105 results about "Random matrix" patented technology

The invention relates to the technical field of remote sensing image processing, in particular to an end-to-end neural network InSAR (Interferometric Synthetic Aperture Radar) phase unwrapping method based on mixed attention, which takes U-Net as a basic framework, extracts key features and improves resolution through down-sampling and up-sampling operations, thereby effectively recovering detail information. Specifically, a convolutional block attention module (CBAM) and two receptive field modules, namely cavity spatial pyramid pooling (ASPP) and a receptive field module (RFB) are combined to construct an RFAUNet network model, and then simulation data sets generated by two methods of digital elevation inversion and random matrix generation are used for training a neural network until a good unwrapping effect is obtained. And finally, carrying out a phase unwrapping experiment on simulation data and real data to verify the effectiveness and robustness of the RFAUNet network model.

The invention discloses an ultra-harmonic rapid detection method based on compressed sampling and multiple measurement vectors, and solves the problems of high sampling rate, low reconstruction efficiency and insufficient spectrum resolution in the prior art. Sub-Nyquist compressed sampling is realized through a sparse random matrix, and the hardware burden is reduced; a dynamic step size sparse self-adaptive matching pursuit algorithm (DSSAMP) is adopted to reconstruct signals, the step size is adjusted in a self-adaptive mode in combination with residual energy changes, and the convergence speed and precision are balanced; an improved multi-measurement vector model (IMMV) is constructed through framing processing and introduction of interpolation factors and prior frequency point screening, and the calculation efficiency is improved; and finally, extracting super-harmonic frequency, amplitude and phase parameters based on an orthogonal matching pursuit (OMP) algorithm. According to the method, the high resolution is ensured, the sampling rate and the calculation complexity are remarkably reduced, and the real-time requirement is met.

The invention relates to the technical field of medical informatization, in particular to a ward distress call method and system based on intelligent medical treatment, and the method comprises the steps: obtaining patient state data collected by a plurality of sensors in a ward; receiving a ward help-calling instruction; constructing a multi-modal data fusion matrix based on the patient state data; executing random matrix transformation and feature extraction according to the multi-modal data fusion matrix; based on the extracted features, topological feature mapping and anomaly detection are carried out; executing group theory base risk assessment according to the topological features; calculating a chaotic power system early warning index based on a group theory risk assessment result; determining a call-for-help judgment result according to the early warning index; a corresponding response strategy is executed based on the call-for-help judgment result, the system can comprehensively grasp the state of the patient through the multi-modal data fusion technology, and the evaluation accuracy is greatly improved. And secondly, the robustness of the system is enhanced by random matrix transformation, so that the system can cope with a complex and changeable medical environment.

The invention discloses a coal-fired power generation data encryption method and device, equipment and a medium, and relates to the technical field of encryption, and the method comprises the steps: carrying out the preprocessing of target coal-fired power generation data; converting the preprocessed target coal-fired power generation data into a two-dimensional matrix; performing multi-round iterative encryption on the two-dimensional matrix in a manner of generating a random matrix for encryption by using chaotic mapping to obtain an encrypted data matrix; carrying out steganography on the encrypted data matrix to obtain steganographic data; the steganographic data is used for storage or transmission. According to the invention, the data security can be improved.

The invention discloses a load twinborn modeling and prediction method and system based on multi-source heterogeneous feature fusion, and the method comprises the steps: collecting original data from a user side, a power grid side and an external environment side, and carrying out the preprocessing, and obtaining multi-source heterogeneous feature data; extracting a structural spectrum index and a structural disturbance embedded spectrum entropy index of the multi-source heterogeneous feature data based on a random matrix theory; training a load prediction model based on the multi-source heterogeneous feature data, the structural spectrum index and the structural disturbance embedded spectrum entropy index; and constructing a virtual load twinborn model based on the trained load prediction model, the user static parameters and the dynamic variables, and performing multi-granularity load prediction. According to the method, the prediction precision and generalization ability can be remarkably improved, the interpretability and prediction precision of user load behaviors are improved, active perception and intelligent regulation and control of power system operation are supported, and the method has good engineering expandability and is suitable for dynamic management and control requirements of park-level, regional-level and distributed load resources.

The invention provides an image encryption method, system and device based on an improved random matrix interpolation iteration sequence and a medium, and relates to the technical field of image encryption, a plaintext image is acquired, and a two-dimensional digital matrix of preset pixels is generated; setting an initial coordinate value and a key parameter matrix; on the basis of a Monte Carlo sampling driven dynamic iteration mechanism, a two-channel encryption sequence is formed after multiple times of iteration; performing analog-to-digital operation and numerical value normalization operation on the encrypted sequence to generate a standardized digital matrix; after the first encryption transformation is completed, generating a primary ciphertext matrix; processing the primary ciphertext matrix to generate a final ciphertext; and outputting the final ciphertext, and performing format packaging to generate an encrypted output file. By performing format conversion processing on the plaintext image to generate the two-dimensional digital matrix of the preset pixel, images of different formats and sizes can be processed in a unified manner, the compatibility and universality of an encryption algorithm are improved, and the method can be applied to various types of image encryption scenes.

The present application discloses a method for making patrol path decisions of a swarm of unmanned aerial vehicles (UAVs) under resource constraints, which relates to the technical field of UAV path planning. The method comprises: discretizing the actual physical locations to be patrolled to construct an undirected topological graph; generating a steady-state distribution of each patrol node according to the topological constraints and the importance of the nodes; generating a plurality of transfer matrices with the same steady-state distribution but different transfer characteristics according to a multi-stage entropy-driven random matrix optimization algorithm; initializing the position of a navigator and determining the path selected by it according to the transfer matrix; implementing adaptive active positioning decisions under positioning constraints according to the navigator's reference path to ensure path tracking effects; according to the navigator's path selection and tracking, the followers form a humanoid grouping cluster with the navigator through a reward function; according to the multi-state transfer matrix and the humanoid grouping, automatically switching to the next transfer matrix when a transfer number threshold is reached, thereby realizing intelligent patrol path decisions of the UAV swarm under resource constraints.

The invention relates to the technical field of medical artificial intelligence, and discloses a brain disease prediction method based on double encoders and a diffusion model, and the method comprises the steps: carrying out the preprocessing of a functional magnetic resonance image, and constructing a brain function network; data enhancement of semantic preservation is achieved through a diffusion model, a dual random matrix and a cosine scheduling strategy are adopted in the noise adding process, and a GraphTransform neural network containing global topological features is utilized in the denoising process; the spatial features of the brain network and the time dynamic features of the BOLD signals are respectively extracted by using double encoders; designing a triple contrast learning mechanism to optimize cross-dimension feature interaction; and finally migrating to a downstream classification task to realize disease prediction. Small sample overfitting is relieved through diffusion enhancement, and the diagnosis reliability is improved; fusing spatial-temporal characteristics to assist multi-dimensional pathological analysis; and the cross-site adaptability of the model is enhanced, and collaborative analysis of multi-center heterogeneous data is supported. The method is suitable for auxiliary diagnosis of cerebral diseases such as infantile autism.

The present disclosure describes techniques of releasing data while protecting individual privacy. A dataset may be compressed by applying a first random matrix. The dataset may be owned by a party among a plurality of parties and there may be a plurality of datasets owned by the plurality of parties. A noise may be added by applying a random Gaussian matrix to the compressed dataset to obtain a processed dataset. The processed dataset ensures data privacy protection. The processed dataset may be released to other parties.

The invention discloses a body fat prediction system and method based on a multi-band impedance signal, and relates to the technical field of big data analys.The method comprises the steps that impedance data of different segments under different frequency bands, the body fat amount, the lean body weight and the human body weight are collected; integrity verification is carried out on the impedance data, missing values are filled with mean values, abnormal values are removed and corrected, and meanwhile timestamp alignment is carried out; extracting features to construct an original impedance vector, and mapping the original impedance vector to a high-dimensional space through a random matrix to generate a random mapping function; constructing a time sequence regression model, taking the embedding dimension as an input layer, taking the body fat amount and the lean body mass as an output layer, and using mean square error calibration and back propagation updating; and through a mean square error weighting evaluation model, outputting a performance standard result. The system comprises a data acquisition module, a data preprocessing module, a random distribution embedding module, a time sequence model training module and a display module. The method can adapt to impedance characteristics of different crowds, is suitable for portable terminal or smart home body measurement, and can be used at high frequency in daily life.

The application provides a multi-AUV system formation coordination control method with non-convex control input constraints, converts the coordination control of the multi-AUV system formation without a leader into a formation consistency problem, and defines the consistency state of the multi-AUV system formation without a leader. In the case that the water acoustic communication bandwidth is limited, the application selects a double-layer communication channel mainly composed of position and speed information. Considering the existence of communication delay and non-convex control input constraints, a discrete-time leaderless formation consistency constraint controller algorithm with communication delay is designed by introducing a constraint operator. By using the properties of graph theory, random matrix and SIA matrix, and selecting appropriate controller parameters, the multi-AUV system formation can reach the defined consistency state and maintain the stability of the formation shape.

The application discloses an image transmission method and device, a storage medium and a program product. In the method, a predetermined random matrix and an ordering value mapping table are acquired from a server; an original image matrix is transformed based on the random matrix to obtain an intermediate matrix; and finally, each pixel value in the intermediate matrix is replaced based on the ordering value mapping table to obtain an encrypted image. According to the technical scheme, each pixel in the original image matrix is subjected to matrix transformation and ordering reset, so that the original image is encrypted, the data processing amount of the image data in the encryption process is reduced, and the security of the image data in the plaintext transmission process is effectively improved.

The invention discloses a compressed sensing method and system based on a guaranteed-dimension semi-tensor product, a medium and equipment. The method comprises the following steps: acquiring a vector expression form of an original image; converting the obtained vector into a sparse vector representation through discrete cosine transform; generating a Gaussian random matrix, and performing dimensionality-preserving semi-tensor product operation through all-one vectors with weights to construct a measurement matrix; sampling the sparse vector by using the measurement matrix to obtain a compressed transmission vector; and the Gaussian random matrix and the transmission vector are sent to a receiving end, the receiving end reconstructs and recovers the original image based on an l1 norm optimization algorithm, and compressed sensing transmission is completed. According to the method, the measurement matrix is improved based on the semi-tensor product of the guaranteed dimension, and the measurement matrix construction method can reduce the size of the generated Gaussian random matrix, so that the memory is saved, the speed in the transmission process is higher, and the bandwidth cost is saved.

The invention discloses a power grid harmonic wave and inter-harmonic wave detection method and device based on distributed multi-frequency measurement data, equipment and a medium. A sine wave method comprises the following steps: detecting harmonic wave and inter-harmonic wave components existing at each sine wave measurement point based on a random matrix theory; determining the frequency and amplitude of each harmonic wave and inter-harmonic wave of each sine wave measurement point by using a rotation invariant technology; clustering the frequency of each harmonic wave and inter-harmonic wave on each measurement point by using a density-based noise application space clustering method to obtain the target frequency of each frequency cluster; and determining harmonics and inter-harmonics of the power grid according to the target frequency and the amplitude. Each harmonic and inter-harmonic component existing on a measurement point is detected through a random matrix theory and a rotation invariant technology, accurate frequencies and amplitudes of the harmonic and the inter-harmonic are obtained, and more accurate frequencies of the harmonic and the inter-harmonic of a power grid are obtained by fusing data of a plurality of distributed measurement points through a clustering method. Therefore, the detection capability of harmonic waves and inter-harmonic waves of a power grid is improved.

The application discloses a bit data transmission scheduling method based on 6G space-ground integrated quantum network, a storage medium and equipment, and comprises the following steps: constructing a 6G space-ground integrated quantum communication network; searching for nodes meeting network scheduling task requirements in the quantum communication network, constructing a candidate node set, and screening out a determined best emission node; sending a quantum state to a receiving end through a quantum key distribution protocol at the best emission node, calculating a quantum bit error rate, selecting a candidate relay node from the candidate node set with the minimum quantum bit error rate as the target, and forming a quantum transmission channel; and the quantum transmission channel prepares a quantum key by using an entangled state, generates a random matrix density, generates a ciphertext through the random matrix density, and transmits the ciphertext to the receiving end. The application can reasonably schedule network resources, ensure data security through quantum communication, and the like.

The invention relates to an adaptive multi-scale decomposition echo state network chaotic time sequence prediction method suitable for meteorological prediction, and belongs to the field of chaotic time sequence prediction. Comprising the following steps: performing multi-scale decomposition on an input sunspot number sequence by using an HP filter; each sub-sequence obtained through decomposition is distributed to an ESN sub-network; inputting a weight in the ESN sub-network by adopting a Xavier weight initialization method, introducing a composite activation function in reservoir nonlinear updating, and performing state splicing by adopting a state splicing strategy; a sparse random matrix is constructed, a unit matrix scale factor is introduced, and the spectral radius is strictly controlled; each ESN sub-network independently trains an output weight, ridge regression is adopted for solving, integration and reconstruction are carried out after corresponding component prediction is completed, and if a target sequence is a sunspot activity index, the model finally outputs a sunspot number prediction value in a future time step; according to the method, high-precision, stability and robustness prediction of the chaotic time sequence can be realized.

The application discloses a large language model parameter fine-tuning method and device based on manifold constraint multi-track adaptation and a medium. In view of the problems of unstable training and limited expression capacity of an existing low-rank adaptation technology, a plurality of parallel low-rank tracks are constructed, and a double random matrix is introduced to constrain the information flow between the tracks, so that the stability of gradient propagation is ensured. Meanwhile, the expression capacity of the adaptation module is enhanced under a limited parameter budget by dynamically fusing the outputs of the tracks through a dynamic gating mechanism. The method can realize stable, efficient and high-performance model fine-tuning by fine-tuning a small number of parameters, and reasoning has no additional overhead, and is particularly suitable for application scenarios with limited resources and high stability requirements, and has wide practical value.

The invention belongs to the field of magnetic resonance sounding signal noise filtering, and particularly relates to a magnetic resonance sounding signal de-noising method based on backtracking generalized OMP, and the method comprises the steps: employing a magnetic resonance sounding water detector to carry out the sampling of a sparse signal at a frequency lower than the Nyquist frequency, and obtaining a noisy MRS signal; converting the noisy MRS signal into a two-dimensional matrix signal; dictionary learning is carried out on the two-dimensional matrix signals through a KSVD algorithm, and an updated dictionary is obtained after iteration; calculating a recovery matrix and measurement data by using an independently distributed Gaussian random matrix; performing iterative operation on the measurement data by using a backtracking generalized orthogonal matching pursuit algorithm, selecting an atomic sequence of maximum projection of the measurement data and a recovery matrix module, and obtaining a sparse coefficient matrix of the measurement data under the recovery matrix; and calculating a recovery signal according to the dictionary and the sparse coefficient matrix of the measurement data under the recovery matrix.

The invention provides a port image analysis method, system and device based on a random matrix, and a medium, and relates to the technical field of port inspection image processing, and the method comprises the steps: obtaining a port inspection image; creating two random matrixes; performing interpolation operation on the two random matrixes by using a bilinear interpolation function; constructing a chaotic system, and adding disturbance parameters to enhance nonlinearity in combination with an interpolation function and a random matrix to obtain a Jacobian matrix; calculating a maximum index value; if the maximum index value is greater than 0, determining that the system is in a chaotic state; drawing a curve of indexes changing along with time, and analyzing sensitive parameters of the system; row and column replacement and bitwise XOR processing are carried out on pixels by using the stable segment of the chaos sequence; performing decryption processing; and outputting the comparison diagram. According to the method, the dynamic key is generated in combination with the random matrix and the sensitive parameters by adopting an encryption mode based on the chaotic sequence, and the image pixels are subjected to multi-stage encryption processing by utilizing the transient state of the chaotic system, so that the security of an encryption algorithm is improved.

The present application belongs to the field of magnetic resonance signal noise filtering, specifically a sparse denoising method for ground magnetic resonance signals using a combined dictionary, which divides the magnetic resonance signal into frequency bands to reconstruct the power frequency harmonic components, and eliminates the power frequency harmonics from the magnetic resonance signal to obtain a power frequency-free signal; a trajectory matrix is ​​constructed using a Gaussian random matrix. Z , using the power frequency signal to construct the trajectory matrix Y , trajectory matrix Z Take the front K After column normalization, the initial dictionary is obtained D , trajectory matrix Y As original samples used to construct sparse coefficient matrix X ; Complete the initial dictionary through K-SVD dictionary learning D and the sparse coefficient matrix X The updated dictionary and sparse coefficient matrix are used to reconstruct the trajectory matrix to obtain the trajectory matrix W ; Take the trajectory matrix W The first row of is used to obtain a pure magnetic resonance signal, which can effectively remove the MRS signal noise in complex electromagnetic interference scenarios under the condition of a single signal acquisition.

The invention discloses an atmospheric laser communication turbulence compensation method based on a novel Kolmogorov-Zakharov model, and belongs to the technical field of atmospheric laser communication. The objective of the invention is to solve the technical problem that the transmission quality and stability of atmosphere laser communication are affected due to reduction of compensation precision in a strong turbulence scene in the prior art. The method comprises the following steps: acquiring phase distribution and light intensity fluctuation of a laser signal in real time to obtain an atmospheric refractive index structure constant, mapping a model parameter and an atmospheric laser communication physical quantity, and updating a model based on the atmospheric refractive index structure constant; based on the model and a Helmholtz equation of atmospheric laser transmission, an atmospheric laser channel KZ turbulence random matrix model is obtained and solved, and a prediction result is obtained; and then turbulence dynamic compensation is carried out based on a prediction result. The method is mainly used for atmosphere laser communication turbulence compensation.

This invention provides a method, medium, and system for acoustic early warning of thermal runaway in lithium-ion batteries based on feature reconstruction, belonging to the field of lithium-ion battery technology. The invention constructs a positive sample set by collecting safety valve opening sounds through multi-condition thermal runaway experiments, and expands the samples using data augmentation. Multi-resolution Mel spectra are extracted from the audio signals. These Mel spectra are then input into a complex-domain phase-aware separation model for joint estimation of complex-domain amplitude masking and phase residuals. Physical prior corrections are applied to the reconstruction results using a sound source separation algorithm based on wave equation time-frequency inverse scattering and a low-rank sparse time-frequency matrix decomposition algorithm based on random matrix theory. The three corrected signals are weighted and fused to obtain a corrected Mel spectra, which are finally input into a temporal convolutional network to classify and identify the safety valve opening sounds and output a thermal runaway early warning signal. This invention solves the technical problem of insufficient accuracy in thermal runaway early warning caused by acoustic feature reconstruction distortion in complex noise environments.

The embodiment of the application relates to an algorithm and a device of a Bayesian classifier of a non-uniform background, which are applied to an underwater active sonar system, the algorithm comprising: obtaining underwater data to be measured and auxiliary data through the active sonar system; the number K of the auxiliary data is greater than 0; modeling classification of the unknown covariance matrix structure into a binary hypothesis testing problem; hypotheses of the binary hypothesis testing problem comprise H i Wherein i=0, 1, H0 is a case that the unknown covariance matrix is a complex conjugate symmetric matrix; H1 is a case that the unknown covariance matrix is a real symmetric matrix; a Bayesian model is set, the Bayesian model comprising a complex inverse Wishart random matrix and a real inverse Wishart random matrix; a classifier for distinguishing the two hypotheses is obtained by using a minimum error probability criterion.

The invention relates to a remote sensing image encryption method based on four-dimensional hyperchaotic mapping, and belongs to the technical field of information security. The method comprises the following steps: constructing a four-dimensional hyperchaotic system based on improved Henon and Quadratic mapping; generating an initial value of a four-dimensional hyper-chaotic system by combining the chi-square test value and the hash value of the remote sensing image to be encrypted, and performing iteration to generate a pseudo-random sequence; the method comprises the following steps: partitioning a remote sensing image to be encrypted, generating a three-dimensional random matrix by using a pseudo-random sequence to perform pixel scrambling on the partitioned image, generating four one-dimensional random matrixes by using the pseudo-random sequence, and obtaining four new S boxes for pixel replacement by combining a dynamic mask interleaving method; and combining the Gaussian function and the sine function to generate a waveform curve, and performing XOR on the waveform curve and the value of the pseudo-random sequence to realize scrambling and diffusion of pixels, and finally forming a ciphertext image. The objective of the invention is to solve the technical problem that security is difficult to guarantee due to relatively single phase space structure and obvious periodic characteristics during image encryption of traditional chaotic mapping.

The present invention proposes a method for reconstructing the 3D topography of a virtual grinding wheel based on matrix convolution operation, which reconstructs the grinding wheel topography by convolution of a digital filter matrix and a random matrix. The actual grinding wheel topography is collected by a 3D optical profilometer and statistically analyzed. Statistical parameters such as mean value, standard deviation, skewness and kurtosis are used to quantitatively analyze the distribution law of the grinding wheel topography height data. The protruding abrasive particles on the grinding wheel surface are extracted, and the shape of the abrasive particles is statistically analyzed. The abrasive particles after statistics are used as a filter function and convolution calculation is performed with a random matrix to realize the reconstruction of the random grinding wheel topography. The actual collected grinding wheel topography is used as a reference for reconstructing the virtual grinding wheel topography. For the grinding wheel topography at different wear stages, the Johnson transformation is used to transform and calculate the grinding wheel topography digital matrix. The present invention solves the problem of poor consistency between the reconstruction of virtual grinding wheel topography and the actual grinding wheel topography, and improves the accuracy of the reconstruction of the grinding wheel topography.

The invention belongs to the technical field of information communication, particularly relates to a directed network environment variance reduction distributed self-adaptive gradient tracking algorithm and system, and aims to solve the following problems in the prior art: 1) an existing distributed self-adaptive random optimization algorithm uses double random matrixes as communication matrixes and cannot be applied to directed network topology; and 2) the existing distributed stochastic optimization algorithm is limited by the property of stochastic gradient in any strong connectivity network, and the optimization precision is influenced by steady-state errors. An efficient variance reduction distributed adaptive gradient tracking algorithm-VRDAGT algorithm is provided, the algorithm combines a gradient tracking technology, an adaptive step length technology and a variance reduction technology, and eliminates steady-state errors introduced by stochastic gradients under a push-pull framework; by using the row random matrix and the column random matrix, the VRDAGT algorithm gets rid of dependence on double random matrixes, so that the applicable network topology range of the algorithm is widened. Compared with an existing algorithm, the VRDAGT algorithm has higher optimization precision and a wider application range.

The invention discloses a photovoltaic scene generation method and system based on random matrix theory enhancement, and the method comprises the steps: constructing a training sample set based on historical data, and constructing a generative adversarial network framework comprising a generator and a discriminator; on the basis of a random matrix theory, time sequence data of a generated photovoltaic power time sequence scene and a real photovoltaic power time sequence scene are constructed into an augmented matrix, spectral distribution of the augmented matrix is calculated, the spectral distribution is converted into a correlation measurement index, and the correlation measurement index serves as an additional loss item to be introduced into a loss function of a discriminator; adopting an alternate training strategy to perform joint training on the generator and the discriminator into which the correlation loss item is introduced, and optimizing a generative adversarial network framework; and inputting the random noise vector to the generator, and generating a photovoltaic power time sequence scene. Spectral distribution analysis is carried out on a generated scene and a real scene through a random matrix theory, a correlation index is used as a discriminator loss item, and the problems of insufficient interpretability, lack of correlation evaluation and the like of the generative adversarial network in photovoltaic scene generation are solved.

The invention provides a method, a device, equipment and a medium for eliminating errors in lattice public key encryption. The method comprises a key generation stage, an encryption stage and an auxiliary encryption stage. The secret key generation stage comprises the steps of generating a random matrix, generating a first secret vector and a first error vector according to uniform distribution sampling, and calculating a target vector according to the first secret vector and the first error vector so as to obtain a public key and a private key; the encryption stage comprises the steps of generating a second secret vector, a second error vector and a secret ring element according to uniform distribution sampling, and generating a ciphertext based on the second secret vector, the second error vector, the secret ring element and a message; the auxiliary encryption stage comprises executing a PSI-CA protocol based on the first secret vector, the first error vector, the second secret vector and the second error vector. According to the method, the uncertainty of error terms in an LPR scheme can be effectively reduced, and then an encryption scheme with smaller design parameters is supported.

Certain aspects of the present disclosure provide techniques and apparatus for efficiently adapting a machine learning model from a base task to a downstream task based on a frozen matrix. An example method generally includes receiving an input for processing through a layer of a neural network. An output of a layer of the neural network is generated based on a first product based on a first trainable scaling vector, a first frozen matrix, a second trainable scaling vector, a second frozen matrix, and the received input.

The application relates to a wireless channel modeling method, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a small-scale channel matrix sample; generating a first distribution matrix; the first distribution matrix is an independent and identically distributed complex normal random matrix; generating a first constant matrix, a second constant matrix and a second distribution matrix according to the small-scale channel matrix sample; the first constant matrix is used for reflecting the energy coupling ability of a line-of-sight component, the second constant matrix is used for reflecting the energy coupling ability of a scattering component, and each element of the second distribution matrix is a power of a mutually independent generalized gamma distribution random variable; and acquiring a target channel matrix according to the first constant matrix, the second constant matrix, the first distribution matrix, the second distribution matrix and the small-scale channel matrix sample. The method can realize equivalence of GBSM and CBSM statistical characteristics which are universally applicable to any application scenario.