42 results about "Singular value decomposition method" patented technology

The invention provides a Prony algorithm-based cable segment wave velocity acquisition method, device and system, and a medium, and the method comprises the steps: testing a to-be-tested cable, and obtaining a cable signal transfer function; on the basis of the cable signal transfer function, in combination with cable joint distribution, constructing a cable signal approximation function based on a Prony method; constructing a linear prediction model of the cable signal based on the cable signal approximation function; performing denoising processing on the linear prediction model based on a singular value decomposition method to obtain a denoised linear prediction model; solving the denoised linear prediction model to obtain an attenuation coefficient; and calculating the segmented wave velocity of the cable based on the attenuation coefficient. According to the method, the Prony estimation method is combined with the singular value decomposition noise reduction algorithm, high-precision extraction of attenuation constants and segmented wave velocity decoupling are achieved, and then the electrical distance positioning precision is improved.

The invention discloses a truss type load-bearing structure time domain dynamics topological optimization method and system based on an intrinsic orthogonal decomposition method, and relates to the technical field of truss type load-bearing structure topological optimization. The method is provided for solving the problems of high calculation power and the like caused by large-scale space-time response iteration of original transient problems and intensive calculation of dual problem sensitivity analysis in the prior art. According to the method, a local geometric control strategy for the cross interference problem of rod piece units is introduced, and a transient dynamics full-order topological optimization model of the truss structure is established through an equal geometric stiffness diffusion method. Based on an intrinsic orthogonal decomposition method, a transient dynamics reduced-order model of a truss structure is constructed, a reduced-order model base is updated in real time through an incremental singular value decomposition method, and then efficient solving of a transient dynamics equation and an adjoint equation in sensitivity analysis is achieved. And updating an iterative design variable by adopting a penalty function method, and seeking the optimal layout design of the truss structure. The invention provides a time-domain dynamics topological optimization method of the truss type load-bearing structure based on an intrinsic orthogonal decomposition method, and the method can be used for efficiently solving the dynamics layout optimization problem of the truss structure under the action of a transient load.

An electric vehicle charging station recommendation method and system, applied to the related field of charging station recommendation. The method comprises: acquiring user charging features and influence degree values of user charging evaluation features on user charging preferences, wherein the user charging evaluation features are constructed on the basis of the user's evaluation information on charging stations (S1); calculating the user's comprehensive scores for different charging stations on the basis of the influence degree values, and constructing a user preference matrix by using the user's comprehensive scores for different charging stations as elements (S2); and decomposing the user preference matrix by means of a singular value decomposition method, acquiring potential matching relationships between the user and charging station features, and recommending a charging station to the user on the basis of the potential matching relationships (S3). The method solves the problem in the prior art that charging stations cannot be personalizedly recommended for different users, thereby improving user experience.

The invention discloses a mechanical arm 3D printing normal calibration method based on singular value decomposition. The mechanical arm 3D printing normal calibration method comprises the steps that S1, a dial indicator is installed at the tail end of a mechanical arm; s2, respectively establishing coordinate systems by taking the center point of the flange at the tail end of the mechanical arm, the actual contact point of the dial indicator and the non-deformation tail end point of the dial indicator as original points; s3, taking the central point of the flange at the tail end of the mechanical arm as a movement track of the point in any set space curve, and sampling to obtain a space point set of sampling points; s4, fitting a plane in the space by using a singular value decomposition method, so that the sum of squares of distances from all sampling points in the space point set to the plane is minimum, and further obtaining a three-dimensional space plane equation and a normal vector of the fitted plane; and S5, coordinate system posture correction is conducted on the mechanical arm tail end flange center point coordinate system, so that the axis of the center of the mechanical arm tail end flange is parallel to the normal vector, and the parallelism between the printing track surface and the forming platform is improved. The forming quality of the first layer of 3D printing can be effectively improved.

The invention belongs to the technical field of health state assessment, and discloses a health state assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the physiological feature data of a user in a preset period, constructing the physiological feature data into tensor data containing an individual dimension, a feature dimension and a time dimension, complementing missing data in the tensor data by using a frequency domain transformation method and a singular value decomposition method to obtain complemented physiological feature data, inputting the complemented physiological feature data into a preset health state evaluation model, and calculating to obtain a health state evaluation result of the user in a preset period; by inputting the complemented physiological feature data obtained by complementing with the frequency domain transformation method and the singular value decomposition method into the preset health state assessment model, the health state assessment result of the user in the preset period is calculated, and the accuracy of health state assessment is improved.

The invention provides a spectral data cube reconstruction method, device and equipment based on compressed physical prior, a storage medium and a program product, and relates to the technical field of spectral reconstruction, and the method comprises the steps: obtaining a two-dimensional measurement image formed after a to-be-observed target is coded and modulated by a spectral imaging chip; obtaining an original transmission spectrum matrix corresponding to each pixel in the spectral imaging chip; performing principal component analysis on the original transmission spectrum matrix in a spectrum dimension, and extracting a principal component feature vector through a singular value decomposition method; projecting the original transmission spectrum matrix into an orthogonal subspace formed by the principal component feature vectors to obtain a compressed physical prior vector; and inputting the two-dimensional measurement image and the compressed physical prior vector into a deep expansion neural network, and outputting a high-dimensional spectral data cube of the to-be-observed target. The reconstruction efficiency of the spectral data cube can be remarkably improved.

The invention provides an optical guiding method for recycling a deep-sea unmanned underwater vehicle, and belongs to the technical field of underwater vehicle recycling. Four blue-green LED guiding light sources are non-uniformly and annularly distributed and installed on the front end face of a recycling cage, the three-dimensional coordinates of the guiding light sources are calibrated in advance, and guiding light source images are collected in real time through a binocular camera; an adaptive exposure control technology is adopted to adjust image quality, statistical information calculation is performed on an identified light source contour, an effective guide light source target is determined through convex constraint configuration judgment and ellipse fitting screening, four guide light sources are constructed as a traveling salesman problem, and a nearest neighbor heuristic algorithm is adopted to complete number matching. A rotation matrix and a translation vector are solved through a singular value decomposition method according to the pre-measured coordinates and the real-time calculated coordinates, and collaborative optimization is carried out at the same time, so that the technical problems of insufficient optical guidance positioning precision and poor real-time performance in the recovery process of the unmanned underwater vehicle in the deep sea environment are solved.

The application belongs to the technical field of three-dimensional point cloud registration, and specifically provides a point cloud registration method based on second-order spatial compatibility measurement, comprising the following steps: extracting corner points of source point cloud and target point cloud as key points by using Harris-3D algorithm; describing and preliminarily matching the extracted key points by using FPFH algorithm to obtain matching pairs; calculating a second-order spatial compatibility matrix between the matching pairs, and removing false matching pairs to obtain correct matching pairs; calculating a coarse registration result of two point clouds by using singular value decomposition method in combination with the correct matching pairs to obtain an initial pose; and calculating a fine registration result of point clouds according to the initial pose of two point clouds in combination with K-dimensional tree accelerated ICP algorithm to finally complete the point cloud registration work. The application effectively suppresses the influence of outliers on registration accuracy, and improves the accuracy and speed of point cloud registration.

The present application relates to aero-engine test bench structure health monitoring and performance evaluation technology, provide a kind of test bench performance attenuation evaluation method, system and equipment based on defect detection, the method comprises: based on at least two types of non-destructive testing method to construct the finite element numerical model of test bench;By singular value decomposition method to finite element numerical model order reduction and solution obtain order reduction model;The predicted structure displacement of test bench is obtained in order reduction model, the actual structure displacement of test bench under test load is collected, and the structure performance attenuation evaluation of test bench is carried out by predicted structure displacement and actual structure displacement.This application method can carry out online structure health identification and performance early warning to test bench by the degradation model constructed, with good accuracy, scalability and engineering practical value, can be widely applied in the state evaluation and fault prediction of aero-engine test bench.

The invention relates to an aero-engine test bed structure health monitoring and performance evaluation technology, and provides a test bed performance attenuation evaluation method, system and equipment based on defect detection, and the method comprises the steps: building a finite element numerical model of a test bed based on at least two types of non-destructive detection methods; performing order reduction and solution on the finite element numerical model through a singular value decomposition method to obtain an order reduction model; and acquiring the predicted structural displacement of the test bench in the reduced-order model, acquiring the actual structural displacement of the test bench under the test load, and evaluating the structural performance attenuation of the test bench through the predicted structural displacement and the actual structural displacement. According to the method, on-line structure health identification and performance early warning can be carried out on the test bed through the constructed degradation model in combination with real-time monitoring data, and the method has good accuracy, expandability and engineering practical value and can be widely applied to state evaluation and fault prediction of the aero-engine test bed.

The invention discloses a multi-scale space target relative pose estimation method and system using parallel double encoders, and belongs to the technical field of space target pose estimation. The invention aims to solve the problems caused by distance change, illumination difference and insufficient multi-scale feature expression of a space target in a dynamic environment. According to the technical key points, a parallel double-encoder architecture is adopted, and local and global features of a space target are extracted; constructing a feature fusion module to fuse features extracted by the CNN and the Transform; multi-scale features are fused through FPN, and angular points are predicted; training and optimizing the network; and solving the pose by using a random consistency perspective n-point method. A plurality of non-coplanar control points are selected, other points are expressed as a linear combination of the control points, a linear equation set is constructed, and a rotation matrix and a translation vector between a target and a camera coordinate system are solved by using a singular value decomposition method. The deep learning network algorithm provides an efficient and accurate solution for space target pose estimation, the algorithm can deeply mine rich information of image data by adopting an encoder-decoder structure, and finally high-precision calculation of the six-degree-of-freedom pose of the target is achieved.

The utility model provides a kind of power station parameter matrix analysis method, belongs to power station fault diagnosis technical field, solve how to design a kind of power station parameter matrix analysis method, solve the numerous power station parameters, data effective information density is low, lead to the problem that fault cause analysis is difficult, and the massive data stream generated occupies a large amount of storage space, by power station parameter data matrixization representation, constructs parameter data matrix, by matrix operation, the rank of matrix is as quantization index, effectively distinguishes the information density of parameter, the correlation between different parameters can be analyzed by the multi-parameter matrix constructed, not limited by parameter number and time sequence length, and by matrix similarity operation, judge the similarity of unit state, provide quick judgment basis for similar fault in later period;Dimensionality reduction is carried out to operation data matrix using matrix singular value decomposition method, retain the part of large singular value proportion, can extract fault feature, realize compressed storage, save storage space.

The application discloses a method for correcting time base jitter of a high-speed sampling oscilloscope, and the method comprises the following steps: constructing a function model of an oscilloscope waveform, processing an equation group of data by using a matrix, introducing a correction vector and a matrix, expressing a constraint matrix and solving; wherein the step of expressing the constraint matrix and solving comprises the following steps: calculating jitter errors generated by the oscilloscope by using an orthogonal distance regression algorithm; the calculation and solving step is to adopt different methods according to the number of solutions of the matrix, when the matrix is a single solution, a singular value decomposition method is adopted; when the matrix is a multiple solution, a gradient descent method is adopted. The technical scheme disclosed by the application corrects the time base jitter problem of the high-speed high-bandwidth sampling oscilloscope, and makes the measurement result more accurate.

The invention discloses a bamboo strip character denoising method based on a fractional order diffusion equation inverse problem POD algorithm, and belongs to the field of image processing and inverse problem calculation, and the method comprises the steps: carrying out the preprocessing of a bamboo strip image, and obtaining a terminal observation image; based on the set diffusion time and the fractional order, a finite difference method is adopted to solve a positive problem of a time fractional order diffusion equation, and a snapshot data set is generated; based on the snapshot data set, a singular value decomposition method is adopted to extract a main mode, and a reduced-order model is established; based on the terminal observation image and the reduced-order model, constructing a regularization least square optimization problem and adopting a gradient iteration algorithm for solving, and performing inversion to obtain a clear image at an initial moment; and processing the clear image at the initial moment by adopting a threshold segmentation method, and outputting a finally recovered high-definition bamboo strip image. The invention provides an efficient and stable treatment means with clear physical significance for digital protection of the fragile cultural relics such as the bamboo strips.

The invention discloses a particle number state prediction method and device, equipment and a medium. The method comprises the steps of performing data prediction on current state information based on a target system model corresponding to a target particle counter and target noise parameter information to determine a basic state prediction value and a basic measurement prediction value corresponding to a target sample; performing covariance calculation on the basic state prediction value based on a preset singular value decomposition method to determine a prediction covariance error, and performing residual calculation on the basic measurement prediction value based on a preset residual rule to determine a measurement prediction error; and performing gain calculation on the prediction covariance error based on a preset adjustment fusion strategy to determine a target gain, and performing state updating on the basic state prediction value based on the target gain and the measurement prediction error to obtain a target state prediction value corresponding to the target sample. According to the technical scheme, the influence caused by multiplicative noise can be effectively reduced, and the accuracy of a particle number state result is improved.

The application provides a method and device for determining the deformation of an open-pit mine dump based on time-series InSAR technology, which aims to effectively fuse continuous multi-period observations, eliminate the interference of surface elevation on SAR interference phase, and improve the determination accuracy of the deformation of the open-pit mine dump. The method comprises the following steps: obtaining SAR satellite images and a digital surface model of a target area in a preset period; constructing a plurality of multi-master image differential interference queues based on the SAR satellite images; calculating differential interference phases based on the plurality of multi-master image differential interference queues and the corresponding digital surface model, and obtaining unwrapped phases after atmospheric filtering processing by combining a singular value decomposition method; and calculating the deformation of the target area based on a sequential adjustment method, wherein after constructing an error estimation matrix containing elevation errors and unwrapped phases, the sequential adjustment method is used to obtain a velocity matrix corresponding to the time-series phase by using the error estimation matrix, and the deformation of the target area is calculated based on the velocity matrix.

The present application relates to the technical field of shale pore and fracture analysis, and particularly relates to a shale in-situ macropore and fracture discrimination method based on nuclear magnetic resonance-mercury injection. The shale sample in an in-situ state is first obtained; then, two-dimensional nuclear magnetic resonance testing is performed on the shale sample, and two-dimensional relaxation spectrum is inverted based on a truncated singular value decomposition method; in the inversion process, the value range of the singular value cutoff value is determined based on the two-dimensional data matrix under different signal-to-noise ratios, and the optimal cutoff value is determined through global optimization; then, high-pressure mercury injection testing is performed on the shale sample, and finally, the macropore and fracture region in the shale sample is determined by comprehensively considering the high-pressure mercury injection testing result and the two-dimensional relaxation spectrum. The present application determines the value range of the singular value cutoff value based on the nuclear magnetic resonance testing result under different signal-to-noise ratios, and determines the optimal cutoff value through global optimization, so as to improve the inversion effect, obtain more reliable two-dimensional relaxation spectrum, and then comprehensively discriminate the macropore and fracture by combining the testing results of nuclear magnetic resonance-mercury injection, thereby improving the discrimination accuracy.

The application provides a spectral data cube reconstruction method and device based on compressed physical prior, equipment, storage medium and program product, relating to the technical field of spectral reconstruction, and the method comprises the following steps: obtaining a two-dimensional measurement image formed by encoding and modulation of a to-be-observed target through a spectral imaging chip; obtaining an original transmission spectrum matrix corresponding to each pixel in the spectral imaging chip; performing principal component analysis on the original transmission spectrum matrix in the spectral dimension, and extracting a principal component feature vector by a singular value decomposition method; projecting the original transmission spectrum matrix into an orthogonal subspace formed by the principal component feature vector to obtain a compressed physical prior vector; inputting the two-dimensional measurement image and the compressed physical prior vector into a deep unfolding neural network to output a high-dimensional spectral data cube of the to-be-observed target. The application can significantly improve the reconstruction efficiency of the spectral data cube.

The application discloses a kind of solving method and device of spatial vehicle pose, and the parameter required for three-dimensional reconstruction is obtained by cooperative robot through vision sensor and calibration plate after the vehicle to be detected reaches specified parking area;Image of the region where the feature points on the surface of the vehicle body are located is collected, and the image is transmitted to host computer for processing;The two-dimensional pixel coordinates of the feature points on the surface of the vehicle body in the plan image are extracted by image processing algorithm;The three-dimensional coordinate information of the feature points on the surface of the vehicle body under the camera coordinate system is restored by three-dimensional reconstruction of feature points;A three-dimensional coordinate transformation model between the camera coordinate system and the vehicle coordinate system is established according to the three-dimensional model of the automobile;The point cloud data of the feature points on the surface of the vehicle body in the camera coordinate system and the vehicle coordinate system is brought in by using the coordinate transformation model based on Rodrigues matrix, and the pose of the vehicle in the indoor space is obtained by singular value decomposition method.The calculation between three-dimensional points is more reliable in the vehicle pose calculation result.

The application discloses an AR model parameter estimation method and device based on forward and backward linear prediction, and the method comprises the following steps: obtaining an interference signal of an infrared spectrometer and converting the interference signal into a matrix form, so as to complete construction of an AR model; a forward and backward expansion matrix of the interference signal is constructed by using a forward and backward prediction total least square method, and a forward and backward linear prediction equation is formed based on the forward and backward expansion matrix; the forward and backward expansion matrix is converted into a homogeneous linear equation by considering the noise disturbance influence of an observation vector and a data matrix, and a total least square problem of the homogeneous linear equation is constructed; and a singular value decomposition method is used to solve the solution of the total least square problem, so as to obtain parameter coefficients of the AR model. The application has the advantages that the problem of spectrum line separation of a restored spectrum is solved, the influence of noise on parameter estimation is reduced, and the accuracy of the restored spectrum is improved.

The invention belongs to the technical field of simulation, and particularly relates to a compartment welding position and compartment safety simulation method and system.The method comprises the following steps that S1, a compartment three-dimensional finite element model is obtained, and an initial damage tensor field representing welding defect anisotropy distribution is established for a weld joint and a heat affected zone; s2, a collision working condition is set, when the plastic strain energy density change rate of a unit in a key bearing area in simulation exceeds a preset threshold value, a non-local damage evolution model is activated for the unit and a unit neighborhood, and initialization is conducted through an initial damage tensor field; according to the method, a space-time evaluation matrix of safety is constructed, and by means of a singular value decomposition method, the contribution degree of each welding position to a dominant failure mode is backtracked and quantitatively evaluated from passenger living space and energy absorption global safety indexes, so that data guidance is provided for welding optimization of a vehicle body structure, and the welding quality of the vehicle body structure is improved. And the vehicle safety performance can be improved.

The application discloses a kind of semi-airborne transient electromagnetic data quality evaluation method and system, comprising: semi-airborne transient electromagnetic data acquisition is carried out to the region to be detected, and cruise trajectory error judgment and pretreatment are carried out;Semi-airborne transient electromagnetic data is carried out empirical mode decomposition, and low-frequency residual component is obtained;Semi-airborne transient electromagnetic data is subtracted low-frequency residual component, and first data are obtained;Noise removal is carried out to first data using singular value decomposition method, and equal logarithm time window is extracted, and multi-channel electromagnetic response data are obtained;According to the coil movement posture data, the multi-channel electromagnetic response data are corrected, and second data are obtained;The region to be detected is repeatedly flown, and third data are obtained;Correlation calculation is carried out to second data and third data, and correlation threshold is preset, and the data corresponding to less than correlation threshold is eliminated;Fourth data and fifth data are evaluated using mean square relative error, and the final transient electromagnetic data are obtained.

The invention discloses a large language model emotion control method and system, and the method comprises the steps: carrying out the decomposition of a hidden state aggregation matrix through employing a centralized singular value decomposition method, obtaining the first k principal components through employing a principal component analysis method, and constructing an emotion subspace basis matrix; mapping the hidden state of the large language model to an emotion subspace by using an emotion subspace basis matrix, and performing emotion migration learning on the emotion subspace by using a multi-layer perceptron module; acquiring an emotion guide data set, and according to the emotion guide data set of the corresponding type, inputting the emotion subspace mapped by the large language model into a multi-layer perceptron module for emotion migration training to obtain an emotion controller; and performing reflection mapping on the trained emotion controller in a corresponding network layer in the large language model to obtain a new hidden state, constructing a joint optimization objective function of semantic maintenance and emotion control loss, and performing optimization learning on the emotion controller based on an output result of the joint optimization objective function and the large language model of the hidden state.

The application relates to the field of intelligent manufacturing, and discloses a high-quality grinding and polishing track planning method for a remanufactured blade robot, which comprises the following steps: determining a track plane for multi-layer grinding and polishing on a remanufactured blade grid surface; determining sequential intersection points between each layer of the track plane and triangular facets of the grid surface; fitting the sequential intersection points of each layer into a target curve path, and discretizing each target curve path according to an equal-arc-length interpolation method to obtain a tool contact point; determining a double-tool-axis vector of each tool contact point; generating a sampling point and determining a material removal function of the sampling point; combining the material removal function and a tool path according to a convolution operation, determining the residence time of each layer of tool contact points in a repair area through a truncated singular value decomposition method, and determining a robot grinding and polishing track according to the spatial coordinate information of a tool position point corresponding to the tool contact point, the double-tool-axis vector and the residence time. The method considers the influence of large deformation of a surface and uneven distribution of a repair area allowance on grinding and polishing effect, and realizes layered and point-by-point precision grinding and polishing processing of a remanufactured blade.

The invention provides a method and a device for determining deformation quantity of a surface mine dumping site based on a time sequence InSAR (Interferometric Synthetic Aperture Radar) technology, and aims to effectively fuse continuous multi-period observation, eliminate interference of surface elevation on an SAR interference phase and improve the determination precision of the deformation quantity of the surface mine dumping site. The method comprises the following steps: acquiring an SAR satellite image and a digital surface model of a target area in a preset period; constructing a plurality of multi-main image differential interference queues based on the SAR satellite image; calculating a differential interference phase based on the plurality of multi-main image differential interference queues and the corresponding digital surface models, and obtaining an unwrapping phase after atmospheric filtering processing by combining a singular value decomposition method; the deformation quantity of a target area is calculated based on a sequential adjustment method, after an error estimation matrix containing an elevation error and an unwrapping phase is constructed by the sequential adjustment method, a speed matrix corresponding to a time sequence phase is obtained by utilizing the error estimation matrix, and the deformation quantity of the target area is calculated based on the speed matrix.

The application discloses a kind of based on the coherent signal DOA estimation method of multi-beam frequency scanning leaky-wave antenna, for solving the coherent signal DOA estimation problem of multi-beam FBS-LWA receiving model.Process as follows:First, based on the signal data matrix of multi-beam FBS-LWA receiving model, according to signal data matrix calculation frequency-space spectrum, select the angle corresponding to frequency-space spectrum peak point to obtain candidate angle set, interpolation is carried out to candidate angle set to obtain the super-complete angle set of pruning, then based on signal data matrix, using singular value decomposition method obtains the signal data matrix of pruning, finally based on the super-complete angle set of pruning and the signal data matrix of pruning, using SBL algorithm obtains the coherent signal DOA estimation result.The method disclosed in the application can successfully estimate the coherent signal DOA of multi-beam FBS-LWA receiving model with higher accuracy and lower running time.

The invention provides a multi-size brick point cloud registration method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining three-dimensional point cloud data corresponding to a to-be-registered target and a reference target, and extracting two corresponding initial corner point sets through geometric structure analysis; for each initial angular point set, sorting is carried out based on azimuth angles of the angular points relative to the center of the initial angular point set, the initial angular point sets are screened through a cyclic shift mode and an overall Euclidean distance difference, and a target angular point set is determined; obtaining rigid rotation matrixes corresponding to the two target angular point sets by using a singular value decomposition method, and determining translation vectors of two point cloud structure centers based on hole features of a point cloud center area extracted from the three-dimensional point cloud data; and synthesizing the rigid rotation matrix and the translation vector into a 4 * 4 rigid body transformation matrix, and realizing registration of the to-be-registered target and the reference target based on the rigid body transformation matrix.

The application discloses a compression and transmission method for a large-scale weight matrix of a neural network, and belongs to the technical field of neural network processors. In order to reduce data transmission cost, the application comprises the following steps: arranging the weight of each layer of a DNN model into a weight matrix; using a singular value decomposition method to process the weight matrix to obtain a left coefficient matrix, a base matrix and a right coefficient matrix of the weight matrix of each layer; using a bubble algorithm to process the base matrix, and using a quantization and approximation compression method to process the left coefficient matrix and the right coefficient matrix; loading the processed left coefficient matrix and the right coefficient matrix of each layer into an off-chip dynamic random access memory, and loading the processed base matrix of the weight matrix into an on-chip cache; using a data recovery array design method to process the loaded data to obtain a reconstructed weight matrix of each layer, and inputting the reconstructed weight matrix into a DNN accelerator for a calculation process of the DNN accelerator. The application greatly reduces memory access cost.

This invention discloses a Pirani data processing method based on singular value decomposition (SVD), belonging to the field of Pirani data processing technology. The method includes placing a Pirani vacuum gauge in an experimental environment for data acquisition and processing. The acquired data is integrated into an m×n dimensional matrix A. Singular value decomposition is performed, and principal components are extracted. Principal components are selected, and matrix truncation and reconstruction are performed to achieve dimensionality reduction reconstruction with a fixed k value. Post-processing is then performed to obtain calibration curves, and pressure interpolation calculations are performed. Multi-temperature point calibration and storage of calibration tables are also included. This invention utilizes a Pirani data processing method based on singular value decomposition. The largest singular value and its corresponding basic waveform, and the second largest singular value and its corresponding waveform, capture the most important correction pattern. The third and subsequent singular values ​​typically correspond to data fluctuations caused by random interference, measurement noise, etc. By forcibly setting them to zero, the algorithm logic becomes simple and stable.

The invention discloses an electric power line hidden danger rapid identification method fusing unmanned aerial vehicle inspection and three-dimensional modeling, relates to the technical field of electric power line safety monitoring, and aims to solve the problems of large clearance calculation deviation and inaccurate hidden danger identification caused by the fact that images are interfered by water surface inverted images in water cross-section electric power line inspection. The method comprises the following steps: firstly, acquiring an image, camera internal reference and unmanned aerial vehicle coarse pose, optimizing pose and rigid body three-dimensional coordinates by taking tower body rigid bodies on both banks as constraints, and constructing a credible coordinate framework; triangularizing a water-land junction area, and fitting a water surface plane by using a singular value decomposition method; constructing a crossing vertical plane by taking rigid bodies on two banks as end points, and constructing a real lead three-dimensional curve through a catenary formula; secondly, modeling an inverted image based on a reflection rule, and comparing pixel residual error stripping inverted images; and finally, adjusting and calibrating a traverse curve by using a bundle method, and calculating a minimum clearance value to identify hidden dangers. According to the method, the pose and clearance calculation precision is improved, the problem of inverted image misjudgment is solved, and the efficient operation and maintenance requirements of the water span section power line are met.