109 results about "Column vector" patented technology

The invention provides a vehicle fault root cause diagnosis method, equipment, equipment and a medium, and the method comprises the steps: obtaining a vehicle target fault phenomenon, determining a to-be-detected fault event set based on a preset fault logic relation, constructing an initial scoring matrix, enabling a row vector to correspond to a fault event, enabling a column vector to correspond to the state feature parameters of a plurality of evaluation dimensions, and carrying out the detection of the fault event set; executing the detection task of the highest comprehensive score fault event, obtaining feedback data, updating the initial score matrix parameters based on the feedback data to obtain an updated score matrix, and calculating the comprehensive score of each event based on the updated score matrix again. Iteratively executing the detection task corresponding to the updated highest score event to identify whether the detection event is a fault root cause or not until the fault root cause of the target fault phenomenon is determined; according to the method, through a dynamic priority scheduling mechanism, real-time feedback data is fused in multi-dimensional evaluation, a high-value diagnosis task is executed preferentially, the resource consumption of traditional traversal diagnosis is remarkably reduced, and the fault positioning efficiency is effectively improved.

The invention discloses a mixed hexahedron magnetic data inversion method based on random weighting, and belongs to the field of magnetic signal processing, and the method comprises the steps: carrying out the space division of an underground inversion region through employing a structure and non-structure combined mixed hexahedron grid, and discretizing an underground space into N hexahedron units; generating projection geometries with different size parameters and spatial positions through random assignment, embedding the projection geometries into the subdivided grid model, repeating N times to construct N independent N * 1-dimensional model column vectors, and then performing random sequential splicing to obtain an N * N-dimensional random weighting matrix; constructing an inversion objective function based on the random weighting matrix, and solving to obtain weighting domain magnetization intensity; and in combination with a random weighting matrix and a solving result, obtaining spatial distribution of three-dimensional magnetization intensity, and realizing construction of an inversion image. According to the invention, rapid and fine inversion of the geophysical magnetic data under the rugged topography is realized, and the inversion precision and reliability of the magnetic survey data under the complex topographic condition are improved.

A communication method includes the following. A terminal device sends a first physical uplink shared channel (PUSCH) and a second PUSCH. The terminal device corresponds to N sounding reference signal (SRS) resource sets, the first PUSCH is associated with a first SRS resource set among the N SRS resource sets, the second PUSCH is associated with a second SRS resource set among the N SRS resource sets, and the first SRS resource set is different from the second SRS resource set. The terminal device includes M antenna ports. A first precoding matrix for the first PUSCH and a second precoding matrix for the second PUSCH belong to a first precoding matrix set, a non-zero element in a column vector of each precoding matrix in the first precoding matrix set is less than or equal to M / N, and M and N are integers greater than or equal to 2.

The invention relates to a matrix decomposition device and method based on a memristor cross array, which are suitable for efficient hardware implementation of singular value decomposition (SVD), the memristor cross array receives a conductance value mapped by a Grubrum matrix constructed by a to-be-decomposed matrix and a bit line input voltage converted by a bit line column vector, and outputs a corresponding current; converting the corresponding current into a voltage vector; performing normalization processing on the voltage vector to obtain a normalized vector; judging whether convergence occurs or not; performing normalization processing on the steady-state voltage vector to obtain a final output vector; and according to the final output vector, main characteristic values are extracted based on a first normalization circuit, and main singular values and corresponding singular matrixes are calculated. Compared with the prior art, the high parallel computing characteristic of the memristor cross array is utilized, efficient operation and hardware acceleration of matrix decomposition are achieved, the method has the advantages of being low in power consumption, high in speed and good in expansibility, and the method is suitable for application scenes such as artificial intelligence, signal processing and large-scale matrix operation.

This invention discloses a method for inverting geophysical magnetic data based on random weighting, belonging to the field of magnetic signal processing. The method includes: spatially partitioning the subsurface inversion area using a hybrid hexahedral mesh combining structured and unstructured elements, discretizing the subsurface space into N hexahedral units; generating projected geometries with different size parameters and spatial locations through random assignment, embedding them into the partitioned mesh model, repeating this process N times to construct N independent N×1 dimensional model column vectors, which are then randomly sequentially concatenated to obtain an N×N dimensional random weighting matrix; constructing an inversion objective function based on the random weighting matrix, and solving for the weighted domain magnetization intensity; combining the random weighting matrix and the solution results to obtain the spatial distribution of three-dimensional magnetization intensity, thus constructing the inversion image. This invention enables rapid and precise inversion of geophysical magnetic data under undulating terrain, improving the accuracy and reliability of magnetic data inversion under complex terrain conditions.

This invention provides a method and apparatus for constructing an LSSS access structure that supports collaborative decryption. The method includes: constructing an LSSS access structure according to a collaborative access strategy, wherein an access matrix M in the LSSS access structure is used to determine a first column vector λ, and the first column vector λ includes a second column vector λ. s ; and the sub-access matrix M used to construct the sub-strategy sub Determine the sub-access matrix M sub The corresponding third column vector λ sub The second column vector λ s Equal to the third column vector λ sub The first column vector λ and the third column vector λ sub Used for encryption; Sub-access matrix M sub and the third column vector λ sub Used for decryption. In this embodiment of the invention, the second column vector λ is used... s Equal to the third column vector λ sub Thus, for the third column vector λ sub The relevant calculation results can be used in the relevant calculations for the first column vector λ, saving computational overhead and thus improving encryption and decryption efficiency.

The invention relates to a voice tag generation method and device, electronic equipment and a storage medium, and belongs to the technical field of computers, and the method comprises the steps: carrying out the feature matrix decomposition of a plurality of to-be-processed first voice feature matrixes, and obtaining a decomposed first matrix and a decomposed second matrix; performing hierarchical clustering on column vectors in the first matrix to obtain a tag tree of the first matrix; performing projection processing on the second voice according to the second matrix to obtain a voice feature representation vector of which the dimension is the same as the column number of the first matrix; querying a tag tree, and determining a feature tag of the second voice; wherein the feature tag of the second voice is determined according to the clustering tag in the tag tree mapped by the voice feature representation vector. According to the embodiment of the invention, the construction and maintenance cost of a voice feature tag generation system can be remarkably reduced, and the automation degree and robustness of tag generation are improved.

The invention discloses an RMFE-based distributed matrix multiplication implementation method. The method comprises the following steps: initializing parameters, constructing an RMFE function, splitting a to-be-processed matrix sequence into sequence vectors, sequentially carrying out phi function operation, obtaining a matrix distributed multiplication result through distributed matrix multiplication, and recovering through psi function operation to obtain a multiplication result of a to-be-processed matrix. According to the method, a distributed multiplication problem of a plurality of matrixes on a small domain / Galois ring is converted into a distributed multiplication problem of a single matrix on a large domain / Galois ring, and the calculation overhead on an expansion domain is allocated to the plurality of matrixes, so that the communication overhead required by distributed matrix multiplication is effectively reduced; the method has important application value for a high-speed communication system.

The invention discloses a multi-unmanned aerial vehicle air combat game payment matrix dimensionality reduction method under incomplete information, and belongs to the field of multi-unmanned aerial vehicle air combat dimensionality reduction game decision. An interval matrix game under incomplete information is converted into a matrix game with parameters, then an equivalent determined value matrix game is constructed, an effective convex hull vertex of a row or column vector set of a matrix is found out by using a convex hull vertex algorithm based on a reward value, and a matrix after dimension reduction is established according to the effective convex hull vertex. And then a mapping function is used to establish a relationship between Nash equilibrium of the matrix game before and after dimension reduction, a solution of an equivalent matrix can be obtained by solving the Nash equilibrium of the matrix after dimension reduction, and a Nash equilibrium solution and expected benefits of the interval matrix game are obtained through transformation. Therefore, the problems that the number of the unmanned aerial vehicles is increased, so that dimensionality of a matrix game is rapidly increased, and sensor information is uncertain due to a complex air combat environment are solved.

The present invention relates to the technical field of neural network compression, and proposes a neural network compression method and a neural network accelerator based on a memristor, comprising the following steps: converting the weights of a pre-trained neural network stored in a memristor into a plurality of column vector subsets; performing a pruning operation on each column vector subset to obtain a first pruned column vector subset; searching for an optimal pruning rate layer by layer; obtaining a second pruned column vector subset using the optimal pruning rate, and encoding the second pruned column vector subset to obtain a plurality of mask subsets; obtaining each third pruned column vector subset using each mask subset; solving the optimal third pruned column vector subset, and clustering them to obtain a clustering sub-matrix set; mapping the clustering sub-matrix set to a memristor, and after the mapping is completed, retraining the pre-trained neural network based on the memristor to complete the compression of the memristor-based neural network.

The application discloses a new correlation image recognition and machine learning method based on fuzzy monotony, and belongs to the technical field of artificial intelligence of pattern recognition and machine learning; the method is defined as FMMCA, which evaluates local fuzzy monotone correlation by comparing row vectors and column vectors of an image matrix pair by pair, then the local correlations are weighted and accumulated, and finally the global fuzzy monotone correlation between images is obtained. The application directly uses fuzzy monotone correlation analysis to replace classical correlation analysis for multi-view research, so that the problems existing in classical correlation analysis do not exist, and the fuzzy monotone method feature does not need to be measured statically by distance, but can be measured dynamically by interval change, so that the influence of some noise is offset, the performance is improved, a new fuzzy monotone machine learning method is formed, the optimization of a focus loss function is not needed, the parameters are few, the robustness is good, and the computing power is small.

The invention relates to the technical field of electrical digital data processing, and discloses a Web application automatic generation and data processing method based on spreadsheet mapping, which comprises the following steps: mapping spreadsheet cells into enumeration values to construct a type fingerprint matrix; calculating a type purity index of a column vector, identifying a vertical blocking band, and splitting the matrix into a plurality of orthogonal sub-matrixes which are not communicated with each other; calculating a type distribution difference value of adjacent row vectors in the sub-matrix to determine a horizontal logic partition line so as to define a logic data block; a Web application object model is assembled based on an arrangement mode of logic data blocks, signal crosstalk between transverse side-by-side data entities is cut off by introducing a vertical topology decoupling mechanism based on column entropy, and the technical problem that downlink feature scanning is easily interfered by orthogonal noise in a multi-table mixed arrangement scene is solved. And the deterministic structure reconstruction of the complex two-dimensional heterogeneous data under the condition of no preset template is realized.

The present application belongs to the field of communications. Provided are a channel state information processing method and apparatus, so as to further improve the channel estimation performance. The method comprises: a first communication apparatus determining some column vectors in an eigenvector matrix corresponding to a channel between a second communication apparatus and the first communication apparatus, and indicating the some column vectors to the second communication apparatus by using first information as a carrier; and the second communication apparatus performing channel estimation on a first channel on the basis of the first information. Compared with performing channel estimation on a channel by means of a second communication apparatus using a complete eigenvector matrix, performing channel estimation on a channel by using some column vectors can effectively filter out noise in the channel, reduce inaccurate information in the eigenvector matrix, further improve a signal-to-interference-plus-noise ratio of the channel, and improve the channel estimation performance.

The invention provides a large language model binarization quantification method and system based on instructive alternate optimization, and relates to the technical field of large language model deploying.The method comprises the steps that a weight matrix is divided into an important area and an unimportant area by evaluating the influence degree of all weight parameters on the performance of a large language model; carrying out binaryzation on all areas of the weight matrix, and alternately optimizing a row vector scaling factor and a column vector scaling factor by adopting a first-order row-column alternate optimization iteration mode to obtain a first-order reconstruction weight matrix; and carrying out binaryzation again on the important region of the first-order reconstruction weight matrix, carrying out optimization by adopting a first-order and second-order row-column alternating optimization iteration mode to obtain a second-order reconstruction weight matrix, and correspondingly taking the second-order reconstruction weight matrix as a weight parameter after the large language model is quantized. The quantization error of the key weight parameter can be effectively reduced, the quantization precision is high, the quantization process completely depends on the internal structure information of the large language model, and the edge deployment compatibility is high.

The application relates to the technical field of electric digital data processing, and discloses a Web application automatic generation and data processing method based on electronic table mapping, which comprises the following steps: mapping electronic table cells into enumeration values to construct a type fingerprint matrix; calculating a type purity index of a column vector and identifying a vertical blocking band, and splitting the matrix into a plurality of mutually unconnected orthogonal sub-matrices; calculating a type distribution difference value of adjacent row vectors in the sub-matrices to determine a horizontal logical segmentation line, so as to define a logical data block; and assembling a Web application object model based on an arrangement mode of the logical data block. The application introduces a vertical topology decoupling mechanism based on column entropy, cuts off signal crosstalk between horizontally arranged data entities, solves the technical problem that a horizontal feature scanning in a multi-table mixed arrangement scene is easily interfered by orthogonal noise, and realizes deterministic structure reconstruction of complex two-dimensional heterogeneous data under the condition of no preset template.

This invention provides a method for solving matrix eigenvalues ​​based on a resistive variable cell array (RVA) circuit. The method includes: setting the node conductance values ​​of the RVA array; writing the initial values ​​of each element of the matrix (A-βI) into the RVA array; inputting the current signal corresponding to the random column vector b through the bit line terminal of the RVA array; simultaneously connecting the RVA unit corresponding to β to the diagonal node of the RVA array via a switch to complete the solution of the linear equation system (A-βI)x = b, and solving and storing the infinite norm of the solution vector a; connecting the RVA unit corresponding to β to a resistance control circuit via a switching circuit to update the resistance value; repeating the above process until the traversal of β is completed, the β values ​​corresponding to all extreme values ​​of the infinite norm are the total eigenvalues ​​of the matrix. This invention employs an in-memory computation method in the analog domain, which can improve computation speed while reducing energy consumption.

Systems and methods for providing an S-box. The methods comprise: implementing a plurality of layers on a field programmable gate array, wherein each of the layers comprises a substitution sublayer and a mixing sublayer. The substitution sublayer comprises a plurality of μ-boxes that are each configured to perform a different randomly chosen non-linear bijective mapping from GF(24) to GF(24). The mixing sublayer is configured to (i) compute a plurality of product terms each representing a product of an element of a maximum distance separable matrix M and an output from one of the plurality of μ-boxes, and (ii) compute a plurality of column vector elements each comprising a sum of respective ones of the plurality of product terms.

This invention provides a method, apparatus, and storage medium for estimating the geometric parameters of a spherical cone target. The method includes: acquiring sparse echo data of the spherical cone target; determining the value range of the parameters to be estimated based on prior information of the spherical cone target, obtaining a parameter combination set including several parameter combinations; the parameters to be estimated include the base radius and the semi-cone angle; substituting the parameter combinations in the parameter combination set into a geometric diffraction theory model to obtain the RCS data of the spherical cone target; performing two-dimensional imaging and transformation processing on the sparse echo data and the RCS data respectively to obtain a first column vector and a second column vector; constructing a parameter dictionary based on the second column vector; and inputting the first column vector and the parameter dictionary into a preset optimization algorithm for iteration to obtain the estimated target parameter values. The geometric parameter estimation method for spherical cone targets provided by this solution reduces the computational load while ensuring the accuracy of parameter estimation.

The invention discloses a median positioning method and system, electronic equipment and a storage medium, and relates to the field of data processing. The method comprises the following steps: acquiring N pieces of binary data and a single-column vector consisting of N elements 1; screening middle-bit elements on the same bit in sequence from high to low, and in a single-column vector, adjusting an element 1 corresponding to invalid data to an element 0; wherein for any invalid data, elements of subsequent bits are adjusted to be elements of the current bit in the invalid data every time one median element is screened out, and for any invalid data, elements of subsequent bits are adjusted to be elements of the current bit in the invalid data; after a median element is screened out and elements in the single-column vector are adjusted, whether the number of elements 1 in the single-column vector is 1 or not is judged, and if yes, binary data corresponding to the elements 1 are obtained and serve as median values; if not, continuing to screen the median elements of the next bit and adjusting the elements in the single-column vector until the number of the elements 1 in the single-column vector is 1 or screening of the median elements of all the bits is completed. Therefore, the median searching efficiency is improved.

The application provides a seismic inversion method and system based on dynamic change of Lagrange operators, and the method comprises the following steps: obtaining a column vector composed of 2n+1 Lagrange operators according to a preset prior velocity model and an initial velocity model, wherein n is a positive integer; substituting the column vector into a preset inversion iteration formula to obtain a corresponding number of inversion velocity models, and obtaining a target function according to the inversion velocity models; determining an inversion velocity model with the minimum function value of the target function as an iteration model, and repeatedly determining the iteration model until the minimum value of the target function reaches a preset value of the target function. The application can facilitate rapid reduction of the target function by using dynamic Lagrange operators in the inversion process, and ensure the speed and accuracy of the inversion result.

Various embodiments of the present disclosure are directed to a photonic circuit for a vector-matrix operation. A source pixel is configured to generate a light beam. An optical fan-out structure is configured to generate a plurality of copies of the light beam. A plurality of modulator pixels are configured to respectively transmit the plurality of copies with individual transmissivities to generate a plurality of transmitted light beams. A plurality of detector pixels are configured to accumulate charge respectively in response to the plurality of transmitted light beams. A controller is configured to control the source pixel and the plurality of modulator pixels to modulate an intensity of the light beam and the individual transmissivities to perform the vector-matrix multiplication operation. The intensity is modulated to temporally encode an input row vector, and the individual transmissivities are modulated to temporally encode corresponding column vectors of a weight matrix.

The application discloses a signal recovery method and system based on a block Gauss-Seidel algorithm, a medium and equipment, and the method comprises the following steps: obtaining a noise observation vector and a perception matrix; initializing the number of iterations, an estimated signal, a residual vector and a hyperparameter; setting an iteration relative error, calculating the inner product of the column vector of the perception matrix and the estimated vector obtained in this iteration, calculating the residual vector, and selecting the maximum homogeneous residual; determining the control index set of the target block; randomly extracting the corresponding working column from the coefficient matrix to obtain a submatrix; substituting the target block of the submatrix into the iteration formula of the block Gauss-Seidel algorithm without pseudo-inverse calculation to obtain the estimated value of the next iteration, and repeating the loop iteration until the threshold condition is met to stop the iteration; and outputting the estimated sparse signal obtained in the last iteration. The application improves the convergence rate and recovery effect of signal recovery and improves the recovery performance of the sparse signal.

The application provides a distributed security outsourcing computing processing method based on sparse matrix transformation, and relates to the technical field of computers, and the method comprises the following steps: acquiring a first matrix and a second matrix; randomly generating a key set comprising a binary permutation matrix, a sparse triangular matrix and a random vector; performing primary encryption on the first matrix and the second matrix based on the binary permutation matrix; performing secondary encryption on the primary encrypted matrix based on the sparse triangular matrix; distributing the secondary encrypted first secondary encrypted matrix to each edge end in an integrated manner, distributing the second secondary encrypted matrix to each edge end in an even manner after dividing the second secondary encrypted matrix into column vectors according to columns, and enabling the edge end to perform a subtask calculation operation by using the received matrix and return the result to the user end; receiving the calculation results of the edge ends, and aggregating the results; verifying the reliability of the aggregated results by using the random vector, and decrypting the final calculation result. The scheme can improve the security and timeliness of outsourcing computing.

The application discloses an online fault diagnosis method for a hydro-generator based on a potential interval distance of a through screw, relates to the technical field of generators, and is suitable for detecting a stator winding ground fault. Voltage collection devices are additionally arranged on a plurality of through screws of the hydro-generator, real-time induced potential is acquired, a historical data matrix is established, and a data buffer matrix is updated at interval time; the data buffer matrix is divided into a data evaluation matrix, a fundamental wave and an odd harmonic phase are extracted through Fourier decomposition, a harmonic phase matrix is constructed, a phase difference matrix is obtained by calculating normalized phase differences of adjacent columns, a quartile distance is solved after being converted into a column vector array, a dynamic quartile distance array is formed, abnormal limit values are determined by combining a box plot principle and an error level of the voltage collection device, and faults are determined by traversing the array. The method does not need additional sensing devices and traditional threshold setting, is convenient to operate, has low implementation difficulty, and can accurately and timely identify faults.

The invention provides a global response reconstruction method and device based on an intelligent enhanced reduced-order model, and the method comprises the steps: obtaining the local structure data of a to-be-reconstructed scene, and enabling the local structure data to be obtained through the collection of the to-be-reconstructed scene based on a sensor; an orthogonal base space matrix under model order reduction is obtained, the column vector of the orthogonal base space matrix is an orthogonal base vector, and the orthogonal base vector is expanded into a low-dimensional subspace; and based on the local structure data and the orthogonal base space matrix, performing reconstruction to obtain a high-dimensional global response of the to-be-reconstructed scene matched with the local structure data. A high-dimensional and complex structure response field is projected to a low-dimensional subspace by using an orthogonal base space matrix with physical consistency, so that the high-dimensional global response of a to-be-reconstructed scene can be obtained through accurate reconstruction and deduction based on local structure data of the to-be-reconstructed scene.

The invention relates to a matrix operation method, a matrix operation device, computer equipment, a storage medium and a program product. The method comprises the steps that in response to operation instructions of a first matrix and a second matrix, matrix blocks are distributed to threads needing to be called by a heterogeneous accelerator in the heterogeneous computing system; and sending an operation instruction to the heterogeneous accelerator, and indicating each thread in the heterogeneous accelerator to operate the distributed matrix blocks in parallel. Wherein each matrix block comprises at least one first matrix row vector and at least one second matrix column vector. By adopting the method, the matrix operation efficiency can be improved.

The global relationship attention guided cross-view geo-location method belongs to the technical field of computers, uses a deep residual network as a backbone network, and uses a global relationship attention module to capture more robust image global structure information for matching. A double-branch network including a global branch and a local branch is designed to capture deep features with rich semantic information and local features with multi-scale context information, respectively. In the local branch, dilated convolution is used to increase the receptive field of the feature map, and a square ring division strategy is used to divide the feature map at four scales. For the feature maps of each branch, they are converted into column vector descriptors, and then a classifier is used to obtain the predicted category of each column vector. Finally, a cross-entropy loss function is used to measure the difference between the predicted category and the real category of the image, thereby improving the geo-location accuracy.

The invention discloses a measurement data fusion method, which is applied to a large-size measured part and belongs to the field of data fusion technology. The method comprises: classifying a plurality of measuring instruments according to measurable parameters; combining the numbers of the plurality of measuring instruments into an instrument matrix; measuring the measurement values ​​of each characteristic parameter of a standard part; combining the measurement values ​​of the characteristic parameters into a measurement matrix; normalizing the column vectors of the measurement matrix to obtain a normalized matrix; establishing an augmented matrix according to the instrument matrix and the normalized matrix; calculating the correlation coefficient between the column vectors of two augmented matrices, and judging whether the correlation coefficient is greater than a preset value; when the correlation coefficient is greater than the preset value, performing dimensionality reduction processing on the column vectors of the two augmented matrices; inputting the column vectors of each dimensionality reduction matrix into a data fusion model for training; and terminating the training of the data fusion model when the deviation between the output value and the standard value of each characteristic parameter of the standard part is within a preset range.

The invention provides a general interference suppression method and system based on adaptive window short-time Fourier transform, and the method comprises the steps: carrying out the adaptive initialization of a Gaussian window width parameter according to the amplitude standard deviation of each pulse signal, carrying out the short-time Fourier transform, obtaining a time-frequency matrix of each pulse, carrying out the vectorization, carrying out the arrangement according to columns, and obtaining a time-frequency matrix of each pulse; constructing a time frequency-pulse matrix; performing complex RPCA decomposition on the matrix, and separating a low-rank matrix and a sparse matrix; adjusting a Gaussian window width parameter according to energy distribution feedback of each column vector of the sparse matrix, and performing iterative optimization until convergence to obtain a final sparse matrix; extracting interference time-frequency column vectors, reconstructing the interference time-frequency column vectors into an interference time-frequency matrix, and performing inverse short-time Fourier transform to obtain a time-domain interference signal; and finally, subtracting the corresponding interference signal from the original echo signal. According to the method, various types of interferences can be effectively suppressed, and particularly, the suppression effect can be remarkably improved on the premise of protecting target signals when broadband interferences and complex deception interferences are handled.

The invention discloses an LDPC (Low Density Parity Check) check matrix construction method for a high-bit-rate flash memory. The construction process of a check matrix is divided into four steps of determining check matrix parameters, constructing a high-bit-rate basis matrix by a regular column weight method, constructing a shift parameter matrix by a basis matrix expansion method and filling the check matrix by taking the shift parameter matrix as a unit. Starting from establishment of check matrix parameters, setting parameters of the high-bit-rate flash memory and a corresponding matrix scale; a high-bit-rate basis matrix is generated through a regular column reconstruction method, and repeatability of a basis matrix column vector is reduced; a high-bit-rate basis matrix is used as a base, a shift parameter matrix is constructed based on a basis matrix expansion method, and short rings are reduced to improve performance; and filling the check matrix by using the cyclic shift matrix as a basic unit to obtain a final construction result. According to the method, the short loop in the check matrix can be eliminated to obtain better performance, the difference of base matrix column vectors is kept, the adverse effect of an error code flat layer on effective information is reduced, and the method also has the advantages of high applicability, low storage space occupation and the like in a flash memory.