62 results about "Matrix optimization" patented technology

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) robust adaptive fusion method for high-precision three-dimensional surface deformation monitoring, which belongs to the technical field of space geodetic survey and remote sensing, and comprises the following steps of: constructing a time sequence deformation field based on a small baseline set time sequence InSAR processing technology, and resampling GNSS observation data to a grid consistent with the InSAR by using Kriging spatial interpolation; a Helmert variance component estimation method is adopted to adaptively estimate variance components of InSAR and GNSS data, and reasonable weight fixing of a multi-source observation value is achieved; iterative reweighted least square estimation is introduced, outlier influences in various observation data are dynamically restrained through a Tukey double-weight function, robust optimization of a fusion model is achieved, and therefore a high-precision three-dimensional deformation field is reconstructed. The method comprises the steps of Helmert weight fixing, IRLS robust, variance component updating and a closed-loop feedback mechanism of weight matrix optimization.

The invention relates to the technical field of power system stability control, in particular to a new energy station transient voltage and frequency coordination stability control method, which comprises the following steps of: analyzing a transient energy evolution rule of an equivalent synchronous motor and stored energy by constructing an alternating current and direct current hybrid dynamic model and fusing with multi-time scale characteristics; based on dynamic weight matrix optimization and a multi-target coordination control strategy, self-adaptive balance of power distribution is realized; a nonlinear feedback algorithm and a real-time compensation mechanism are designed in combination with the Lyapunov theory, and voltage frequency fluctuation in a disturbance scene is suppressed; establishing a multivariable cooperative control architecture, and dynamically adjusting a power adjustment priority and an inertia time constant through coupling iterative optimization of a source storage cooperative strategy to realize adaptive compensation of a transient energy propagation path; according to the method, the dynamic stability and the anti-interference capability of the new energy station under transient impact are remarkably improved, and safe operation of a high-proportion new energy power grid is guaranteed.

The invention relates to the technical field of machine vision and optical detection, in particular to a tea color automatic evaluation method and system based on machine vision. The method comprises the following steps: constructing a stable and repeatable optical measurement environment and calculating radiation baseline parameters; performing multi-band polarization sequence imaging on the tea sample to obtain original data with spectrum and structural characteristics; separating mirror surface and diffuse reflection components on the surfaces of the tea leaves, and performing spectrum consistency correction by combining a radiation baseline; performing multiband spectrum reconstruction and normalization processing on the basis of the corrected diffuse reflection image, mapping to a CIE Lab color space, extracting pixel-level brightness, chromaticity and color distribution statistical indexes, and generating a color feature vector in combination with spatial weighting matrix optimization; a final score is calculated by constructing a quantitative evaluation and grade judgment model and integrating perception color separation, drying calibration color separation and a penalty term, and a grade result is output. According to the method, objective, accurate and automatic evaluation of the color of the tea leaves is realized.

The invention discloses a multi-mode enterprise intelligent brain system based on artificial intelligence, and belongs to the technical field of big data processing. The method and the device are used for solving the technical problems of poor multi-modal data processing precision and poor system robustness in the existing scheme. Semantic alignment of multi-modal features can be realized through a closed-loop process of pre-extraction, comparative learning, dimension alignment, dynamic optimization and verification and fine adjustment, cross-modal semantic association is established through comparative learning, space mismatching is solved through dimension alignment, dynamic matrix optimization fits task requirements, and actual effects are ensured through verification and fine adjustment. Through a progressive process of confidence evaluation, task coding, weight calculation, feature fusion and end-to-end optimization, intelligent weighting of multi-modal features is realized, and through confidence evaluation, reliable data is screened, a decision target is determined through task coding, a gating network dynamically allocates weights, and end-to-end optimization ensures that a strategy meets service requirements.

The invention belongs to the technical field of tire engineering design, and discloses an engineering tire belted layer end structure finite element design analysis method and system. Comprising the steps of obtaining and preprocessing tire carcass contour data and initial belted layer parameters, conducting belted layer end parametric modeling on the basis of a standardized tire geometric model, constructing a durability finite element analysis model to obtain a stress-strain distribution field, conducting multi-parameter sensitivity analysis to determine a key influence factor weight matrix, and obtaining a stress-strain distribution field. And optimally designing a belted layer end structure based on the matrix, and finally performing durability verification and evaluation. According to the method, through accurate geometric expression, flexible parametric modeling, accurate performance analysis and multi-objective optimization design, belted layer end stress distribution uniformization and structural rigidity optimization are achieved.

The invention relates to the technical field of data processing, in particular to a digital twinning-based comprehensive energy efficiency analysis and judgment system for a high-speed rail station building, and the system comprises a multi-dimensional perception acquisition module which outputs an environment tensor, an equipment vector and a people flow matrix which are synchronous in time; the dynamic coupling modeling module generates an equipment coupling characteristic impedance coefficient matrix; the optimization algorithm knowledge base module dynamically activates a multi-target genetic algorithm, a particle swarm optimization algorithm and a model prediction control or reinforcement learning algorithm according to the passenger flow growth rate and temperature rise rate characteristic indexes, and outputs an optimization parameter set to the four-dimensional parallel simulation module; the simulation module generates an equipment state parameter combination and an energy efficiency index; the energy efficiency collaborative evaluation module generates a global energy efficiency evaluation report through Pareto frontier analysis and Markov decision; the energy efficiency report generation module outputs a visual chart and equipment maintenance suggestions, and the energy efficiency and state double-closed-loop module triggers model correction based on pipe network pressure difference, temperature uniformity and energy efficiency ratio deviation.

The invention relates to the technical field of computers, and discloses an information source coding matrix data processing method based on a modified conjugate gradient algorithm. According to the method, sparsity constraint and a fixed-point quantization mechanism are fused in iterative optimization, and a sparse fixed-point coding matrix meeting hardware deployment requirements is directly generated through gradient shielding, correction conjugate parameter calculation and Armijo criterion line search and synchronous execution of hard threshold sparseness and fixed-point quantization after each update. According to the method, the sparsity constraint and the fixed-point quantization mechanism are embedded in the iteration process of the modified conjugate gradient algorithm, so that end-to-end alignment of coding matrix optimization and hardware deployment requirements is realized.

The invention provides a multi-domain power grid data collaborative modeling method and system based on a tensor game diagram, and the method comprises the following steps: firstly constructing a six-dimensional enhanced tensor model, constructing a six-order tensor based on the number of nodes, timestamps and other six dimensions, and obtaining a kernel tensor and a factor matrix through CPD-Tucker mixed decomposition; a joint optimization objective function containing reconstruction errors, game equilibrium and privacy risks is constructed, and an optimization kernel tensor and factor matrix is solved; and finally, inputting an optimization result into the MGGCN, processing double targets through a leader branch (a physical topology adjacency matrix guarantees reliability) and a follower branch (a market transaction incidence matrix optimizes economic cost), and generating a collaborative decision result through multi-head attention fusion. According to the method, the problems of low multi-source heterogeneous data fusion efficiency and difficulty in considering cross-domain collaborative privacy security and dynamic optimization are solved, the new energy output prediction error can be reduced, and the data utility loss caused by global encryption is reduced.

The invention relates to the technical field of electric power engineering construction, and particularly provides a data processing method and system for scene decision optimization of electric power stringing construction, and the method comprises the steps: constructing a parameter matrix of multiple scenes, and generating a decision task of each scene; for the decision-making task, construction result data is calculated according to a set logic calculation chain through a data flow and logic association engine; the construction result data is displayed through a visual interface, and a working condition input window is provided; updating the parameter matrix, and calculating updated construction result data; and comparing the updated construction result data with a preset safety rule base, performing safety verification, and optimizing the construction scheme based on the parameter matrix under the condition that the verification is not passed. The technical problems of low calculation efficiency, poor accuracy and insufficient safety due to the fact that independent calculation needs to be manually performed according to different data sources in different sub-processes in electric power stringing construction in related technologies are solved.

The invention relates to an emergency field multi-dimensional inference knowledge graph construction and real-time response method, which comprises the following steps: defining entity attributes and semantic relationships of a disaster-bearing body layer, a disaster-inducing mechanism layer, an emergency resource layer and a disposal process layer, and constructing a multi-dimensional inference knowledge graph; performing adjacent matrix optimization on the multi-dimensional inference mapping knowledge domain by adopting a diffusion model; a disaster-bearing body entity and a disaster-inducing factor entity in the implementation intelligence are identified, spatial semantics are analyzed to GIS coordinates, and an event association strength index is generated in combination with historical cases; performing three-level propagation on the basis of the optimized knowledge graph by taking an entity of which the association strength index meets a threshold value as a seed node; and sorting and outputting according to the comprehensive risk values of the propagation paths, and generating a resource scheduling instruction. Structural defects of an existing system can be thoroughly overcome, and it is ensured that an emergency scheme is always matched with disaster evolution.

The invention discloses a complex soft foundation waterscape miniature steel pipe pile reinforcing method which comprises the following steps: accurately predicting a deformation value of a potential settlement area by fusing thickness distribution parameters, soil compression modulus and numerical simulation, and comparing the deformation value with an engineering safety threshold. When deformation exceeds the standard, an optimization algorithm is adopted to adjust pile foundation arrangement, optimized pile foundation position parameters are generated, then connection node coordinates are extracted from the bottom plate structure model, and stress transmission efficiency and load distribution uniformity are calculated. And if the uniformity is lower than a threshold value, a reinforcement configuration scheme is finally generated by iteratively updating a connection node stiffness matrix, optimizing connection mechanism parameters, extracting mechanical transmission path data, simulating long-term stability and evaluating the durability improvement degree. According to the method, multi-level parameter optimization and numerical simulation are combined, so that the foundation stability and the load distribution uniformity are ensured, and the engineering durability and safety are remarkably improved.

This application belongs to the field of intelligent assessment technology, specifically disclosing a matrix optimization method, apparatus, and device based on a cognitive diagnostic model. This application removes the questions to be verified from the correlation matrix to be optimized and the corresponding answer data from the answer matrix; it determines the student's knowledge point mastery vector and question feature vector based on the target cognitive diagnostic model; and it sequentially verifies multiple knowledge points associated with the questions to be verified according to a preset positive testing strategy, optimizing the correlation matrix by correcting the knowledge points to be optimized. By removing the questions to be verified and their corresponding answer data from their respective matrices, the accuracy of the input model's data is improved. The preset positive testing strategy ensures that knowledge points in the correlation matrix to be optimized are corrected only when sufficient and significant statistical evidence is obtained, thereby effectively improving the accuracy and efficiency of the optimization matrix.

The invention provides a power distribution network reactive voltage optimization method based on mathematical analysis, and relates to the technical field of power distribution network control, and the method comprises the steps: S1, building a control model of a distributed active power distribution network reactive power compensation device according to a physical model and control characteristics of the distributed active power distribution network reactive power compensation device; s2, establishing a multi-optimization objective function which takes the minimum network loss as a core and takes voltage quality into account, and establishing operation constraint conditions; s3, based on the obstacle function conversion constraint and gradient / Hessian matrix optimization, designing a solving algorithm model; and S4, integrating the control model, the multi-objective optimization function and the operation constraint condition into an active power distribution network reactive voltage coordination control optimization model, calling a solution algorithm model for solution, and outputting an optimization control scheme of the reactive power compensation device. According to the scheme, the problems that a traditional distribution network optimization model does not fully fuse the characteristics of the distributed active reactive power compensation device, and a multi-dimensional target and complete constraint are not designed for the active power distribution network can be solved.

The invention discloses a real-time estimation method for tail water flow of aquaculture. The method comprises the following steps: firstly, comprehensively collecting basic environment data of geographic position, landform, water quality and geometric dimension of an aquaculture pond, and arranging and storing the data; secondly, monitoring the initial flow velocity, flow direction and cross sectional area change of the water flow at the tail water discharge port in real time by using professional equipment, and transmitting data according to preset intervals; then analyzing interference factors such as wind power, tide and cultivated organism activity, constructing a correlation model to correct water flow state parameters, and preliminarily estimating instantaneous flow based on a water flow dynamics algorithm; and predicting a flow change trend through time sequence analysis, and constructing an incidence matrix optimization estimation value in combination with production activity data. And finally, the accurate estimated value is transmitted to a culture management system database in a JSON format through a Modbus protocol, and key data support is provided for scientific management. According to the method, a more scientific, efficient and accurate solution is provided for estimating the tail water flow in the aquaculture industry.

The invention discloses a double-constraint second-order blind identification bridge dynamic deflection signal noise reduction method, which comprises the following steps: acquiring dynamic deflection observation signals of bridge target points, and constructing an observation signal matrix by using the observation signals of at least three adjacent target points; carrying out centralization processing on the observation signal matrix, and carrying out whitening transformation to obtain a whitening signal with a covariance matrix as a unit matrix; selecting a group of non-zero time delay values, calculating a time delay covariance matrix of the whitening signal under each time delay, and performing joint approximate diagonalization on all the time delay covariance matrixes to obtain a separation matrix and an initial separation source signal; optimizing the separation matrix based on sparsity constraint to obtain a hybrid matrix; performing dominant frequency optimization on the initial separation source signal; and reconstructing based on the optimized hybrid matrix and the optimized main frequency source signal to obtain a de-noised bridge dynamic deflection signal. The hybrid matrix optimization based on the sparsity constraint enables the reconstruction result to be closer to the real bridge dynamic deformation.

This invention relates to the field of computer technology and discloses a method for processing source coding matrix data based on a modified conjugate gradient algorithm. This method integrates sparsity constraints and fixed-point quantization mechanisms in iterative optimization. Through gradient masking, modified conjugate parameter calculation, and Armijo criterion line search, and by synchronously performing hard-threshold sparsification and fixed-point quantization after each update, it directly generates a sparse fixed-point coding matrix that meets hardware deployment requirements. This invention achieves end-to-end alignment between coding matrix optimization and hardware deployment requirements by embedding sparsity constraints and fixed-point quantization mechanisms within the iterative process of the modified conjugate gradient algorithm.

The invention discloses a visual positioning method based on a second visual angle. According to the method, the flexibility and efficiency of the positioning system are remarkably improved through dynamic vision matrix optimization. In a traditional static matrix, equipment combination is fixed and is difficult to adapt to rapid movement of a target or network fluctuation, so that the precision is reduced or the bandwidth is wasted. According to the method, the visual device combination is dynamically adjusted by evaluating the device efficiency and the target motion trend in real time: when the target is close to the edge of the matrix or the network load is increased, the system automatically replaces the low-efficiency device and activates the standby node, and the positioning precision is always maintained at the centimeter level. According to the mechanism, a view blind area is avoided, redundant video transmission is greatly reduced, bandwidth pressure during cluster scale expansion is directly relieved, a dynamic recombination process and multi-view calculation are seamlessly coordinated, a positioning result is closer to a real position of a target, reliable support is provided for a cluster coordination task in a complex scene, and the method is suitable for large-scale application. And the robustness and the response speed of the system are further enhanced.

The invention discloses a model training method and system based on alternative adaptive momentum optimization, and solves the technical problems that the memory efficiency of large-scale matrix optimization is low and training of a larger-scale model cannot be supported due to an existing model training method based on an adaptive optimization algorithm. The method comprises the steps that initial model parameters are acquired and marked as leaf nodes, and a gradient space used for storing first-order momentum is constructed; performing preprocessing according to the type of the initial model parameter, and outputting a target model parameter and model parameter analysis data; calculating unbiased first-order momentum of the current step number based on target model parameters and historical first-order momentum in a gradient space, and obtaining second-order momentum through element-by-element square; and performing odd-even alternate updating on the second-order momentum vector factor through an alternate adaptive momentum method, and iteratively updating a target model parameter according to an adaptive step length rule in combination with model parameter analysis data and unbiased first-order momentum to obtain a pre-training optimal parameter and construct a pre-training model.

The present application relates to the field of machine vision and optical detection technology, in particular to a tea color automatic evaluation method and system based on machine vision; the method comprises the following steps: constructing a stable and repeatable optical measurement environment and calculating radiation baseline parameters; performing multi-band polarization sequence imaging on tea samples to obtain original data with spectral and structural characteristics; separating the specular and diffuse reflection components of the tea surface and performing spectral consistency correction combined with the radiation baseline; performing multi-band spectral reconstruction and normalization processing based on the corrected diffuse reflection image, mapping to the CIE Lab color space, extracting pixel-level brightness, chrominance and color distribution statistical indicators, combining spatial weighting matrix optimization to generate color feature vectors; constructing a quantitative evaluation and grade determination model, calculating the final score by combining the perceived color fraction, dry calibration color fraction and penalty term, and outputting the grade result. The present application realizes objective, accurate and automatic evaluation of tea color.

The invention provides an e-commerce platform commodity sales data analysis system and method, relates to the field of data analysis, and effectively improves the efficiency and accuracy of e-commerce platform commodity sales data analysis through operations such as hierarchical coding, incremental matrix optimization and price sensitivity hierarchical recommendation. Through hierarchical coding and dynamic compression, data storage and processing are optimized, the system burden is reduced, and the real-time performance of data processing is improved. The incremental matrix optimization method ensures that the system can quickly respond to changes of new users and new commodities, and the cold start problem is effectively solved. And the hierarchical dynamic recommendation module performs personalized recommendation for price-sensitive users, so that the recommendation accuracy is improved.

The invention relates to the field of automobile motors, and discloses a stator punching sheet lamination positioning forming method, which comprises the following steps: S1, detecting a single stator punching sheet to be laminated on line, and generating a single sheet error feature representing the geometric defect of the single stator punching sheet; s2, calculating an optimal rotation angle used for counteracting the accumulative error characteristics; s3, according to the optimal rotation angle, the single stator punching sheet is laminated on the laminated iron core, so that the stator punching sheet and the laminated iron core are fixedly connected into a whole; and S4, forming an updated accumulative error feature for the next overlying calculation. Through online detection and matrix optimization, an optimal rotation direction for active error cancellation is found for each punching sheet. According to the method, the geometric accuracy of the iron core is pushed to a new height, which is fundamentally distinguished from a positioning mode which adopts a fixed angle to carry out error averaging in an existing large rotation process, and the inherent defect that accumulative errors cannot be eradicated due to the fact that a compensation strategy is rigid is overcome.

The application discloses a kind of based on storage optimization's hybrid beam forming method, system and storage medium, its method includes steps: according to the number of radio frequency link, antenna quantity and channel state calculation transmitting end hybrid precoding matrix constraint condition;According to constraint condition, the objective function of transmitting end hybrid precoding matrix optimization is established;According to the relationship of time delay and / or spectral efficiency and / or memory occupancy and quasi-newton method storage capacity calculation optimal storage capacity;According to the optimal storage capacity corresponding finite memory quasi-newton method, the optimal solution of transmitting end hybrid precoding matrix optimization objective function is calculated;According to the optimal solution of transmitting end hybrid precoding matrix optimization objective function, transmitting end hybrid precoding matrix is obtained and is used for hybrid beam forming.The application solves the problem that the system performance of existing hybrid beam forming method is poor and the calculation time delay is high.

The application discloses a heterogeneous helicopter fleet scheduling scheme hierarchical optimization method, comprising the following steps: S1, important information in a disaster is summarized, and structured input task scenario information is input; S2, a scheduling problem model of the heterogeneous helicopter fleet is constructed, including two levels of a task division optimization model and a path planning problem model; S3, a data structure of a solution is designed, a task division matrix is taken as the data structure corresponding to the task division optimization model, and a helicopter task trajectory is taken as the data structure corresponding to the path planning problem model; S4, a path planning solver is designed in combination with the data structure of the solution and the path planning problem model; and S5, a task division matrix optimization solver is designed in combination with the data structure of the solution, the task division optimization problem model and the path planning solver. The method changes the scheduling scheme from a text type into a data type, and changes real-time scheduling into scheme making, and can effectively improve the scheduling scheme efficiency.

The invention discloses an ECG hash coding method and system based on sample similarity guidance, and belongs to the technical field of biological signal data machine learning, and the method comprises the steps: obtaining a data set containing a plurality of ECG signals, extracting features, and constructing an ECG feature matrix; for unsupervised learning, a feature space measurement matrix and a Hash space modulation matrix are introduced, and a generalized similarity preserving equation for aligning an Euclidean space and a Hamming space is constructed in combination with an ECG feature matrix and a to-be-solved Hash coding matrix; for supervised learning, introducing a semantic space measurement matrix on the basis of unsupervised learning, and constructing generalized similarity preserving equations which simultaneously align the Euclidean space and the Hamming space and align the semantic space and the Hamming space; converting the equation into a matrix optimization problem, and solving to obtain a Hash coding matrix; and the Hash coding matrix is used for coding the ECG signal, so that the Hash coding quality and the auxiliary diagnosis retrieval efficiency can be effectively improved.

The application relates to the technical field of map construction, and provides a SLAM map optimization method and device based on local characteristic values. The method comprises the following steps: acquiring sensor data, and constructing a state matrix of a SLAM system based on the sensor data; performing eigenvalue decomposition on the state matrix, acquiring each eigenvalue, and calculating the change rate of each eigenvalue; determining an eigenvalue with a change rate exceeding a preset threshold as a to-be-optimized eigenvalue, selecting the first M eigenvalues with the largest change rates from the to-be-optimized eigenvalues, and forming a target eigenvalue set; updating a local block corresponding to the first M eigenvalues in the state matrix according to the target eigenvalue set, obtaining an updated state matrix; and optimizing and outputting pose data and map data based on the updated state matrix. By performing local recalculation on a small number of eigenvalues with significant changes in the state matrix, the calculation complexity is significantly reduced while ensuring the positioning and mapping accuracy, and the real-time requirement of high-frequency positioning scenes such as intelligent driving is met.

The invention discloses a constructed wetland pollution reduction and carbon reduction synergistic effect analysis system and method, and relates to the technical field of water pollution control and greenhouse gas emission reduction. Comprising a simulated wetland reaction unit, a multi-parameter monitoring module, an operation regulation and control module, a microbiological analysis module and a synergistic effect evaluation module. The simulated wetland reaction unit is filled with a FeC composite matrix to realize subsurface flow constructed wetland environment simulation; the multi-parameter monitoring module acquires water quality and greenhouse gas emission data in real time; the operation regulation and control module realizes accurate regulation and control of water inlet parameters; the microbiological analysis module analyzes microflora and functional gene information; and the synergistic effect evaluation module integrates multi-module data to construct a correlation model, calculates a synergistic effect index and outputs an optimization parameter. Through five stages of matrix optimization, operation regulation and control, microbial mechanism revealing, collaborative evaluation optimization and feedback application, evaluation and optimization of the constructed wetland pollution reduction and carbon reduction collaborative efficiency are realized, and a scientific basis is provided for actual constructed wetland engineering design and operation.

The invention belongs to the technical field of image processing, and particularly relates to a single-pixel imaging-free classification measurement matrix optimization method and device based on principal component analysis. The method comprises the following steps: collecting a compressed light intensity signal of a target scene through a single-pixel detector to form original measurement data; extracting a representative feature vector from the full-sampling light intensity value of the category prior image by adopting a principal component analysis (PCA) dimension reduction technology; carrying out feature fusion on the feature vector and an original measurement matrix, and constructing a principal component analysis measurement matrix (PCAMM); carrying out compressed sampling on a target based on PCAMM to obtain a one-dimensional light intensity sequence; and target classification is directly carried out on the one-dimensional light intensity sequence through a classifier. According to the method, the technical route of'first reconstruction and then perception 'of traditional single-pixel imaging is broken through, a PCAMM matrix construction method is innovatively provided, and the randomness of a measured value is remarkably reduced through feature enhancement measurement. According to the scheme, an optimization effect on various classic measurement matrixes is achieved, high classification precision is still kept under the complex environment of high noise, low sampling rate and the like, the storage space can be saved to the maximum extent through the imaging-free characteristic, and a new high-energy-efficiency perception normal form is provided for a visual system with limited resources.

The invention provides a picoampere-level measurement data access corrosion AI large model training method and system, and relates to the technical field of model training, and the method comprises the steps: receiving picoampere-level current measurement data, and dividing the picoampere-level current measurement data into source domain data and target domain data; constructing a corrosion prediction model and calculating a prediction error; mapping the source domain data to an electrochemical polarization curve space to generate a confrontation sample; constructing migration mapping matrix optimization interface reaction parameters; and generating a prediction result by using a polarization curve and impedance spectrum feature training model. According to the method, accurate mapping of the picoampere-level current signal and the corrosion state is realized, and the cross-scene corrosion prediction accuracy is improved.

This invention discloses an angle-prior-assisted intelligent metasurface phase coding design method for two-dimensional DOA estimation. The method includes: constructing a system model for two-dimensional DOA estimation with a single receiving channel on the intelligent metasurface; deriving the explicit relationship between the CRB of the two-dimensional DOA estimation parameters and the phase coding matrix; utilizing the prior range information of the target two-dimensional angle, constructing a phase coding matrix optimization problem with a unit modulus constraint, aiming to minimize the average CRB; and efficiently solving this non-convex problem using the Riemannian manifold optimization algorithm to obtain the optimal phase coding matrix. This invention directly links the coding design to the theoretical performance lower bound. By optimizing the coding to focus beam energy in the target prior angle region, it significantly improves the accuracy of two-dimensional DOA estimation for azimuth and elevation angles within the angle prior range, reduces the required measurement overhead, and exhibits good robustness under one-bit phase coding.