40 results about "Cholesky decomposition" patented technology

Disclosed in the present invention are a method and apparatus for synthesizing spatially cross-correlated multi-point ground motions fitting a response spectrum. The method comprises: using an influence matrix method to obtain ground motion acceleration time histories fitting a target reaction spectrum with high precision, and calculating a Fourier amplitude spectrum and a phase spectrum thereof; and considering a calculation relationship between a power spectral density function and the Fourier amplitude spectrum, taking the power spectral density function of the obtained time histories as an initial value, using a random vibration theory and Cholesky decomposition to obtain spatially cross-correlated multi-point ground motion time histories, further taking into account a phase spectrum of the ground motion time histories fitting the target reaction spectrum with high precision, and performing correction and iteration to obtain spatially cross-correlated multi-point ground motions fitting a response spectrum. The present invention overcomes the shortcomings in current multi-point ground motion time history synthesis techniques, such as the difficulty in ensuring good fitting to a target response spectrum and the non-stationarity of synthesized ground motions, helping to improve the reliability of dynamic response analysis results for large-scale engineering structures under the action of multi-point ground motions.

The invention relates to a rock-soil-building block collaborative slope reliability analysis method considering spatial variability. The method comprises the following steps: establishing a slope finite element model; aiming at the finite element model, constructing an autocorrelation random field which takes a soil mass parameter as a variable and has a discrete characteristic by using a Cholesky Decomposition midpoint method; coupling the self-correlation random field based on a Copula model to form a cross-correlation standard uniform random field, and performing equal probability conversion on the cross-correlation standard uniform random field to form a cross-correlation random field in logarithmic normal distribution; and inputting the generated random field into a finite element model, obtaining a safety coefficient by using a strength reduction method, and determining the slope reliability based on a Monte Carlo method. According to the method, the finite element analysis result is closer to the actual situation, and the accuracy of the reliability analysis result is remarkably improved.

A sensor unit is coupled to a machine and configured to detect anomalous behavior of the machine. The sensor unit includes a low power microcontroller that learns to recognize a plurality of operations of the machine. The sensor unit generates mean vector and inverse of a Cholesky decomposition matrix for each operation. During a detection mode the sensor unit computes a Mahalanobis distance for each feature vector, mean vector and first matrix. The sensor unit detects anomalous behavior or classifies the operation of the machine based on the Mahalanobis distances.

The invention relates to the technical field of data center optimization, and particularly discloses a data center two-stage stochastic optimization method and system considering wind and light uncertainty, and the method comprises the steps: carrying out the modeling of a data center energy supply system, generating a wind and light output sample based on Monte Carlo simulation, enabling the generated random sample to meet wind power and photovoltaic output complementation through Corisky decomposition; introducing a first-order autoregression model, generating random scenes in combination with multivariate normal distribution, and obtaining a wind and light output curve in each scene; a two-stage stochastic optimization model containing computing power scheduling and multi-energy coordination is constructed by taking the minimum expected operation cost of a system as a target, the model is solved, and an optimal time sequence operation strategy of a computing power task is decided. According to the two-stage random optimization, the adjustment cost caused by prediction errors is covered with low risk premium, and effective support is provided for reliable operation of the data center under new energy output fluctuation and computing power load time sequence mismatch.

An electronic device includes a communication circuit including 2N reception antennas, and a communication processor including a covariance matrix generation circuit that generates a covariance matrix for the 2N reception antennas based on a measurement of a received signal, and a whitening filter matrix generation circuit that calculates a whitening filter for N reception antennas based on Cholesky decomposition. The covariance matrix includes a first sub-matrix, a second sub-matrix, a third sub-matrix, and a fourth sub-matrix, and the whitening filter matrix generation circuit calculates a whitening filter matrix for the 2N reception antennas based on characteristics of a first whitening filter matrix and the Cholesky decomposition for the first sub-matrix corresponding to a diagonal sub-matrix of the covariance matrix.

The invention discloses a covariance matrix prediction method based on a neural network, and relates to the crossing field of financial engineering and deep learning. The method comprises the following steps: firstly, collecting five-minute-level high-frequency data of a Chinese stock market, screening and filling, then calculating a realized covariance (RCOV) matrix, and then preprocessing through square root transformation and Cholesky decomposition; thirdly, constructing a 3D input tensor and a label, and dividing and standardizing a time sequence data set; then, a CM-ConvBiLSTM model containing a coding layer (ConvBiLSTM + CNN) and a generation layer is built, and training is carried out through Huber loss and an Adam optimizer; and finally, carrying out inverse preprocessing to obtain a positive definite prediction RCOV matrix which is used for constructing investment portfolios such as global minimum variance and monthly rebalance. According to the method, the problems of high-dimensional adaptation, positive qualitative guarantee and the like are solved, the investment portfolio optimization precision and stability are improved, and the method adapts to Chinese stock market characteristics.

This application discloses an accelerated Cholesky decomposition calculation method based on a dataflow architecture. The method includes: a data preparation step, a 2×2 matrix block calculation step, and a 1×1 matrix block calculation step. The dataflow architecture divides the processing unit (PE) array into multiple PE groups. The irregular matrix to be calculated is divided into multiple regular matrix blocks according to a predetermined partitioning rule. If the N of the N×N matrix to be calculated is greater than a predetermined threshold, a 2×2 block-based Cholesky splitting algorithm is used to iteratively calculate the matrix blocks in each PE group. Data transfer between PE groups is achieved through the data dependency relationship between different PE groups on the Cholesky decomposition calculation, and the size of the matrix to be calculated is dynamically adjusted and reduced. If the N of the N×N matrix to be calculated is less than or equal to a predetermined threshold, a 1×1 block-based Cholesky splitting algorithm is used to iteratively calculate until the calculation is completed and the calculation result is output, thus completing the accelerated Cholesky decomposition calculation based on the dataflow architecture.

The invention discloses a cascade hydropower scene generation method and device, equipment, a medium and a product. The method comprises the steps of performing standardization and quantile transformation on actually measured data of a plurality of cycles before a plurality of hydropower station prediction periods of a target drainage basin; performing iterative random sampling through multivariate normal condition distribution and Cholesky decomposition by adopting a preset TVP-VAR model according to the converted measured data to generate a flow scene of each period in a prediction period; wherein the preset TVP-VAR model is formed by splicing a lag coefficient matrix and a disturbance covariance matrix of each period according to a time sequence; and quantile inverse transformation and inverse standardization are carried out on the flow scene of each period of the prediction period to obtain cascade hydropower flow scenes of the plurality of hydropower station prediction periods of the target drainage basin. The calculation efficiency under the high-dimensional condition and the model compatibility under the small sample condition can be considered, the consistency of the distribution of the generated scene and the real flow distribution of the hydropower station is improved, and the scene generation precision is improved.

The invention provides a terminal initialization method and device, electronic equipment and a storage medium. The terminal initialization method comprises the following steps: acquiring initialization parameters, inertial constraints and visual constraints of a terminal; constructing an information matrix according to the initialization parameters, inertial constraints and visual constraints; performing Schel compensation elimination on the information matrix, and eliminating the feature points from the information matrix to obtain an eliminated information matrix; performing Cholesky decomposition on the eliminated information matrix to obtain an upper triangular matrix and a lower triangular matrix; performing inversion on the image frame state quantity corresponding to the nearest preset moment in the upper triangular matrix to obtain a square root of a covariance, and further determining the covariance; and determining the initialization state of the terminal according to the covariance and the initialization parameter. According to the method disclosed by the invention, the covariance can be quickly solved, and the state quantity fluctuation amplitude is reduced.

The invention relates to the technical field of power distribution network optimization, in particular to a power distribution network operation optimization method considering multiple uncertainties. According to the method, a robust optimization framework considering multiple uncertainties and relevance of a distributed power supply, a novel flexible load and distributed energy storage is constructed, so that the limitation that modeling is dispersed and resource coupling is ignored in a traditional method can be effectively overcome, random characteristics and association relationships of wind and light loads are accurately described by utilizing probability distribution and Cholesky decomposition, and the robustness of the distributed power supply, the novel flexible load and the distributed energy storage is improved. And the flexibility of the system is enhanced through energy storage dynamic balance and flexible load regulation and control, efficient solving is realized by adopting mixed integer linear programming and a column-constraint generation algorithm, and finally, on the premise of ensuring the voltage safety of the power distribution network, the power flow stability and the equipment capacity limitation, the new energy consumption capability is remarkably improved, and the system operation economy is optimized. And a scheduling scheme with high robustness and high implementability is generated.

The invention relates to the technical field of wireless communication, in particular to a low-complexity signal detection method of an MIMO-OTFS system, which comprises the following steps: setting a grouping symbol number according to a channel condition, and calculating a maximum iteration number; in each iteration: calculating a filtered received signal through the channel matrix and the received signal; constructing a matrix psi, processing a structure matrix psi S of the matrix psi by adopting an RCM algorithm to obtain a permutation matrix, and calculating a reordering matrix according to the permutation matrix; performing Cholesky decomposition on the reordering matrix to obtain a banded matrix; calculating the SINR of each symbol which is not detected at present; all symbols which are not detected at present are arranged in a descending order according to the SINR size, the first S symbols in the sorting result are taken to be detected one by one and output, and the channel matrix and the received signal are updated; judging whether the maximum iteration number is reached or not, and if not, continuing the next iteration; according to the method, the time complexity is reduced while the bit error rate performance is ensured through an iterative strategy of reliability symbol detection and interference elimination.

The invention discloses a power grid prediction state estimation method and system based on square root unscented particle filtering, and the method comprises the steps: obtaining the historical operation data of a power grid, generating an initial particle set, and obtaining a state variable estimation value of the power grid and the weight of the state variable estimation value through employing a square root unscented particle filtering method based on a prediction state estimation model; in iteration, calculating a weighted covariance matrix by using a state variable estimation value of the power grid and a corresponding normalized weight, performing Cholesky decomposition on the weighted covariance matrix to obtain a square root of the weighted covariance matrix, and taking the square root as a square root estimation value of each particle after resampling; the number of effective particles is used as a normalized weight initial value of each particle after resampling; the state variable estimation value of the power grid and the weighted mean value of the weight of the state variable estimation value serve as the prediction state estimation result of the power system, and the accuracy and robustness of state estimation of the power system with new energy access are improved.

The invention relates to the technical field of automatic driving, in particular to a self-adaptive trajectory tracking control method for an automatic driving vehicle, and the method comprises the following steps: building a vehicle dynamics trajectory tracking model; based on the vehicle dynamics trajectory tracking model, a high-order strong tracking volume Kalman filtering algorithm is adopted to estimate the lateral force of the tire, and in the high-order strong tracking volume Kalman filtering algorithm, singular value decomposition is adopted to replace Cholesky decomposition, and a fading factor is introduced to dynamically adjust the filtering covariance; according to the tire lateral force obtained through estimation and the tire lateral force obtained through calculation based on a linear tire model, a cornering stiffness correction factor is calculated; and correcting a tire cornering stiffness parameter in a model prediction controller in real time by using the obtained cornering stiffness correction factor, and constructing a self-adaptive model prediction controller. According to the invention, the path tracking performance of the autonomous vehicle under the uncertain working condition can be improved.

The invention provides a fan three-dimensional flow field reconstruction method based on multi-point observation. The fan three-dimensional flow field reconstruction method comprises the following steps: S1, multi-point observation data acquisition: acquiring a wind speed time sequence of N actual measurement points; s2, trend and turbulence separation and frequency domain transformation: obtaining a spatially distributed average wind speed field and a disturbance time sequence, and obtaining actually measured frequency domain disturbance distribution; s3, generating a standard background turbulent flow field: based on a Kaimal turbulent flow spectrum model, combining a coherence function, and generating a standard turbulent flow field conforming to statistical characteristics and frequency domain distribution of the standard turbulent flow field through random phase superposition and inverse Fourier transform; s4, observation constraint superposition and spatial coherence reconstruction: establishing a joint vector matrix; constructing a complex covariance matrix based on the coherence function; carrying out Cholesky decomposition, and extracting an actual measurement-simulation structure coupling relationship; standardization processing of actual measurement constraints; constructing simulation disturbance of a belt actual measurement structure; performing inverse Fourier transform to generate a time domain turbulence field; and S5, superimposing a trend field and outputting a final flow field.

The application discloses a kind of non-stationary wind field high-efficiency simulation method based on dynamic interpolation setting point strategy, including determining simulation point number and its space position;According to the time-varying fluctuation power spectrum model of typhoon, the evolution power spectrum density matrix is calculated;Determine the time range and frequency range of simulation, and it is evenly dispersed into P and Q nodes on time axis and frequency axis respectively;In these discrete nodes, the interpolation node coordinates are selected by dynamic interpolation setting point strategy;By Cholesky decomposition to the evolution power spectrum at interpolation node, the spectral decomposition matrix at the place is obtained, then combined with interpolation method, the spectral decomposition matrix at non-interpolation node is obtained;According to the formula of harmonic superposition method, generate fluctuating wind speed sample.The application is based on classical harmonic superposition method, constructs a kind of dynamic interpolation setting point strategy, can accurately, conveniently select the interpolation node of frequency dimension, effectively reduces the amount of calculation, significantly improves simulation efficiency.

A kind of data service recommendation result diversification method based on determinant point process, comprising the following steps: first, construct service recommendation score auxiliary matrix and service function similarity auxiliary matrix;Second, construct the service information kernel matrix required by determinant point process calculation, process determinant calculation result, optimize calculation process;Third, using cholesky decomposition, quickly judge in the process of greedy iteration, generate diversified reordering service recommendation result.The present application grasps the diversification demand under the service recommendation scene, as a kind of post-processing method, can carry out diversification processing on the basis of existing service recommendation model;While considering the functional correlation and diversity between services;Using the optimized solving method based on decomposition factor and row vector, the diversification process is optimized, and the calculation consumption of each iteration is reduced;For the existing service recommendation algorithm with recommendation accuracy as index, the recommendation supplement of diversity demand is carried out.

The invention discloses a natural gas pipeline time-varying reliability prediction method considering corrosion correlation, and relates to the field of natural gas pipeline safety assessment, and the method comprises the steps: collecting finite continuous ILI data of a natural gas pipeline, building a logarithmic normal probability model of pipeline corrosion defect geometric parameters, and calculating the corrosion defect geometric parameters of the pipeline; acquiring distribution parameters and correlation coefficients of the geometric parameters; based on the distribution parameters and the correlation coefficients, constructing a covariance matrix, and performing Cholesky decomposition to generate an initial corrosion random sample of the pipeline; based on limited continuous ILI data, constructing a gamma process growth model, and performing defect evolution prediction on the initial corrosion random sample to obtain a prediction result; on the basis of the prediction result, the bursting pressure of the natural gas pipeline at the prediction time point is calculated, a limit state equation is constructed, and the time-varying failure probability of the pipeline is calculated through a PHI2 method; through the four steps, the accuracy and scientificity of pipeline corrosion safety risk decision making under the condition of limited detection data are improved.

The present application discloses a matrix inversion method and system for iterative calculation based on Cholesky decomposition, which relates to the field of DSP system optimization technology. The method includes obtaining a target source matrix; performing a first iterative process on the target source matrix based on Cholesky decomposition to generate an upper triangular matrix; performing a second iterative process on the upper triangular matrix to generate an inverse matrix of the upper triangular matrix; performing a conjugate transpose process on the inverse matrix of the upper triangular matrix to generate a lower triangular matrix; wherein the lower triangular matrix is ​​stored in the form of whole column storage; the storage mode of the inverse matrix of the upper triangular matrix is ​​converted into a sequential storage form; performing matrix multiplication process on the inverse matrix of the upper triangular matrix and the lower triangular matrix to generate the inverse matrix of the target source matrix. The present application replaces cumulative summation with iteration, adopts complex multiplication and addition optimization calculation, supports multi-parallel operation, parallelizes zero-filling operation, can adapt to general matrix multiplication module, and reduces calculation time and area overhead.

The invention provides a dynamic optimization method for strip matrix Cholesky decomposition. The dynamic optimization method comprises the following steps: firstly, generating a partitioning strategy according to hardware characteristics and matrix bandwidth, carrying out iterative computation along a main diagonal, decomposing a current diagonal block and updating in-band related sub-blocks in each iteration, and processing out-of-band data through a working area; secondly, fusing dot product, matrix-vector multiplication and vector scaling operations in diagonal block decomposition into a single kernel function, and dynamically allocating threads according to a current column calculation load; and finally, organizing the decomposition process into a fixed assembly line based on the strategy and the fusion kernel, and scheduling the corresponding kernel to execute along a diagonal sequence according to the block type until the decomposition is completed. The invention further provides a dynamic optimization system for the strip matrix Cholesky decomposition. Therefore, by means of the method, the kernel starting overhead is effectively reduced, the data locality and parallel efficiency are improved, and the calculation performance of strip matrix Cholesky decomposition on a heterogeneous platform is remarkably improved.

The application relates to the technical field of state estimation methods of integrated navigation systems, and discloses an adaptive filtering method and device for integrated navigation, which comprises the following steps: obtaining sensor measurement data at a current time; obtaining a predicted state at the current time; constructing a differential feature sequence at a historical time; inputting the differential feature sequence into a hybrid neural network respectively to output a first feature vector and a second feature vector; performing physical constraint parameterization reconstruction based on Cholesky decomposition on the first feature vector and the second feature vector respectively to generate a predicted state covariance matrix and a measurement noise covariance matrix; obtaining Kalman gain at the current time by using a standard analytical formula of Kalman gain; and performing state updating to obtain a posterior state estimation at the current time, so that the technical bottleneck faced in an embedded application scene with a known measurement model, a fixed structure and a low dimension can be solved.

The invention relates to the field of clinical nursing, in particular to a diabetic complication risk detection system based on artificial intelligence, which comprises a data acquisition module, a data preprocessing module, a complication risk feature extraction module, a complication risk prediction module and an intelligent feedback module. A positive definite execution layer is introduced, the output of a time sequence is converted into a lower triangular matrix, a positive definite covariance matrix is generated through Cholesky decomposition, the correlation and uncertainty of input data are further quantified, meanwhile, a lawny eagle algorithm is designed, and by simulating attack and migration behaviors of the lawny eagle, the time sequence of the lawny eagle is calculated. And the search direction is dynamically adjusted by combining the actual parts and the virtual parts of the plurality of individuals, so that the global search capability and the local development capability are improved.

An operation method suitable for a transformer model, the operation method includes the following steps. An input matrix corresponding to an input sequence is mapped to a query matrix according to a plurality of first learnable weights. The input matrix is mapped to a value matrix according to a plurality of second learnable weights. A decomposition matrix is generated by an incomplete Cholesky decomposition according to the query matrix and a transpose query matrix of the query matrix. An intermediate matrix is calculated according to a product of a transpose decomposition matrix of the decomposition matrix and the value matrix. An output matrix is calculated according to a product of the decomposition matrix and the intermediate matrix. An operation system is also disclosed. By using the incomplete Cholesky decomposition, the transformer model can process input sequences with longer lengths.

The present invention discloses a method and device for synthesizing spatially cross-correlated multi-point seismic motions for fitting a response spectrum. The method comprises: utilizing an influence matrix method to obtain a seismic acceleration time history that fits a target response spectrum with high precision, and calculating its Fourier amplitude spectrum and phase spectrum; considering the computational relationship between the power spectrum density function and the Fourier amplitude spectrum, using the power spectrum density function of the obtained time history as an initial value, utilizing random vibration theory and Cholesky decomposition to obtain a spatially cross-correlated multi-point seismic motion time history, further incorporating the phase spectrum of the seismic motion time history that fits the target response spectrum with high precision, and obtaining a spatially cross-correlated multi-point seismic motion for fitting a response spectrum through correction iteration. The present invention overcomes the shortcomings of current multi-point seismic motion time history synthesis technology, which is difficult to ensure the fitting effect with the target response spectrum and the non-stationarity of the synthesized seismic motion, and helps to improve the reliability of the dynamic response analysis results of large-scale engineering structures under the action of multi-point seismic motions.

A kind of static calculation method for prefabricated dam, based on finite element method and contact element method, a large number of joints in prefabricated dam are simulated, the properties of contact element are superimposed into the overall stiffness matrix using traditional finite element integration rule, to ensure that the overall stiffness matrix of prefabricated dam is a symmetric positive definite matrix, the linear equations are solved using Cholesky decomposition, and the iterative convergence speed is improved using preconditioned conjugate gradient method, to ensure the calculation efficiency. C# and FORTRAN programming language are used to write programs to obtain model information file and contact element information file, to complete the heavy pre-processing work of prefabricated dam performance analysis, and programs are written to convert the calculation result file into post-processing file; in addition to the calculation efficiency of the method itself, the whole process of the application is almost completed by program, which greatly improves the performance analysis efficiency of prefabricated dam.

The application particularly relates to a simulation method of a completely non-stationary wind field of a mountain bridge, which comprises the following steps: obtaining an evolution power spectrum function of a completely non-stationary wind field of a bridge to be simulated; determining representative interpolation nodes based on the evolution power spectrum function; the interpolation nodes comprise time domain interpolation nodes and frequency domain interpolation nodes; performing Cholesky decomposition on the interpolation nodes to obtain corresponding node Cholesky decomposition values; then further decomposing the node Cholesky decomposition values into a series of time and frequency function products by a non-negative matrix decomposition method; applying a Hermite interpolation method to establish global time and frequency interpolation functions; and generating a simulation high-efficiency calculation expression of the corresponding completely non-stationary wind field of the bridge based on the global time and frequency interpolation functions, so as to realize the simulation of the completely non-stationary wind field of the mountain bridge. The simulation method in the application can adapt to the completely non-stationary wind field, can take into account the time-varying frequency characteristics and spatial correlation of the wind speed field simulation, and can improve the practicability and efficiency of the bridge wind field simulation.

The invention discloses a large-bearing automatic aligning and leveling device and method for large disc parts of large rotary equipment, and belongs to the technical field of aero-engines. The method comprises the following steps: firstly, acquiring an initial value of an inclination angle of a guide rail by utilizing ellipsoid fitting, a linear least square method and Cholesky decomposition; and the inclination angle between the guide rails, the installation error angle of the sensor and the position of the rotation axis are accurately estimated through joint nonlinear optimization, effective compensation of the measurement error is finally achieved, and then calibration of the aero-engine blade measuring instrument is achieved. According to the'ball-to-three-parameter constraint model 'constructed by the invention, triple geometric constraints are constructed by utilizing the radiuses of the two balls and the distance between the centers of the balls, so that the combined calibration of the attitude parameters of the system is realized, and the measurement precision of the measurement system is effectively improved.

The invention provides an electronic device and an operating method thereof. The electronic device includes a communication circuit and a communication processor, where the communication circuit includes 2N receiving antennas, and the communication processor includes a covariance matrix generation circuit and a whitening filter matrix generation circuit, the covariance matrix generation circuit generates a covariance matrix for the 2N receive antennas based on a measurement of a receive signal, and the whitening filter matrix generation circuit calculates a whitening filter for N receive antennas based on a Cholesky decomposition. The covariance matrix includes a first sub-matrix, a second sub-matrix, a third sub-matrix, and a fourth sub-matrix, and the whitening filter matrix generation circuit calculates a whitening filter matrix for the 2N reception antennas based on a first whitening filter matrix for the first sub-matrix and characteristics of a Cholesky decomposition, the first sub-matrix corresponds to a diagonal sub-matrix of the covariance matrix.

The invention relates to the technical field of communication measurement, in particular to a signal-to-noise ratio estimation method and device, electronic equipment, a storage medium and a program product, and the signal-to-noise ratio estimation method comprises the steps that dual-receiving-antenna terminal equipment receives a wireless signal sent by a base station; acquiring channel characteristics and noise characteristics based on the wireless signal; performing noise cancellation processing on the noise feature to obtain a first noise feature; acquiring signal transmission capability information of a channel between the dual-receiving antenna terminal equipment and the base station based on the channel feature and the first noise feature; and based on the signal transmission capability information and the unit matrix, obtaining first signal-to-noise ratio estimation corresponding to linear multiple-input multiple-output detection and second signal-to-noise ratio estimation corresponding to nonlinear multiple-input multiple-output detection. According to the method and the device, the Cholesky decomposition and matrix inversion operation required by a traditional scheme are avoided, the calculation complexity is greatly reduced, the implementation requirements of a hardware circuit on the area and the power consumption are relieved, and the link state after nonlinear MIMO detection can be accurately represented.

The invention discloses a reconfigurable matrix operation circuit and system, and belongs to the field of high-performance computing hardware accelerators, and the circuit comprises a first operation input end which is used for receiving matrix data; the first operation output end is used for outputting a matrix operation result; the system comprises M cascaded processing units, an independent operation part, a result collection unit and a module top layer control logic, the first processing unit in the M processing units is connected with a first operation input end, the Mth processing unit is connected with a first operation output end, and each processing unit comprises an internal state machine and an operation core; the module top layer control logic is used for setting an internal state machine in the processing unit to realize Cholesky decomposition, LU decomposition, QR decomposition, lower triangular matrix inversion and matrix transposition multiplication operation of a matrix; the result collection unit is used for collecting operation results; and the independent operation component is used for realizing rooting operation and division operation. By means of the reconfigurable matrix operation circuit, hardware multiplexing is achieved, and resources are saved.

The present disclosure provides a terminal initialization method, device, electronic equipment and storage medium. The terminal initialization method comprises: obtaining initialization parameters, inertia constraints and visual constraints of a terminal; constructing an information matrix according to the initialization parameters, inertia constraints and visual constraints; performing Schur complement elimination on the information matrix to eliminate the feature points from the information matrix to obtain an eliminated information matrix; performing Cholesky decomposition on the eliminated information matrix to obtain an upper triangular matrix and a lower triangular matrix; inverting the image frame state quantity corresponding to the nearest preset time in the upper triangular matrix to obtain the square root of the covariance, and then determining the covariance; and determining the initialization state of the terminal according to the covariance and the initialization parameters. The method of the present disclosure can quickly solve the covariance and reduce the state quantity fluctuation amplitude.