37 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 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 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.

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 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 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 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 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 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.

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.

The invention discloses an intelligent generation system and method for a scattering track of a scattering vehicle based on big data, and relates to the field of agricultural mechanization and intellectualization, and the method comprises the steps: collecting environment data, carrying out the rasterization processing, topological simplification and dynamic attribute labeling of the collected environment data, and generating a standardized grid map, a topological node set and a dynamic attribute matrix; constructing a dynamic graph model based on the standardized grid map, the topological node set and the dynamic attribute matrix; updating the adjacency relation in real time through a dynamic adjacency list generation algorithm; based on the dynamic graph model, path planning is carried out by adopting an improved breadth-first search algorithm, and a structured path sequence is generated; the improved breadth-first search algorithm adopts a three-dimensional state space design, records historical access times and sets a repeated access threshold. Through dynamic weight matrix optimization and a parameter feedback mechanism, a complete closed-loop control system is formed, and an efficient and reliable solution is provided for intelligent operation of agricultural machinery.

The application discloses a spraying process parameter optimization method and system based on big data, and the method comprises data acquisition, calculation of parameter influence value, calculation of quality index correlation degree, reconstruction of correlation matrix, construction of neighborhood similarity matrix, optimization of membership matrix, and real-time process parameter optimization.The application belongs to the field of data processing, and specifically refers to a spraying process parameter optimization method and system based on big data.The scheme defines parameter node influence value, describes the level and attenuation characteristics of parameter influence, introduces a time factor to define process parameter combination similarity, objectively reflects the internal correlation of quality indexes based on quality index correlation degree, completes unverified correlation by reconstructing the correlation matrix, introduces a dynamic attenuation term, dynamically adjusts the attenuation degree according to the spraying process combination similarity, and avoids information loss, dynamically adjusts the similarity influence, and optimizes the neighborhood similarity weight, thereby improving the process parameter optimization effect.

The application provides an Internet of Things large model pruning and compression method and system based on Attention matrix optimization, relates to the technical field of data processing, and comprises the following steps: obtaining a pre-trained Internet of Things large model, extracting parameters in an Attention matrix to obtain a parameter set to be optimized; based on the parameter set, performing importance evaluation on the parameters in the Attention matrix to generate parameter importance distribution data; taking the obtained parameter importance distribution data as a processing basis, establishing high importance calibration, medium importance calibration and low importance calibration, and constructing a ternary calibration matrix. The application effectively balances the compression ratio and inference accuracy.

This application provides a method, apparatus, and electronic device for extrinsic parameter calibration of radar and pose sensors. The method includes: acquiring multiple target frame pairs; then, calculating a first extrinsic parameter matrix for the radar and pose sensors based on the multiple target frame pairs, and solving for initial extrinsic parameters based on the hand-eye constraint relationship of multiple frame pairs; subsequently, evaluating the geometric consistency and motion consistency of the first extrinsic parameter matrix based on the multiple target frame pairs to obtain a comprehensive evaluation index for the first extrinsic parameter matrix. This comprehensively quantifies the calibration quality of the initial extrinsic parameters from two dimensions: the geometric accuracy of point cloud fusion and the motion accuracy of trajectory estimation, avoiding the limitations of a single index. Finally, if the comprehensive evaluation index is less than a preset threshold, the first extrinsic parameter matrix is ​​optimized using a global optimization algorithm to obtain a second extrinsic parameter matrix. Global optimization further corrects the deviation of the initial extrinsic parameters, improving extrinsic parameter accuracy and robustness, ensuring that the final calibration result meets the high-precision positioning requirements of autonomous driving scenarios.

The application discloses a kind of robot-assisted multi-view three-dimensional scanning measurement methods, first, acquisition checkerboard calibration plate image, obtain n group camera information and corresponding shooting position robot posture information, then based on Kronecker product without special mathematical tool and effectively solve linear equation closed solution, reduce the error influence caused by system noise, calculation in calibration process, complete preliminary hand-eye calibration;Then with hand-eye relationship matrix as initial value, according to photogrammetry bundle adjustment as optimization model, establish minimization re-projection error as cost function, introduce particle swarm algorithm iteration to hand-eye relationship matrix optimization, further improve the hand-eye calibration precision, ensure the registration quality of point cloud image;Finally, by controlling robot motion carries binocular camera under multiple measurement poses Acquisition large complex parts unit measurement point cloud information, based on the optimized hand-eye matrix conversion to same coordinate system, complete multi-view point cloud registration function, to improve the three-dimensional topography measurement efficiency of large complex parts.

This invention relates to an unsupervised cell segmentation method based on Transformer. The multimodal text-image alignment module aims to effectively fuse text and image data, achieving high-level alignment of multimodal information through the Transformer architecture. A low-rank attention mechanism enhances the interrelationship of data, enabling more accurate extraction and alignment of multimodal features in an unsupervised environment. The matching matrix feature optimization module further processes the aligned feature data. Utilizing a unique matching matrix optimization algorithm, the accuracy of feature matching is significantly improved. Through the optimized matching matrix, the parameters for segmenting cells are extracted and adjusted, providing more accurate initial cues for subsequent segmentation. The optimized features are input into the SAM segmentation module. SAM leverages its powerful segmentation capabilities to achieve high-precision cell segmentation. This module fully utilizes the advantages of the low-rank attention mechanism and matching matrix optimization to ensure the accuracy and robustness of the segmentation results.

The invention discloses a planting plot space structure reconstruction method and system based on multi-source data fusion, and relates to the technical field of remote sensing application, and the method comprises the steps: obtaining a high-resolution optical image, a synthetic aperture radar image and digital elevation model data of a target region, carrying out the radiometric calibration, atmospheric correction, terrain correction and registration processing, and carrying out the reconstruction of the spatial structure of the planting plot; generating a multi-source data cube; spectral features, texture features, scattering features and topographic features are extracted from the multi-source data cube, channel dimension stacking is carried out, and a high-dimensional multi-channel feature image is generated; a back scattering coefficient graph of the analog synthetic aperture radar is calculated by using a surface microwave scattering physical model and a high-dimensional multi-channel feature image, and a scattering residual image is obtained by calculating a residual with a real synthetic aperture radar image. According to the method, feature aggregation and semantic similarity calculation are carried out through a graph convolutional neural network, graph spots are optimized and merged according to a node incidence matrix, and a high-precision plot space structure reconstruction vector graph is output after topology check and geometric correction.

This invention discloses a slope surface displacement inspection and monitoring system and method based on grouped planar target points and robust fusion of multiple images. The system includes: a target point layout module, a planar coordinate modeling module, an image acquisition and processing module, a target point identification module, a homography solution module, a matrix optimization module, a displacement inversion module, a fusion module, an early warning module, and a data management module. This invention uses uniquely coded target points, which significantly improves the reliability of automatic identification and cross-time-series matching of slope monitoring points. By grouping slope target points according to local coplanar features, this invention avoids errors caused by establishing a unified planar model for complex slopes as a whole, thus improving the accuracy of geometric modeling.

The invention provides an energy efficiency power model-based RIS-assisted ISAC joint beam forming method and system. The method comprises the following steps: S1, a base station sends a signal to a reconfigurable intelligent surface RIS and a user k; s2, calculating a receiving signal and an equivalent channel according to the transmitting signal in the S1, obtaining an SINR of the kth user, modeling a power model, determining spectral efficiency (SE) and energy efficiency (EE), and calculating the minimum transmitting power; s3, on the basis of an alternating optimization framework, power distribution optimization is executed on the basis of the minimum transmitting power obtained in the S2; s4, fixing the power distribution matrix obtained in the S3, and executing RIS phase offset matrix optimization; and S5, judging whether the algorithm converges or not, otherwise, executing S3 again, and calculating the SE and the energy efficiency EE of the current iteration. And S6, outputting the maximum energy efficiency value and the corresponding beam forming matrix and RIS phase offset matrix. According to the method, the energy efficiency of the RIS-assisted ISAC system can be accurately optimized, the communication quality and the environment perception capability are dynamically balanced, and the calculation complexity of non-convex constraint processing is reduced.

The application discloses a public resource configuration and use collaborative optimization method based on a hypergraph game, relates to the field of public goods game, and comprises the following steps: a hypergraph public resource game model is constructed, the hypergraph node is a participant, the hyperedge is a public resource item, and the participation relationship is described by a hypergraph correlation matrix; the income of each subject under different cooperation states is calculated according to the hypergraph model; the income of the subject is calculated according to a linear public good income function, and the threshold of the subject is calculated by using the full cooperation maintenance condition under the Grim strategy; the resource allocation and contribution strategy are optimized, and the resource allocation scheme and individual contribution scheme beneficial to maintaining full cooperation are output. The application uses a hypergraph to represent multi-person high-order interaction, is more accurate than a common graph model in describing the real structure of multiple subjects participating in a public item, uses a full cooperation threshold and a feasible interval to judge stability, is more interpretable, and provides resource allocation optimization and contribution matrix optimization, serving managers and participant subjects.

The application discloses a kind of compressed imaging block circulation structure measurement matrix optimization design method, belong to image technical field.The method includes: based on the total pixel number of image to be reconstructed constructs particle swarm, based on the current free element vector of each particle determines the current block circulation measurement matrix, calculates the Gram matrix corresponding to the current block circulation measurement matrix;Using the objective function based on Gram matrix constructs to calculate the fitness of each particle;Based on the current free element vector and fitness of each particle determines and updates the local optimal free element vector corresponding to each particle, determines the inertia weight of each particle;From all local optimal free element vectors, determine the global optimal free element vector;Based on global optimal free element vector determines the block circulation measurement matrix corresponding to the compressed sampling, reconstructs image based on block circulation measurement matrix.The application can improve subsequent image reconstruction effect.

The invention discloses a dose calculation system and method for neutron therapy, electronic equipment and a medium, the dose calculation system for neutron therapy comprises a CT image information acquisition module, a material model module, an ROI parameter acquisition module, a material matrix optimization module, a material density table optimization module and a dose calculation module; according to the method, the original dose grid is globally simplified, the region of interest of the defined material and the HU-based material model are combined, the transition region is extracted through bidirectional statistics, CT anatomical information and HU information can be taken into consideration, precision loss caused by roughening processing is eliminated, the optimal dose calculation grid is obtained through multiple iterations, the optimized transition region is obtained, and the accuracy of the optimal dose calculation grid is improved. Physical information and biological information under high resolution are fully considered in the transition area, and the accuracy of a calculation result can be considered while the dose calculation speed is increased.

The invention discloses a power transmission line engineering mechanized operation monitoring object identification method based on super-resolution reconstruction, relates to the technical field of remote sensing image processing, and comprises the steps of extracting gradient information and a guide direction through a method of combining a Sobel operator and an arc tangent function, improving the accuracy of candidate point screening, and improving the accuracy of candidate point screening. According to the method, a path generation process is optimized on the basis of a priority mask matrix through a method of combining dual-threshold segmentation and a greedy search algorithm, candidate point screening and direction updating efficiency is improved, and multi-level gridding representation of a remote sensing image is realized through a method of combining a uniform grid division method and a quadtree recursive segmentation method. Through a method of combining an A * search algorithm and a grid two-tuple form, path expansion and determination of a reconstruction sequence are completed, and the processing efficiency of a large-scale remote sensing image is improved.

The invention discloses a perception power and estimation precision optimization method and system for DOA estimation of an RIS-ISAC system, and the method comprises the following steps: S1, converting a binary data stream of a base station into a radio frequency signal; s2, the signals in the S1 are transmitted to the users and a sensing target, the CRB of the two-dimensional angle at the active RIS is adopted as a sensing signal index, and the SINR of each communication user is adopted as a communication index; the CRB (i) of the ith iteration is calculated; s3, judging whether CRB (i) obtained in S2 meets an inequality or not, and if yes, skipping to and executing S6; otherwise, taking the CRB (i) as an output parameter, skipping and executing S4; s4, taking the CRB (i) obtained in the step S3 as an input parameter, executing a beam forming vector optimization method, and outputting an Mx1-dimensional vector w of which elements are complex numbers; s5, taking the beam forming vector w as an input parameter, executing an RIS phase shift matrix optimization method, outputting an NxN-dimensional matrix of which elements are complex numbers, and skipping to and executing S2; and S6, obtaining an equilibrium perception and communication optimal value according to the step S3, and taking the equilibrium perception and communication optimal value as an output result.