10 results about "Design matrix" patented technology

A kind of GNSS data processing method based on single observation filtering, comprising the following steps: first, design matrix, observation vector and covariance matrix are decomposed into multiple row vectors or scalar, then one observation is processed each time, after all the observations of current epoch are processed through multiple cycles, the state vector and covariance matrix of current epoch recursive update are obtained;GNSS parameters in state vector are divided into common parameters and non-common parameters, when processing each observation, update common parameters, then gradually increase new parameters in state vector, i.e. process observation by observation, to obtain extended state vector and covariance matrix;In the third step, when data processing is carried out using the above-mentioned extended state vector and covariance matrix, it includes single epoch processing and multi-epoch processing;The state estimation of current epoch is output as the result of GNSS positioning.The present application has high efficiency when processing high-dimensional observation data.

The invention provides a Beidou multi-frequency positioning method and device, electronic equipment and a storage medium. The method comprises the following steps: S1, collecting observation data and satellite ephemeris data of two target frequency points of a Beidou satellite; s2, based on the satellite ephemeris data, modeling the Beidou satellite clock correction by adopting a quadratic polynomial to obtain a clock correction modeling error, and taking the clock correction modeling error as a constraint term of an original design matrix to generate a design matrix with clock correction constraint; s3, constructing an observation vector based on the observation data, and substituting the design matrix with the clock error constraint and the observation vector into a TLS resolving model for resolving to obtain a positioning parameter initial value and a corresponding resolving residual error; s4, fitting a mapping relation between the residual error and the noise intensity based on the resolving residual error, generating a weight matrix, and performing weighted updating on the TLS resolving model by using the weight matrix to obtain an updated TLS resolving model; according to the embodiment of the invention, effective constraint on the design matrix error can be realized, and the clock error modeling precision is improved.

The application provides a semi-sphere adaptive grid division method for MHGM multi-path error modeling, comprising the following steps: obtaining a high-resolution multi-path error prior model as prior information by using non-difference residuals, and obtaining the spatial distribution characteristics of the multi-path error at a survey station; establishing a fixed grid division MHGM model on the survey station, extracting an AMR initial grid, obtaining the prior model value in the grid, setting to-be-estimated parameters at the corner points of the grid, and obtaining the relationship between the prior model value and the coordinates by using a bilinear interpolation method; setting the grid corner points as a to-be-estimated parameter matrix, the corresponding bilinear interpolation coefficient matrix as a design matrix, and the prior model value as an observation value matrix, so as to obtain the estimated value of the parameter matrix and the corresponding residual vector; traversing the residual vector, and considering that the grid can be divided when the absolute value is greater than the corresponding threshold value; stopping the division when the preset condition is met for any grid, or taking the grid as four congruent grids; and outputting the semi-sphere adaptive grid division result after the division is completed.

The invention discloses a method and a device for preprocessing industrial generated data containing high-dimensional noise, and relates to the field of data preprocessing, and the method comprises the following steps: outputting a sampling time point set and a multi-dimensional observation data set by collecting multi-dimensional function type observation data in a set time interval; and representing the real function of each dimension observation value as a primary function linear combination, extracting effective features, and outputting a primary function set and an expansion coefficient. And constructing a design matrix, and establishing association between the multi-dimensional observation data and the effective features. A combined objective function is constructed by combining multivariable synchronous processing requirements and signal importance differences, and a penalty matrix is constructed through an inner product of a second derivative of a primary function. And selecting a smoothing parameter by minimizing a generalized cross validation criterion, solving a target function to obtain an estimation expansion coefficient, and constructing and outputting a smoothing function as input data of a statistical process control or fault diagnosis model. The method solves the problems that in the prior art, effective features are prone to being lost, multi-dimensional collaboration is not considered, and smooth parameter selection lacks self-adaption.

The invention discloses a test design method for determining test repetition times by giving a test efficacy value, which comprises the following steps of: 1, determining influence factors of response variables, constructing an initial test design scheme, and setting constraint conditions for a factor test efficacy threshold value; step 2, extracting non-repeated test schemes from the test design schemes; 3, setting different repetition times for each row of tests of the non-repeated test scheme, and constructing a test design matrix; and step 4, considering a target optimization problem, optimizing a target function, and determining the minimum number of repetitions of each row of tests conforming to the factor test efficacy threshold. By adopting the technical scheme provided by the invention, the optimal repeated test times meeting the test efficacy limitation can be determined, so that the uncertainty of the test repeated times is reduced.

The application discloses a GNSS and leveling fusion ground deformation monitoring method and system with dynamic optimization weight, and relates to the technical field of geodesy and deformation monitoring.The method comprises the following steps: analyzing leveling and GNSS rate observation data; setting the elevation correction number and subsidence rate parameter of a monitoring point as unknown parameters, constructing adjustment observation equations coupled with observation time and subsidence rate parameters, forming a design matrix, an observation vector and an initial weight matrix; performing dynamic weight optimization, introducing a global weight factor for adaptive scanning, and on the basis of the optimal global weight ratio, calculating a weight reduction factor by using robust estimation according to the standardized residual error of GNSS observation values, and obtaining the optimal weight matrix; and finally solving the optimal estimation value of unknown parameters based on the optimal weight matrix.The application realizes two-stage dynamic optimization of the weight, objectively optimizes the weight by data driving, effectively suppresses the influence of gross errors, and significantly improves the precision and reliability of multi-source data fusion deformation monitoring.

The present application belongs to the field of color correction, and discloses a multi-classification color correction method based on spectral shape driving, which comprises the following steps: obtaining standard tristimulus values of a plurality of standard color panels, collecting spectral reflectance data of the standard color panels and performing smoothing and normalization processing; extracting a group of spectral shape features from the processed spectral reflectance data, and performing standardization processing on the feature vectors composed of the spectral shape features; dividing all the standard color panels into multiple categories, and determining the centroid of each category; constructing a design matrix and a target matrix for each category, designing a corresponding loss function, solving to obtain the calibration coefficient matrix and the regularization hyperparameter of each category, and packaging them into a calibration model for deployment to a color difference meter; predicting samples by using the calibration model of each category, and performing weighted fusion according to the similarity value to output corrected tristimulus values. While maintaining the measurement accuracy of the color difference meter, the color calibration stability of the high and low reflectivity regions is significantly improved.

The invention relates to a random test design and robust optimization method and system for a complex equipment system. The method comprises the following steps: generating an initial test design matrix; according to the initial test design matrix, obtaining system response variable data; identifying key test factors according to system response variable data, and establishing an agent model; calculating an estimation result of a random error term according to the proxy model, and establishing a joint multi-parameter distribution model; coupling the agent model and the joint multi-parameter distribution model, and establishing a prediction model of system response variables; and analyzing a prediction result of the prediction model to obtain a robustness metric value, and if the robustness metric value does not reach a preset threshold value or does not meet a preset convergence criterion, optimizing the prediction model of the system response variable by newly adding data until a preset condition is met, obtaining the optimized prediction model, and outputting a final robust test design scheme. According to the invention, the robustness and reliability of the equipment system test can be obviously improved in a complex environment.

The invention discloses an asphalt pavement performance multivariable grey prediction method of a sparrow optimization regular term, which takes PCI sequences of a plurality of detection units as system behavior variables, constructs a novel discrete multivariable grey model NSMGM (1, 6, 1), weakens data randomness through grey accumulation generation and adjacent mean value generation, and improves the performance of an asphalt pavement. A model structure is perfected by introducing a linear correction term and a gray action quantity, and the problem of non-homology of parameters is solved; a regularization item is introduced, model deviation and variance are balanced through an Euclidean norm of a penalty parameter vector, and the ill-conditioned problem of a design matrix under a small sample is improved; a sparrow search algorithm is adopted to simulate a "explorer-follower-alerter" population behavior, regularization parameters are adaptively optimized in a preset search space, and subjective trial and error are avoided. The three components organically cooperate to form an integrated prediction framework of variable correlation capture, parameter stability estimation and global optimization, and a quantitative basis is provided for pavement maintenance planning and engineering decision.

The invention belongs to the field of color correction, and discloses a multi-classification color correction method based on spectral shape driving, and the method comprises the steps: obtaining the standard tristimulus values of a plurality of standard color palettes, collecting the spectral reflectivity data of the standard color palettes, and carrying out the smoothing and normalization processing; extracting a group of spectral shape features from the processed spectral reflectivity data, and carrying out standardization processing on a feature vector formed by the spectral shape features; dividing all the standard color plates into a plurality of categories, and determining the centroid of each category; constructing a design matrix and a target matrix for each category, designing a corresponding loss function, solving to obtain a calibration coefficient matrix and a regularization hyper-parameter of each category, and packaging the calibration coefficient matrix and the regularization hyper-parameter into a calibration model to be deployed to a colorimeter; and predicting the sample by using the calibration model of each category, performing weighted fusion according to the similarity value, and outputting a corrected tristimulus value. While the measurement precision of the colorimeter is maintained, the color calibration stability of high and low reflectivity areas is significantly improved.