optimization design. Therefore, the problems that due to the fact that the memory of a GPU global memory is limited, the element stiffness matrix cannot be stored in the topological optimization problem of the large-scale fiber reinforced composite material structure, and the calculation efficiency of the dynamic calculation element stiffness matrix in the finite element solving process is low are solved.

The embodiment of the invention discloses a motion state prediction method and device, and the method comprises the steps: obtaining a dynamic function corresponding to a target object, and enabling the dynamic function to be obtained according to a dynamic equation, and to be used for representing the relation between the motion state of the target object and time; determining a target function by using the kinetic function; performing iterative search on the dynamic variable of the target function by using a conjugate gradient method to minimize the value of the target function; and predicting the motion state of the target object by using the searched dynamic variable value. The motion state of the target object is predicted by using the conjugate gradient method, and the prediction efficiency of the motion state is improved.

The invention discloses a constant modulus waveform design method based on a penalty flow pattern optimization strategy, which relates to the technical field of radars, and comprises the following steps: for the problem of joint design of a constant modulus waveform and a filter of local fuzzy function shaping in single-input single-output radar system modeling, designing a constant modulus waveform and a filter; solving the problem of joint design of modeling based on a flow pattern accurate penalty method, and constructing an accurate penalty mechanism item for performing penalty optimization on a spectrum constraint condition; a plurality of inequality constraints are constructed into subitems of a target function through an accurate penalty function to eliminate the inequality constraints, and then a problem is projected to a CCM to obtain an equivalent complex quaternary unconstrained problem; the complex quaternary unconstrained problem is solved through a conjugate gradient method in a flow pattern space. The method provided by the invention shows better practicability in the aspects of radio frequency interference resistance, spectrum utilization, target detection and the like.

The invention provides a signal purity spectrum guided stratum absorption compensation method and system. The compensation method comprises the following steps: acquiring a seismic signal; estimating the seismic signal from the seismic record based on the spatial predictability of the seismic signal, and calculating a signal purity spectrum of the seismic record; constructing an unsteady-state filter representing the seismic signal absorption effect, wherein the unsteady-state filter describes the frequency change and the phase change of the seismic signal in the viscoelastic medium; introducing the signal purity spectrum into a regularization condition of an absorption compensation inversion system, and establishing the absorption compensation inversion system guided by the signal purity spectrum; and solving the inversion system by adopting a conjugate gradient method to obtain the seismic record after absorption compensation. Operation is simple, operation is stable, the effect is obvious, the stability of absorption compensation is enhanced, and the compensation precision of seismic signals is improved.

The invention discloses a sea target depth map recovery method based on a polarization image and a regularization technology, and belongs to the field of polarization image depth recovery, and the method comprises the following steps: obtaining an original polarization image of a target; based on the four-channel polarization image, according to an illumination estimation model and a Stokes parameter under mirror reflection and diffuse mixing illumination conditions, respectively obtaining zenith angle and azimuth angle images of the target; carrying out polarization information calculation by adopting an analog polarization method to obtain a non-light-intensity polarization image of the target; determining a target surface normal by referring to the polar coordinate system of the target surface normal and using a light source dot product principle, and further obtaining a normal vector field image; gradient information of a normal vector field image is converted into a large discretization sparse matrix under a Poisson equation, on the basis of solving the Poisson equation through a biconjugate gradient method, an adaptive fusion regularization formula is proposed to stably solve the Poisson equation, and depth image recovery of the marine target is completed.

The present invention discloses a high-precision flow-induced vibration simulation method based on FETI. First, the corresponding example data is read in, and the NewMark method is used to numerically discretize the flow-induced vibration dynamic process. Secondly, the FETI method is used to perform parallel decomposition on the discretized equations, and the subdomains are glued together by Lagrange multipliers at the partition boundaries. A graph bipartition algorithm for domain boundary balance is proposed to balance the number of elements and the computational amount in each subdomain, ensuring load balance among processes. Finally, the preconditioned conjugate gradient method is used for iterative solution. After iterative solution, according to the obtained corresponding displacements, the NewMark method is used to update the time steps of the flow-induced vibration process. The present invention has completed the solution of grid data on the scale of hundreds of millions, improved the efficiency of solving large-scale flow-induced vibration problems, realized the fast and efficient simulation of flow-induced vibration, and ensured load balance among processes.

The invention provides a super-large-scale MIMO system beam focusing method based on an adaptive time delay-phase structure, and relates to the technical field of radio communication, comprising the following steps: constructing an adaptive time delay-phase structure suitable for a super-large-scale MIMO system, and establishing a near-field beam focusing joint optimization mathematical model; solving an all-digital optimal beam focusing matrix through maximum ratio transmission; a switch matrix optimization problem is converted into a bipartite graph matching problem, and an optimal matching matrix is solved by using a Hungary algorithm; solving a phase shift matrix through a Riemann spectrum conjugate gradient method; optimizing the time delay matrix by using a gradient descent method; optimizing the digital precoding matrix by using a least square method; and giving a base station beam focusing matrix through alternate iteration, and completing beam focusing of the super-large-scale MIMO system. According to the structure provided by the invention, dynamic sharing with the antenna array elements can be realized by utilizing the switching network, so that the adaptive capacity of hybrid precoding to a channel environment is effectively enhanced.

The invention relates to a magnetic resonance spectrum reconstruction method based on a structured matrix null space, and belongs to the field of undersampling and reconstruction of magnetic resonance spectrums. The method comprises the following steps: acquiring calibration data from a full sampling region of a non-uniform sampling time-domain magnetic resonance spectrum signal to be reconstructed; rearranging and constructing a structured matrix on the calibration data through a sliding window; calculating a null space based on singular value decomposition of the structured matrix; constructing a magnetic resonance spectrum reconstruction model containing a data consistency item and a null space constraint item; and solving a signal to be reconstructed by adopting a conjugate gradient method. According to the method, the null-space constraint relation is constructed by using the low-rank characteristic of the time-domain magnetic resonance spectrum signal under the structured matrix, so that rapid and high-precision magnetic resonance spectrum reconstruction is realized at a low sampling rate. The method is excellent in effect and can be applied to one-dimensional, two-dimensional and higher-dimensional undersampled magnetic resonance spectrum reconstruction.

The invention discloses a non-intrusive transient contact numerical calculation method based on a penalty function and a conjugate gradient, and relates to the field of structural impact dynamics numerical simulation, and the method comprises the steps: for a current calculation time step, estimating a displacement prediction value based on the motion state parameters of two collision objects in a previous calculation time step, and calculating the displacement prediction value; determining a contact gap prediction value of the current calculation time step; a penalty function method is adopted to carry out contact initial calculation to obtain a contact force estimated value, and a contact gap is updated; when the updated contact gap does not meet the non-intrusion condition, a preprocessed conjugate gradient method is adopted to carry out contact secondary calculation to obtain a contact force correction value; and determining the contact force of the current calculation time step based on the contact force estimated value and the contact force corrected value, and entering the next calculation time step after updating the motion state parameters. According to the method, interface non-intrusive calculation is achieved, and meanwhile high convergence and calculation efficiency are achieved.

The invention discloses a radiotherapy plan automatic optimization design method based on machine learning, and relates to the technical field of radiotherapy, and the method comprises the steps: firstly collecting and preprocessing radiotherapy core data to generate a training set, training feature extraction, dose prediction and optimization adjustment of a model; processing to-be-processed data through a feature extraction model, screening three optimal similar cases in combination with cosine similarity, and extracting effective features; based on the feature vectors and the effective features, outputting an initial dose plan, constraint parameters and a deposition matrix by a dose prediction model; and finally, constructing a target function, and iteratively optimizing the radiation field parameters and the photon flux by adopting an improved ant colony algorithm and a conjugate gradient method. According to the method, clinical experience and individual differences are fused, the radiotherapy plan is efficiently and accurately generated, and the quality stability and clinical suitability of the plan are improved.

This application provides a bridge dynamic weighing algorithm based on maximum entropy regularization. The steps include: First, obtain the measured response M of the bridge when the vehicle crosses the bridge m and the influence line matrix IL; Second, use M m and IL to calculate the initial value A of the axle weight 0 , and define the initial value r of the regularization parameter i=1 , where i represents the number of times of the main loop; Third, enter the sub-loop, substitute r i and A 0 into the iterative formula of the non-linear conjugate gradient method for calculation until the axle weight converges, and the absolute value of the difference between the axle weights obtained from the previous and the current iterations is less than e, that is: |A i k - A i k‑1 | < e. Assign the axle weight A i k at the k-th iteration after the convergence of the sub-loop to A i ; Fourth, substitute A i into the Regińska formula to obtain the parameter V, and calculate the next regularization parameter r i+1 ; Fifth, judge whether i is greater than 10. If not, substitute r i=i+1 into step three, and repeat the calculation process of steps three to five; If so, draw the r-V curve; Sixth, observe whether there is a minimum value in the curve. If not, substitute r i=i+1 into step three, and repeat the calculation process of steps three to five; If there is, output the axle weight corresponding to the minimum value and use it as the axle weight identification result.

The application provides a kind of based on adaptive time delay-phase structure's super large scale MIMO system beam focusing method, it is related to radio communication technical field, including the following steps: constructing adaptive time delay-phase structure suitable for super large scale MIMO system, and establishing near-field beam focusing joint optimization mathematical model;Through maximum ratio transmission solving all digital optimal beam focusing matrix;Switch matrix optimization problem is converted into two-part graph matching problem, and optimal matching matrix is solved using hungarian algorithm;Through riemann spectrum conjugate gradient method solving phase shift matrix;Utilize gradient descent method to optimize time delay matrix;Least square method is used to optimize digital precoding matrix;Through alternate iteration, base station beam focusing matrix is given, and the beam focusing of super large scale MIMO system is completed.The structure provided in the application can be realized with switch network and the dynamic sharing of antenna array element, so as to effectively enhance the adaptive ability of hybrid precoding to channel environment.

This invention discloses a method and system for reconstructing a three-dimensional meteorological field for transmission lines considering the influence of terrain. The method includes: constructing a physically constrained projection function based on the terrain normal vectors and measured values ​​of each station in the transmission line; constructing a computational physics operator system for sparse stations; constructing a variational optimization objective function for each station based on the physically constrained projection function and the computational physics operator system; constructing a POD basis matrix based on historical data, and iteratively solving the POD basis matrix using the preprocessed conjugate gradient method to obtain the modal coefficient vector that optimizes the variational optimization objective function, i.e., the optimal modal coefficient vector; reconstructing the three-dimensional field based on the optimal modal coefficient vector, and simultaneously calculating the spatial gradient. This method can improve the accuracy, physical consistency, and reliability of the reconstruction, meeting the needs of high-precision three-dimensional meteorological fields in various aspects such as monitoring and early warning of severe convective weather along transmission lines and verifying meteorological forecasts.

The invention provides a water-cooled wall temperature field reconstruction method based on a tube panel heat transfer network model, which comprises the following steps of: obtaining a geometric structure of a water-cooled wall tube panel, and mapping the water-cooled wall tube panel into a two-dimensional regular grid according to computational nodes; establishing a heat balance equation corresponding to the node according to the heat transfer data and the equivalent heat resistance; synthesizing the heat balance equations corresponding to the computational nodes of the upper actual installation measuring points of the water wall tube panel into linear equations, and representing the linear equations as a solution matrix; temperature measurement data are used as boundary conditions to be implanted into the solution matrix; solving the solving matrix according to a preprocessing conjugate gradient method; and mapping the temperature values in the vectors obtained by solving back to the geometric structure of the water-cooled wall tube panel according to node coordinates, and outputting a temperature field data set of the water-cooled wall tube panel. According to the method, the physical structure of the water wall tube panel is abstracted into the regular heat transfer network, the node-level linear equation is established based on the heat balance principle, and the whole-field temperature distribution of the water wall is reconstructed only through existing sparse measuring points.