657 results about "Singular value decomposition" patented technology

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention discloses a large language model progressive field fine tuning and knowledge fusion method for shield engineering. The method comprises the following steps: constructing a layered shield training course containing a wide-area academic theory and a proprietary enterprise construction method; parallelly training a plurality of physically isolated parameter efficient adapters based on the frozen base; performing singular value decomposition on the adapter, extracting a geometric feature subspace representing knowledge distribution, and calculating a conflict correlation degree; based on this, a uniform adaptation mechanism of resource awareness is constructed. The mechanism not only can generate a static fusion model for conflict removal, but also can dynamically activate a specific rank slice of an adapter through a routing network based on real-time hardware resource budget (video memory / FLOPs) and geometry-resource signature. According to the method, multi-source knowledge is reserved, and adaptive dynamic scheduling of edge hardware resources by model reasoning is realized.

The invention relates to the technical field of numerical control programming, in particular to a five-axis machining path planning method and system based on data driving, and the method comprises the following steps: obtaining real-time coordinates of each axis of a machine tool, calculating linear velocity and angular velocity components to construct a Jacobian matrix, executing singular value decomposition, and calculating a conditional number ratio by using maximum and minimum singular values; and inputting a nonlinear mapping function to calculate a dynamic penalty factor, generating a rotating shaft weighted item in combination with a rotating shaft identifier, constructing a weighted damping least square objective function, calculating a five-axis motion increment, and accumulating the five-axis motion increment with a real-time coordinate to generate a target absolute position coordinate. According to the method, the pose singularity degree is quantified by monitoring the machine tool pose condition number ratio and converted into the dynamic penalty factor to apply the self-adaptive constraint to the rotating shaft, the severe sudden change of the rotating shaft in the singularity area is inhibited, the tool nose track following error is minimized, and meanwhile smooth distribution of the motion increment is achieved; and the dynamic stability and the surface quality of five-axis linkage machining are improved.

The invention provides a high-resolution emission flux inversion system and method. The method comprises the following steps: collecting atmosphere multi-source data; determining a distance weighting function, and calculating the correlation between the grid points in the simulation area and the observation value; generating a set sample meeting physical constraints of the assimilation object; performing singular value decomposition and dimension reduction on the set samples; replacing a tangent line and an adjoint mode of a regional air quality mode with a mixed assimilation method, and obtaining a simulated regional space grid point analysis increment; obtaining corrected concentration and flux distribution data; the data is used for carrying out emission flux inversion, and the unit time emission flux of different positions is estimated. According to the method, a traditional four-dimensional variation mode is replaced with a mixed assimilation method, and the calculation and programming difficulty is reduced; generating a set sample by using a four-dimensional sliding sampling algorithm, reducing dimensions, and calculating resource consumption; a joint assimilation algorithm is introduced to optimize the concentration and flux field, and accurate inversion of the high-temporal-spatial-resolution emission flux is achieved.

The invention provides a power load prediction method based on a space-time diagram convolutional network in extreme weather. Comprising the following steps: collecting historical load data and regional meteorological element data of a plurality of load nodes in a power system, and screening key meteorological characteristics which have obvious influence on loads through a mode of combining model interpretation and regression analysis to construct a meteorological characteristic vector; multivariable empirical mode decomposition and singular value decomposition are adopted to carry out multi-scale reconstruction on load data, and smooth and effective load feature tensors are extracted. On the basis, a graph network structure is constructed in combination with a node physical connection relationship, and load and meteorological characteristics are fused in a time dimension to form node time sequence characteristics. And predicting the load by using the space-time diagram convolutional network model. According to the method, the space-time dependency relationship of the load data can be effectively modeled, the prediction accuracy of the load change under the extreme weather condition is enhanced, and the method has good robustness and generalization ability.

The invention provides a photovoltaic inverter operation state diagnosis method, which belongs to the technical field of photovoltaic inverters, and comprises the following steps: collecting multi-source operation signals of a photovoltaic inverter, carrying out wavelet packet decomposition on the signals to construct a time-frequency characteristic dense matrix, generating a fault characteristic super-sparse representation vector through singular value decomposition and sparse processing, and carrying out fault characteristic super-sparse representation on the fault characteristic super-sparse representation vector. Performing envelope demodulation on sensitive mode components obtained by complete set empirical mode decomposition to extract approaching periodic feature vectors, and inputting three types of complementary features into a weak fault recognition model with a circulation attention mechanism to perform fusion diagnosis. And whether preventive maintenance early warning is triggered or not is judged according to the output determinant characteristic value of the convergence state matrix, and an operation strategy is adjusted. The technical problem that early weak fault characteristics of the photovoltaic inverter are difficult to be accurately identified and early warned in time in a strong noise background is solved.

The invention discloses a flow field prediction-oriented full-process automatic joint reduced-order modeling method, which comprises the following steps of: specifying a target physical field parameter space, and randomly generating a sample space according to a Latin hypercube sampling method; constructing a full-process automatic simulation tool chain, driving target physical field numerical calculation and generating a training data set; carrying out singular value decomposition-based intrinsic orthogonal decomposition on the output physical field data, and only retaining first r main feature components to construct a reduced-order data set; constructing a multi-input multi-output full-connection feedforward neural network, and modeling and training a nonlinear mapping relation between input parameters and reduced-order features; new working condition parameters are input, reduced-order features are predicted through the trained neural network, distribution of a target physical field is reconstructed according to a singular value decomposition reduction matrix, and more flexible and reliable technical support is provided for reducing the training cost of a reduced-order model and improving simulation efficiency.

The invention discloses a robot hand-eye calibration method, control equipment and a robot system, and relates to the technical field of robots. The robot hand-eye calibration method comprises the following steps: uniformly correcting a plurality of pixel coordinates obtained by dynamic shooting to a reference pixel coordinate system which is consistent with a camera coordinate system at a reference position in direction; and meanwhile, the world coordinates of the end effector corresponding to each sampling position are localized relative to the world coordinates of the end effector at the reference position, so that interference caused by global coordinate offset and camera orientation difference is eliminated, and the application range of the hand-eye calibration method is expanded. Besides, a singular value decomposition algorithm is adopted to solve a mapping relation between pixel coordinates and local world coordinates under a reference pixel coordinate system, so that a solved rotation matrix can meet orthogonality constraints, the hand-eye transformation process strictly conforms to a robot motion model with translation and rotation motion, and the calibration precision is improved. The calibration process can be automatically calculated and completed, and the calibration efficiency is high.

The invention discloses a partial discharge signal denoising method based on STFT-SVD and Bayesian kurtosis threshold adaptive optimization, and the method comprises the steps: collecting an analog signal outputted by a high-frequency current transformer, carrying out the analog-to-digital conversion, obtaining a one-dimensional time domain signal sequence, carrying out the DC component removal and amplitude normalization of the signal, and obtaining a preprocessing time domain signal; performing short-time Fourier transform on the preprocessed time-domain signal to obtain a time-frequency spectrum, suppressing low-amplitude noise by adopting a soft mask method, and retaining main characteristics of partial discharge pulses; performing singular value decomposition on the time-frequency spectrum after soft masking, automatically selecting a principal component number according to a principal component, and adaptively reserving a main signal component to obtain a principal component spectrum; and performing inverse short-time Fourier transform on the principal component atlas to reconstruct a time domain signal, adaptively selecting a kurtosis threshold in combination with a Bayesian optimization algorithm, and outputting a denoised time domain signal.

The invention belongs to the technical field of wireless communication, and discloses a CNN-Transform direction estimation method based on covariance-unitary matrix input, and the method comprises the following steps: generating received signal sample data of different incident angles; calculating a covariance matrix for a received signal sample, performing singular value decomposition, extracting real parts and imaginary parts of the covariance matrix and a unitary matrix, and constructing a four-channel two-dimensional real number tensor as input; extracting local spatial features and coherence structures through CNN; serializing the feature map and adding a position code; a Transform encoder module is input, and global spatial dependence is modeled; carrying out average pooling and full-connection classification on the output to realize direction angle prediction; and training is carried out by adopting cross entropy loss, an AdamW optimizer and a cosine annealing learning rate scheduler. According to the method, local and global features are fused, and the method has high precision, strong robustness and excellent generalization ability in complex environments of low signal-to-noise ratio, multipath interference and the like.

The invention relates to the technical field of quantum computing, and provides a linear complexity quantum state preparation method based on tensor decomposition and a quantum circuit construction system.The high-dimensional tensor is decomposed into a series of low-rank core tensors through continuous singular value decomposition, each core tensor in a core tensor sequence is expanded into a unitary matrix, and the unitary matrix is used as a quantum circuit; the unitary matrix sequence is mapped to quantum lines coupled using adjacent qubits, and the quantum lines are run to prepare a target quantum state. Based on this, the line generated by the method can approximately or accurately prepare a target quantum state only by coupling adjacent quantum bits, and is perfectly adaptive to quantum chips of linear or grid topologies such as superconducting and semiconductor quantum dots and the like.

The invention discloses a model processing method and device, equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, in the method, singular value decomposition is carried out on a network weight matrix of a hybrid expert model, and in an obtained right singular vector matrix, columns with singular values smaller than a threshold value are screened to serve as a null-space basis matrix. In this way, the null-space basis matrix can represent the activation mode direction which contributes little to the activation of the expert network. Furthermore, a sub-network weight matrix is constructed based on each column of the null-space basis matrix and the network weight matrix. Therefore, according to the maximum singular value and the minimum singular value of the sub-network weight matrix, the closeness degree of linear correlation between each column of the network weight matrix and the null-space base matrix can be determined, then the invalid feature sensitivity of each expert network can be determined, and according to the invalid feature sensitivity, the invalid feature sensitivity of each expert network can be determined. And cutting network parameters of the hybrid expert model. In this way, the model parameter quantity can be reduced.

The invention discloses a concrete dam modal parameter automatic identification method, which belongs to the technical field of water conservancy project structure health monitoring, and comprises the following steps: obtaining monitoring data of a target concrete dam under environmental excitation, constructing and training a singular value decomposition neural network, decomposing a response matrix formed by the monitoring data, and calculating the modal parameter of the target concrete dam; the modal order of the system is automatically determined through singular values obtained through decomposition; constructing and training a blind source separation neural network according to the determined modal order, separating the multi-channel monitoring data into independent single-degree-of-freedom modal response signals, and extracting a modal shape matrix from network weights; and constructing a parameterized vibration equation neural network for the separated modal response of each order, and identifying the inherent frequency and the damping ratio of the modal of the order by taking a single-degree-of-freedom system vibration differential equation as a physical constraint training network. The technical problems that in the prior art, the modal recognition process is low in automation degree, depends on artificial experience and is not high in precision are solved.

The invention discloses a lanyard data line interaction control method based on artificial intelligence, and the method comprises the following steps: S1, collecting interaction data of a lanyard data line, and carrying out the preprocessing of the interaction data; s2, segmenting according to a set window, performing singular value decomposition on each segment, and constructing a main feature set; s3, performing hypersphere embedding on the principal component vector, performing linear projection on the residual vector, and generating a characteristic spectrum by adopting Laplacian mapping; s4, performing tensor decomposition on the characteristic spectrum, and constructing a multi-order structure; s5, executing bidirectional loop iteration on the tensor interaction sequence and controlling information transmission; s6, analyzing the prediction action, matching the prediction action with an instruction mapping table, generating a control instruction, and sending the control instruction to the terminal equipment; and S7, counting execution feedback, updating a tensor interaction sequence weight, and optimizing an interaction control strategy. According to the invention, high-precision identification and stable adaptive control of the lanyard data line are realized, and the interaction precision, the response speed and the use convenience are effectively improved.

The invention provides a TOF (Time of Flight) estimation method of a matrix bundle based on partitioning and random SVD (Singular Value Decomposition). The method comprises the following steps: firstly, acquiring channel state information (CSI) by using a commercial WiFi device, and constructing a CSI matrix; and secondly, performing block overlapping processing on the CSI matrix, constructing an augmented matrix for each CSI data block, and performing SVD decomposition on the augmented matrix, thereby reducing the calculation complexity of SVD decomposition. And then, the time of flight (TOF) of each CSI data block is estimated by using a matrix pencil algorithm, and the minimum TOF is direct path estimation. According to the method designed by the invention, the robustness of the system and the estimation precision of the TOF are improved under the condition that the algorithm complexity is greatly reduced.

The invention relates to the field of array signal processing, and particularly discloses a DOA joint estimation method based on a quaternion polarization sensitive array. Electromagnetic wave dual polarization components are captured through the orthogonal dipole and the loop antenna, and a quaternion observation matrix is constructed; calculating a quaternion covariance matrix by adopting a sliding window mechanism, and separating a signal / noise subspace by adopting quaternion singular value decomposition; and constructing a spatial spectrum function in combination with a quaternion steering vector, and realizing joint estimation of an azimuth angle and a pitch angle through two-dimensional search. According to the method, the unified characterization capability of quaternions on polarization-airspace information is fully utilized, and the DOA estimation precision and the anti-interference performance of the multi-polarization signal are remarkably improved.

The invention discloses a multi-view clustering method based on tensor feature extraction, and the method comprises the steps: inputting a multi-view data matrix, constructing a similarity matrix of each view through a K-NN algorithm and a Gaussian kernel function, and carrying out the spectral clustering to obtain a sample embedding matrix; performing singular value decomposition on an original data matrix of each view, taking first c left singular vectors to construct a feature embedding matrix, applying 2, 1 norm group sparse constraint on the feature embedding matrix, and connecting a sample embedding matrix through a bigraph to extract features; and normalizing the sample embedded matrix, and reconstructing a block diagonal matrix into a third-order tensor. And integrating the sample embedding matrix, the feature embedding matrix and global tensor learning to construct a target function, and optimizing through an alternating direction multiplier method until convergence. And finally, the normalized samples are embedded into the matrix to form block diagonals to form a consistent similarity graph, and a clustering result is obtained by using an N-Cut or k-means algorithm.

The invention discloses a singular value decomposition-based multi-modal task fine tuning method for enhancing a routing function, which comprises the following steps of: mapping input language and visual features from a high-dimensional space to a low-rank space by using a PEFT method, performing singular value decomposition on language features in the low-rank space, performing routing function alignment through a tensor after efficient reconstruction, and performing multi-modal task fine tuning on a multi-modal task based on a singular value decomposition-enhanced routing function. And finally, after the low-rank space is recovered to the original dimension again, performing residual connection with the original language features, and outputting the features. According to the method, singular value decomposition is applied to language features before a routing function, a low-rank dominant mode of the language features is extracted, the alignment precision of vision and language features is enhanced, interference of high-dimensional noise is eliminated, and meanwhile calculation efficiency and model stability are kept. Routing calculation is carried out through the reconstructed tensor, key information in the features can be better extracted and aligned, and therefore the precision and effect of feature alignment are improved. The method is suitable for VL tasks such as visual questioning and answering and image description generation, and model performance can be obviously improved.

The invention discloses a Non-IID federated learning backdoor attack defense method and system and a medium, and relates to the field of federated learning and network security. In order to solve the problems that an existing method depends on model parameters and is easy to avoid by a malicious client, and adaptive Non-IID scenes are poor, truncated singular value decomposition is executed through the client to extract first p left singular vectors, and the first p left singular vectors are uploaded to a server; the server constructs a matrix based on left singular vector cosine similarity and performs hierarchical clustering, and calculates a client similarity score and an aggregation weight by using a zoom dot product attention mechanism in combination with a left singular vector of the clean reference data set; the server distributes a global model, the client uploads parameters after local training, and the server weights and aggregates the model parameters in the cluster according to the weight and iteratively optimizes the model parameters. According to the method, the malicious client is identified from the data essential features, the malicious proportion does not need to be preset, the good client contribution and the data privacy are guaranteed while the backdoor attack is inhibited, the method is suitable for a Non-IID scene, and the model robustness and the main task performance are improved.

The invention discloses an automatic history fitting method based on multiple data assimilation of an improved set smoother, and relates to the technical field of petroleum engineering. The method comprises the following steps: firstly, constructing a plurality of oil reservoir models, setting a prior model, a real model and an initial expansion factor, obtaining the yield of each oil reservoir model by utilizing oil reservoir model numerical simulation, and calculating a residual error; based on corrected data covariance matrix singular value decomposition, production observation data disturbance enhancement, adaptive learning rate matrix scaling and geological boundary constraint, improving a set smoother, updating the permeability of each oil reservoir model, performing numerical simulation again, updating an expansion factor, judging whether the expansion factor meets a preset condition or not, continuing iteration if the expansion factor meets the preset condition, and if the expansion factor does not meet the preset condition, continuing iteration until the expansion factor meets the preset condition; and if not, updating the expansion factor of the iteration, ending the iteration, and outputting the permeability field of each updated oil reservoir model, thereby solving the problems of parameter overshoot, covariance statistical deviation and low calculation efficiency in oil reservoir history fitting, and facilitating history fitting of complex oil reservoir production parameters.

The invention relates to a metal additive manufacturing three-dimensional temperature field Gaussian process prediction method, belongs to the technical field of material science, and particularly relates to a metal additive manufacturing three-dimensional temperature field prediction method. Logarithmic transformation is adopted to preprocess a temperature field, and prediction difficulty caused by extreme gradient near a molten pool is avoided; dividing the overall computational domain into a plurality of sub-domains by adopting a domain decomposition strategy to reduce the problem dimension; for each sub-domain, further combining singular value decomposition to extract a temperature field reduced-order base; establishing a local Gaussian process regression model based on a Maren kernel function and carrying out parallel training so as to establish rapid mapping from process parameters to reduced-order output; during online prediction, efficient and accurate prediction of a complete temperature field is realized through parallel calculation and full-field assembly of each local model.

The invention discloses a medical knowledge graph construction method and device for multi-source heterogeneous data fusion and incremental updating, and relates to the technical field of medical knowledge graphs. The method comprises the steps that multi-source medical data such as outpatient records, hospitalization medical records, inspection reports and image data of a patient in the whole life cycle are collected; performing time sequence alignment by adopting a space-time alignment algorithm to obtain standardized medical data, calculating a data fusion weight and constructing an incidence matrix; performing singular value decomposition on the incidence matrix to extract a feature vector, and obtaining a dynamic update coefficient through standardization, cosine similarity calculation, convolutional neural network processing and exponential weighted moving average; adjusting the reference map according to the dynamic updating coefficient increment to generate an updated medical knowledge map, and outputting diagnosis and treatment decision support information; and multi-dimensional medical data space monitoring abnormity can be constructed, a joint optimization model is established to generate a personalized diagnosis and treatment scheme, and edge deployment is realized through a knowledge distillation compression map.

The invention relates to the technical field of electrostatic dust collection and discloses an accurate frequency adjustment method for an intelligent frequency modulation high-voltage power supply of an electrostatic dust collector. The method comprises the steps of collecting voltage signals with frequency fluctuation in operation of a high-voltage power supply to form a voltage sequence; dividing period segments by using wave crest distribution in the voltage sequence, and clustering to obtain adjustment subsequences; obtaining a trend fluctuation index based on local signal change trend fluctuation and signal differences of the adjustment subsequences, and obtaining an amplitude drop difference and an amplitude interval difference according to an amplitude change trend difference and an amplitude bit sequence change trend difference; obtaining a period stability coefficient by combining the difference, the average condition of the trend fluctuation index and the average condition of the sub-sequence similarity; constructing a Hankel matrix and performing singular value decomposition, and obtaining a main component proportion and a difference index based on a singular value condition; and according to the Hankel matrix line data correlation, the period stability coefficient, the main component proportion and the difference index, obtaining a signal stability index, and according to the signal stability index, accurately adjusting the high-voltage power supply frequency.

The invention discloses a power distribution system harmonic source positioning method based on subsystem division, and aims to improve the accuracy and anti-interference capability of harmonic source identification in a power distribution system. According to the method, firstly, an obtained voltage signal is preprocessed, frequency domain feature information is extracted, a subspace projection operator is constructed through singular value decomposition, and effective extraction of each harmonic component in the signal is achieved. Then, carrying out subsystem division on the power distribution system according to the position of the tail end measurement point, for each subsystem, respectively injecting simulated harmonic current into each candidate node by adopting a virtual harmonic injection method, and calculating the response of the node to the tail end voltage; and comparing a simulation result with a measured value, constructing an error vector, screening out a node with the minimum error as a backup harmonic source node, and finally positioning the harmonic source node through zero setting of a connection node and a correction strategy of the most downstream node. The method is suitable for a multi-node and multi-source coexistence large-scale power distribution network harmonic suppression scene.

The invention belongs to the technical field of partial discharge detection, particularly relates to a partial discharge signal denoising method based on SVDTQWT, and aims to effectively remove periodic narrow-band interference and white noise in partial discharge signals. Comprising the following steps: performing Fourier transform on a noisy partial discharge signal to obtain a frequency spectrum, determining the number of periodic narrowband interferences through singular value decomposition, constructing a Hankel matrix to eliminate the periodic narrowband interferences, and obtaining a preliminary de-noised signal; and decomposing the preliminarily denoised signal by adopting adjustable quality factor wavelet transform to obtain a plurality of sub-bands. And dividing the plurality of sub-bands into high-frequency sub-bands and low-frequency sub-bands through sample entropy. Wherein the sample entropy indicates measurement of the complexity of the time series. And de-noising the high-frequency sub-band by using a group sparse total variation de-noising algorithm, de-noising the low-frequency sub-band by using an improved wavelet threshold de-noising algorithm, and reconstructing by using adjustable quality factor wavelet transform to obtain a pure partial discharge signal.

The invention discloses a GNSS (Global Navigation Satellite System) signal sparse compression capture method for a low-correlation measurement matrix, which comprises the following steps of: firstly, constructing a code phase matrix and a frequency offset matrix, and carrying out sparse transformation on a downsampled GNSS signal to obtain a correlation matrix; the method comprises the following steps of: firstly, carrying out iterative optimization on a measurement matrix by taking a Gramb matrix as a matrix, then taking a Frobenius norm of a difference between the Gramb matrix and an equiangular tight frame matrix as a target function, alternately using singular value decomposition (SVD) and a shrinkage operator to carry out iterative optimization on the measurement matrix, and carrying out compression measurement on a correlation matrix through the optimized measurement matrix. Secondly, a correlation matrix of compression measurement is quickly and accurately reconstructed by using a least square operator and an orthogonal matching pursuit algorithm; and finally, when the peak value ratio of the primary peak to the secondary peak of the reconstructed correlation matrix is greater than a threshold value, outputting code phase time delay and Doppler frequency offset corresponding to the primary peak. And if the ratio of the primary peak value to the secondary peak value of the reconstructed correlation matrix is smaller than the threshold value, capturing the GNSS signal again. According to the method, the maximum correlation coefficient and the average correlation coefficient of the measurement matrix can be reduced, the GNSS signal capturing sensitivity is improved, and hardware resources in the signal capturing process in a GNSS receiver can be reduced.

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.

The invention provides a quantization method and a quantization device, which can perform singular value decomposition on a first matrix formed by word embedding vectors to obtain the maximum characteristic value of each block matrix in M block matrixes, and can determine the quantity of quantization bits of each block matrix according to the maximum characteristic value of each block matrix. The method comprises the following steps of: dividing each block matrix into a plurality of block matrixes, quantifying each block matrix to obtain each quantized block matrix, combining each quantized block matrix to obtain a quantized first matrix, and representing the amount of semantic information carried in each block matrix by a maximum characteristic value of each block matrix to a certain extent, according to the embodiment of the invention, the quantized bit number can be allocated to each block matrix according to the amount of semantic information carried by each block matrix, so that different bit numbers can be allocated to the block matrixes carrying different semantic information, differential bit number allocation can be realized, semantic information loss can be reduced, and user experience can be improved.

The invention provides a performance optimization method and system for a closed cooling tower, and relates to the technical field of data processing.The method comprises the steps that input parameters describing the closed cooling tower are obtained; determining a cumulative distribution function of each input parameter; discretizing each input parameter to form a discretized simulation sample; performing computational fluid mechanics simulation on the closed cooling tower by utilizing the simulation samples, and determining system response of each simulation sample; performing principal component analysis on system response data through singular value decomposition; establishing an incidence relation between the input variable and the score matrix through Kriging interpolation, and forming a complete preliminary prediction model between the input variable and the system response; through a high-dimensional model representation technology, the high-order interaction effect of the preliminary prediction model is optimized, and a performance prediction model of the closed cooling tower is obtained; in order to improve the cooling efficiency and reduce the construction cost, the design parameters of the closed cooling tower are optimized through a butterfly optimization algorithm.

The invention discloses a cross-sensor long-time-sequence InSAR deformation monitoring method and device, a medium and a product, and relates to the field of synthetic aperture radar interferometry, the method comprises the following steps: obtaining image data sets of a plurality of SAR sensors in a monitoring area, and sequentially carrying out differential interference and time sequence deformation calculation to obtain an LOS direction deformation result; geocoding all results to unify a coordinate system; screening high-coherence point targets and constructing an observation geometry unified analysis model; time information is obtained by combining the images, and a long-time-sequence earth surface deformation resolving function model after observation geometric correction is established; and further introducing a truncated singular value decomposition operator, constructing a cross-sensor long-time-sequence deformation resolving analysis model, and finally resolving to obtain a high-precision and long-time-span earth surface deformation product in the monitoring area. According to the method, the problems of rank deficiency and solution failure caused by time shortage and observation geometric difference in multi-platform InSAR data joint solution are effectively solved, and the precision and applicability of long-time-sequence deformation monitoring are remarkably improved.