37 results about "Tikhonov regularization" patented technology

The invention discloses an electrolytic tank performance analysis method based on polarization feature recognition. The method comprises the steps that S1, electrolytic tank parameters are set, electrochemical testing is conducted, and EIS impedance data are obtained; s2, preprocessing the EIS impedance data: performing Kramers-Kronig verification on the EIS impedance data, calculating a relative residual error of the data, and selecting a regularization parameter lambda; s3, applying the EIS impedance data to DRT quantitative analysis: converting the EIS impedance data into a DRT spectrogram based on discretization of a radial basis function and a Tikhonov regularization method; and S4, performing DRT spectrogram identification by using a DRT method, establishing an ECM model, and evaluating the influence of metal cation pollution on PEMWE performance degradation by using the ECM model. According to the electrolytic cell performance analysis method based on polarization feature recognition, the core problem that all polarization processes and the evolution rule of the polarization processes in performance degradation are difficult to accurately analyze under the condition of high current density in the prior art is solved.

The invention belongs to the technical field of Internet of Things. The invention provides a large-scale infrastructure network link dynamic prediction method based on nonlinear reserve pool calculation. According to the embodiment of the invention, by combining nonlinear reserve pool calculation and regularization regression technologies, the dynamic, efficient and accurate prediction of a complex network link is realized, and the method is particularly suitable for state inference and stability analysis of a key link in an infrastructure network. A reserve pool calculation framework is adopted, and the core advantage of the method is that only the linear weight of an output layer needs to be trained, and the internal connection of a huge reserve pool is kept random and fixed. A large amount of iterative computation and GPU resources required by back propagation are avoided, and the training complexity and the time cost are remarkably reduced. A Tikhonov regularization item is introduced, so that the overfitting problem under limited training data or noise interference is effectively prevented, and the generalization ability and prediction stability of the model are improved.

The invention discloses a dust concentration monitoring method and system based on scattered light measurement, and relates to the field of dust concentration monitoring. The method comprises the following steps: S1, in a range point detection stage, turning off an emitter to implement zero point detection to obtain a zero point value, and generating a range point detection result; s2, collecting an optical signal and a temperature signal, obtaining basic scattering intensity based on demodulation and zero value deduction, and generating an analog output current; s3, on-line correction is carried out on the basic scattering intensity through the correction coefficient and the temperature correction coefficient which are calculated in real time, and the optical scattering intensity is obtained; and S4, calibrating by simulating output current and weighing method concentration, solving a current coefficient vector and converting the current coefficient vector into a regression coefficient, and generating dust concentration by directly using optical scattering intensity and the regression coefficient when equipment runs online. Through real-time correction, intelligent dark baseline compensation, temperature correction and Tikhonov regularization methods, the measurement accuracy and the equipment stability and reliability are improved, and high-precision real-time monitoring is ensured.

The application discloses a kind of light field regulation detection method and device based on computing spectral reconstruction, belong to spectral analysis technical field.The method is by introducing medium-metal composite nanostructure, integrates metal nanoisland array and silicon nitride nanocolumn array on quartz substrate, realizes electric field enhancement using the local surface plasmon resonance effect excited by metal nanoisland array, simultaneously realizes wide spectral range phase coding by medium nanocolumn array;Super surface chip is integrated with single point detector, combines pre-calibrated super surface sensing matrix A and enhancement factor matrix E, and reconstructs spectrum using compressed sensing or Tikhonov regularization algorithm.The application keeps the advantages of miniaturization and no moving parts, realizes directional high sensitivity detection to weak signal, and signal-to-noise ratio is improved.

The invention discloses a method and a device for measuring the motion idle stroke and the overall flexibility of a flapping-wing aircraft mechanism, and relates to the field of miniature aircrafts, and the method comprises the steps: loading an aircraft wing rod on a high-speed camera measurement platform, and recording an image of the wing rod in a motion period; after the recorded image is optimized and denoised, an optical flow method is used for capturing a moving wing rod, a sub-pixel edge detection method is used for recognizing a gray level image of wing rod movement, then the edge is optimized and corrected based on the geometrical characteristics of the wing rod, and edge calibration is carried out on the gray level image of wing rod movement; then, the angular displacement of the wing rod is recognized according to the wing rod motion gray level image after coordinate calibration is completed, meanwhile, the distribution load of the wing rod is reconstructed, and inversion is conducted on the bending moment of the wing rod through recognized coordinate information and a Tikhonov regularization method; and finally, drawing an angular displacement-bending moment curve graph of the wing rod and extracting the idle stroke and the overall flexibility of the aircraft. According to the method, the motion idle stroke and the overall flexibility of the mechanism on the flapping-wing air vehicle are effectively extracted with high precision, and the method has great guiding significance in design, test and function research of the flapping-wing air vehicle.

The invention discloses an intelligent electric energy meter error data processing method based on edge calculation, and belongs to the technical field of error data processing methods. The method comprises the following steps: S1, calculating a mean value and a standard deviation in a total electric energy increment sequence and a sliding window, defining a first-order autoregressive lag correction item, setting an adaptive threshold, defining a state machine system state, and finding out a computer increment sequence of a user in an inactive period by constructing an inactive period criterion; s2, storing the screened effective data increment, calculating the electric energy increment of each sub-meter, defining a continuous zero increment detection function, and constructing an observation matrix X and an observation vector Y by setting an ammeter failure detection window and parameters and collecting effective rearranged data points; s3, calculating a characteristic value and a condition number through an improved genetic optimization algorithm framework, and carrying out iteration to obtain optimal and different individuals; and S4, determining an optimal regularization parameter through an L curve method, and calculating an error coefficient of each electric energy meter through improved Tikhonov regularization.

The invention discloses a cable net structure prestress correction method and system based on multi-experiment sensitivity, and relates to the technical field of structural engineering and structural health monitoring. A sensitivity matrix is approximately constructed through a finite difference method, and construction of the sensitivity matrix is completed in combination with multi-working-condition displacement data; meanwhile, a parameter-aware Tikhonov regularization method is introduced to suppress the ill-conditioned error amplification problem, and self-adaptive step length optimization is realized by adopting left-one-out cross validation (LOOCV), so that the convergence efficiency of prestress correction is effectively improved. The method is not only suitable for a pre-stress identification scene of a cable net antenna, but also can be extensively applied to multiple fields such as large-span space structures, bridge engineering and civil engineering health monitoring, and provides effective technical support for parameter identification and finite element model correction requirements related to complex coupling structures in the fields; and the method has very wide application range and practical value.

The invention relates to the technical field of distortion correction, and discloses a laser imaging wavefront distortion correction system and method based on deep learning, and the system comprises a first module which collects three intensity images; the second module is used for estimating the Zernike coefficient and the amplitude of the target complex amplitude; the third module is used for constructing a loss function; a fourth module that calculates an actuator voltage; the fifth module is used for calculating the Stell ratio and the high-frequency lifting ratio in the modulation transfer function; and the sixth module is used for re-applying the voltage of the actuator when judging that the evaluation condition is not met. According to the method, a full-process correction mechanism is constructed by integrating deep learning and an optical physical rule, and initial correction is realized by stably solving the voltage of the actuator by utilizing Tagunov regularization, so that the high-frequency detail definition in imaging is improved, the correction accuracy, real-time performance and robustness are both considered, and the method is suitable for large-scale popularization and application. And the application suitability of the laser imaging technology in the high-precision field is greatly expanded.

The invention discloses a seamless steel tube intelligent production method based on digital twinning, which comprises the following steps: collecting historical data and real-time monitoring data, and preprocessing to generate a standardized data set; establishing a high-dimensional digital twinning parent model, and reducing the order to obtain a digital twinning reduced-order model; performing parameter calibration on the model to obtain an initial parameter set and a state mapping matrix; inputting real-time monitoring data into the model to obtain updated key parameters, state variables and predictive control quantities; a Tikhonov regularization module is introduced, and a robust correction result is generated; the input model prediction control module generates a smooth control instruction; and collecting feedback data, and inputting the feedback data into the model for online correction and self-learning. By introducing the digital twin reduced-order model and the Tikhonov regularization module, real-time and robust control and high-precision process optimization in the seamless steel tube production process are realized.

The present application relates to the technical field of power system metering, in particular to a method and system for operation error diagnosis and analysis of electric energy meter based on electric energy meter operation error, wherein the method comprises: obtaining electric energy meter measurement value curve data of a target area in a set period from a metering automation system, and generating standardized electric energy meter operation data sequence through preprocessing; using an improved spectral clustering algorithm to carry out curve shape clustering on the data sequence, combining elbow rule to determine the optimal classification number of electric energy meter operation state, and completing the classification of electric energy meters in different operation states; relying on the principle of energy conservation to build a state evaluation model, screening effective data points through a data optimization strategy, reducing the model ill-conditioning by means of an improved Tikhonov regularization method, solving the error estimation equation to obtain the electric energy meter operation error estimation value and complete the state diagnosis. This method is suitable for processing massive electric energy meter time series data, smoothly weakening model operation interference, regularizing the electric energy meter operation error solving process, and optimizing the working condition diagnosis adaptability.

The invention discloses a section temperature and non-gray absorption coefficient reconstruction method based on multi-camera spectral imaging. The method comprises the following steps: measuring flame monochromatic radiation intensity under multiple wavelengths by using multiple multi-spectral cameras; setting an initial absorption coefficient, and calculating a DRESOR coefficient matrix under each wavelength through a DRESOR method; the DRESOR coefficient matrix and a modified Tikhonov regularization method are combined to solve medium radiation source item distribution, an absorption coefficient is updated through a Newton iteration method, and medium radiation source item distribution under multiple wavelengths is output; simultaneously reconstructing temperature and absorption coefficient distribution from a radiation source item by using an L-M polynomial fitting method; and if the relative error minimization condition is met or the maximum number of iterations is reached, outputting a final flame section temperature and non-ash absorption coefficient distribution result. By means of the scheme, high-precision flame section temperature and absorption coefficient distribution can be obtained based on multispectral image information, the result is reliable, and precision is high.

The application belongs to the technical field of image processing, and discloses an image processing method and device based on an event camera, equipment and a medium. The method comprises the following steps: acquiring event data of a blurred image collected by an event camera; constructing an initial PINN model, embedding an event generation equation in the initial PINN model; inputting the event data into the PINN model to obtain a predicted luminance value, performing self-supervision of a time derivative based on the predicted luminance value and an actual luminance value, introducing a Tikhonov regularization constraint condition to optimize the PINN model, and determining an optimized PINN model; inputting event data of an image to be processed into the optimized PINN model to obtain log luminance values of three color channels of the image to be processed; and converting the log luminance values into a reconstructed image frame by using tone mapping. The method can reduce noise and artifacts of the reconstructed image and improve the quality of the model reconstructed image.

The application discloses a time delay optimization method for a nonlinear time delay control system of an aircraft, and the method comprises the following steps: constructing a dynamic equation of a general nonlinear time delay system of the aircraft, introducing a Fourier transform to fit a time delay term, and obtaining a time delay term with a truncated series of N; performing two-angle sum transformation on the time delay term with the truncated series of N and solving by using a time domain minimum residual method to obtain a semi-analytical form solution; introducing Tikhonov regularization and a confidence domain limit, performing time delay iterative adjustment on an objective function of a minimum value optimization problem, and outputting an optimal time delay. The application can control a gap / time delay wing flutter system to a steady state in the shortest time, improve instantaneous stability of the aircraft, and reduce loss of the aircraft. The application can be widely applied to the field of aircraft control technology as a time delay optimization method for a nonlinear time delay control system of an aircraft.

The invention discloses a land water reserve inversion method and device, a medium and equipment, and relates to the technical field of hydrogeodetics. The method determines the average distance of the observation stations according to the ratio of the area of the target area to the number of the global navigation satellite system observation stations, and determines the average distance of the observation stations for each grid point according to the spatial distance from each global navigation satellite system observation station to the grid point and the ratio of the average distance of the observation stations to the average distance of the observation stations in the target range around the grid. The method comprises the following steps of: determining a weighting coefficient at a grid point to weight a Laplacian operator, combining the weighted Laplacian operator and a GNSS observation equation to construct a weighted Tikhonov regularization inversion model, and inverting the land water reserve change by minimizing a target function, thereby effectively improving the precision, reliability and space balance of an inversion result, and greatly improving the accuracy, reliability and space balance of the inversion result. Especially, effective constraints can still be obtained in areas with sparse or non-uniform observation stations, the land water reserve inversion deviation is reduced, and the inversion precision is improved.

The invention discloses an EIT image reconstruction quality evaluation-based dike termite nest identification method, and relates to the technical field of dike termite nest identification, and the method comprises the following steps: multi-channel EIT data collection, ant nest multi-physical field positive problem solving, image reconstruction based on a Tikhonov regularization algorithm, image reconstruction quality function calculation and image reconstruction quality evaluation. By designing a multi-channel EIT data acquisition scheme, full-coverage acquisition of electrical signals of the whole dike is realized, weak conductivity abnormal signals caused by ant caves are effectively reserved, comprehensive and pure original data support is provided for subsequent positive problem solving and image reconstruction, and the method has the advantages of being high in accuracy and high in reliability. The method ensures that signals of deep or tiny ant caves are not covered by noise, and solves the problems of limited data acquisition range, large noise interference and difficulty in capturing weak electrical characteristics of the ant caves in the dike in the existing detection technology.

This invention discloses a method for processing error data of smart energy meters based on edge computing, belonging to the technical field of error data processing methods. The invention includes: S1, calculating the mean and standard deviation of the total energy increment sequence within a sliding window, defining a first-order autoregressive hysteresis correction term, setting an adaptive threshold, defining the state machine system state, and finding the computer increment sequence during inactive periods by constructing an inactive period criterion; S2, storing the filtered effective data increments, calculating the energy increment of each sub-meter, defining a continuous zero increment detection function, collecting effective rearranged data points by setting the meter failure detection window and parameters, and constructing an observation matrix X and an observation vector Y; S3, using an improved genetic optimization algorithm framework, calculating eigenvalues ​​and condition numbers, and iteratively obtaining optimal and distinct individuals; S4, determining the optimal regularization parameters using the L-curve method, and calculating the error coefficients of each energy meter using improved Tikhonov regularization.

The invention discloses a radio frequency current non-contact detection method based on space double magnetic field components. The method comprises the following steps: measuring magnetic field distribution on the surface of a to-be-detected device by using a space double magnetic field component probe; expanding the magnetic field plane under the condition of judging that the boundary of the measurement plane cuts off the magnetic field distribution; establishing a relationship between current and magnetic field distribution in a frequency domain space, solving Green functions under a static current hypothesis and a radio frequency current hypothesis, establishing a Tikhonov regularization cost function, and respectively obtaining current solutions under the static current hypothesis and the radio frequency current hypothesis by minimizing the cost function; taking the part exceeding the mean value in the current solution under the static current hypothesis as a static current distribution area, taking the static current distribution area as a mask to act on the current solution under the radio frequency current hypothesis, and filtering current artifacts; and extracting the current distribution in the magnetic field plane before expansion as the final current distribution of the radio frequency current. According to the invention, non-contact accurate imaging of radio frequency current can be realized in a wide frequency band.

The invention discloses a spectrum situation map enhancement construction method based on differential Tikhonov fusion, and the method comprises the steps: 1, collecting the spectrum data of each air sensing node at different moments and different spatial positions in a target region through an unmanned plane, taking the acquired multi-source spectrum data as an original data source for constructing a spectrum situation map; step 2, performing data registration and space alignment: performing time synchronization and space registration on the multi-source spectrum data, unifying to the same coordinate system and time reference frame, and forming a regular spectrum data grid; 3, establishing a spectrum data interpolation model, and forming a preliminary spectrum situation map; 4, constructing a differential Tikhonov regularization model: substituting high-precision spectrum situation map data into the enhanced spectrum situation map obtained in the step 4 to obtain a final enhanced spectrum situation map; and step 6, outputting a spectrum situation map.

The application discloses a kind of magnetic field inversion current methods based on near-field scanning, including steps: obtaining magnetic field data in circuit, and as input;The magnetic field is extended measurement plane, and the magnetic field matrix is obtained, so that the original magnetic field matrix H is continuous on the current of boundary;The relationship between magnetic field and current in frequency domain space is constructed;Using Tikhonov regularization method to reconstruct current distribution, and using GCV method to optimize the regularization parameter required by Tikhonov regularization method;Extract the structural information of static current model, use it as a mask, and apply it to the result of radio frequency current model;Output the current in the original measurement window as the current reconstructed by the final method.The application eliminates the current artifact on the boundary by extending the measurement plane, automatically solves the current distribution by regularization, avoids the error caused by empirical selection of filter parameters, improves the accuracy of reconstructed current, and finally suppresses spatial noise by mask method to realize accurate reconstruction of radio frequency current.

The present application relates to the technical field of lithium ion battery, and discloses a method for optimizing compaction density of graphite negative electrode based on relaxation time distribution analysis, which comprises the following steps: preparing dry-state graphite negative electrode base sheets with different gradient compaction densities and assembling them into button-type simulation batteries respectively; performing alternating current impedance spectrum test on the button-type simulation batteries to obtain frequency domain impedance data; using Tikhonov regularization algorithm to deconvolute the frequency domain impedance data and convert it into a relaxation time distribution spectrum diagram with time constant as the abscissa; locating the characteristic peak of a specific time constant interval in the spectrum diagram and performing integration, and extracting the absolute values of charge transfer impedance and diffusion impedance respectively; comparing the above two impedances under different compaction densities, and determining the compaction density corresponding to the common minimum value of the two as the optimal compaction density. The present application overcomes the problem of alternating current impedance polarization semicircle overlap, and quantitatively determines the compaction state in which electronic conduction and ion transport reach balance.

The invention belongs to the technical field of geophysical signal processing, and relates to a nuclear magnetic resonance adaptive transverse constraint inversion method for a complex geological environment. According to the method, an inversion objective function is constructed through a Tikhonov regularization algorithm, an LM iteration framework is deeply fused, and the numerical stability of an inversion process in an ill-conditioned matrix environment is ensured by utilizing the cooperative adjustment of a damping factor and a unit matrix. According to the method, the limitation that only the correlation coefficient of the simple adjacent measuring points is constructed in the prior art is abandoned, and the self-smoothing correlation coefficient is combined with the Pearson correlation coefficient. Linear correlation of measuring point data is accurately captured through a Pearson coefficient, and a self-smoothing mechanism based on weighted moving average is introduced, so that environmental noise interference is effectively reduced, and continuity of an inversion model in space is enhanced. According to the method, the constraint force can be adaptively adjusted according to the continuity of the underground geologic structure. The anti-noise capability is obviously improved, and the transverse resolution of the nuclear magnetic resonance signal is effectively improved.

The present disclosure relates to various systems and methods for utilizing these T1-T2 maps for enhanced formation evaluation. For example, as described in greater detail herein, these techniques include: (1) Tikhonov regularization used in an NMR inversion and resultant bias, (2) variable depth averaging of NMR two-dimensional (2D) maps on per T1-T2 basis, and (3) NMR kernel correction.

The present application relates to the technical field of flexible riser motion analysis, and particularly relates to a marine dynamic flexible riser motion response reconstruction method based on monitoring data, comprising: using a structural embedded layout method or an external attached layout method to layout sensors in a flexible riser system, and optimizing the sensor layout mode; setting a data acquisition and transmission system on a marine platform to obtain flexible riser motion response data; converting the flexible riser motion response data into flexible riser displacement data based on a Timoshenko beam model and Tikhonov regularization; carrying out noise reduction processing on the flexible riser displacement data based on a wavelet threshold noise reduction method to obtain flexible riser displacement noise reduction data; and carrying out motion response reconstruction on the flexible riser displacement noise reduction data based on variational mode decomposition VMD. The present application can effectively improve the data interpretation, data reconstruction accuracy and real-time performance of motion reconstruction of the flexible riser.

The invention discloses an improved regularized radial gate support arm pulsating load time domain identification method, and belongs to the technical field of radial gate flood discharge vibration monitoring. Comprising the steps of collecting a vibration signal and performing noise reduction; constructing a three-dimensional finite element mathematical model of the radial gate support arm and calculating a unit impulse response function matrix; displacement response of the radial gate support arm under discharge excitation is obtained through Duhamel integration, and an inverse analysis mathematical model is constructed; solving is carried out through a Tikhonov regularization algorithm, regularization parameters are obtained through an ICAA regularization selection method, and the inverse analysis mathematical model is corrected; the vibration displacement response obtained after noise reduction serves as input, iterative solution is conducted on the corrected inverse analysis mathematical model, and the water flow pulsation load is obtained. According to the method, the difficulty that the pulsating load cannot be directly measured or the pulsating load S cannot be solved according to the dynamic response variable in the prior art is solved, the selection accuracy of the regularization parameters is improved, the interference of noise is weakened to a certain extent, and the load identification precision is improved.

A method, an apparatus, a device, and a medium of image processing based on an event camera. The method obtains event data from blurred image captured by the event camera; constructs an initial PINN model, and embeds an event generation equation into the initial PINN model; inputs the event data into the PINN model to obtain a predicted luminance change-gradient value; performs self-supervised optimization using a temporal derivative loss based on the predicted luminance change-gradient value and a ground-truth luminance change-gradient value, and introduces a Tikhonov regularization constraint condition to optimize the PINN model; inputs the event data corresponding to a to-be-processed image into the optimized PINN model to obtain luminance values in logarithmic domain of three color channels of the to-be-processed image; and uses tone mapping to convert the luminance values in the logarithmic domain into reconstructed image frames.

The present disclosure relates to a bridge influence line identification method and system based on two-stage spatial weighted regularization. The method comprises: acquiring dynamic response data of measuring points when a moving vehicle passes through a bridge; using a standard Tikhonov regularization method to solve an influence line to obtain a preliminarily identified influence line; identifying a peak value position; constructing a weight matrix of a smoothing constraint and a weight matrix of a structure preservation constraint according to the distance between the bridge position and the nearest peak value region; the smoothing constraint weight of the bridge position located in the peak value region is smaller than that of the bridge position located in the non-peak value region, and the structure preservation constraint weight of the bridge position located in the peak value region is smaller than that of the bridge position located in the non-peak value region; and using a second objective function to solve the influence line to obtain a finally identified influence line. The present disclosure maintains smoothness, avoids peak reduction, more accurately restores the peak value of the influence line, and takes into account the calculation efficiency.

This application discloses a sub-Sun-level drought monitoring method and system based on gravity satellites, relating to the field of surveying and mapping science and technology. It achieves efficient monitoring through a triple technical architecture: First, it employs a line-of-sight gravity difference algorithm, subtracting the integral reference orbit from the gravity satellite payload ranging data. Based on numerical difference, it obtains the distance acceleration residual, then calculates the line-of-sight gravity difference using a transfer function. The sub-Sun-level mass block of the nadir trajectory is divided into arcs of 2700 epochs (13500 seconds). Combined with Fibonacci point discrete integrals and Tikhonov regularized inversion, it obtains the sub-Sun-level mass change along the orbit. Second, it constructs standardized drought monitoring factors based on the mass change sequence, classifying mild, moderate, and severe drought levels using standard deviation as a threshold. Third, it extracts temporal and spatial features, fuses these features using a fully connected layer, and outputs the drought anomaly probability. The judgment threshold for the anomaly probability is dynamically adjusted, and real-time three-dimensional visualization is achieved through a 5G-A network, improving the accuracy of drought identification.

The invention provides a seabed sedimentary layer structure modeling and parameter inversion method based on very-low-frequency sound wave signals, belongs to the technical field of underwater acoustic engineering and ocean geophysical exploration, and solves the problems that traditional seabed parameter estimation is low in resolution, a local optimization algorithm is prone to local optimization, and underwater acoustic inversion is multiple in solution. The method comprises the following steps of: generating synthetic sound pressure data by using a normal wave model through configuring underwater sound parameters; building an objective function by combining a mean square error with Tikhonov regularization; hybrid optimization inversion is achieved through PSO global search and SQP local optimization, and seabed sedimentary layer parameters are output after verification. According to the method, efficient and accurate forward modeling is guaranteed through a normal wave model, the problem of multiple inversion extremum values is solved through a mixed optimization algorithm, and seabed multilayer parameter high-precision synchronous inversion is achieved.

This invention discloses a method, apparatus, device, and medium for calculating initial residual stress based on additional stress. The method includes: determining a processing strategy based on the current titanium alloy material type, and determining the cutting force volume coefficient matrix and the additional force volume coefficient matrix according to the processing strategy; determining the additional residual stress based on the additional force volume coefficient matrix, and substituting the cutting force volume coefficient matrix, the additional force volume coefficient matrix, and the additional residual stress into a pre-constructed initial residual stress calculation model to obtain the initial residual stress function to be solved; constructing a reverse solution model for the initial residual stress based on the initial residual stress function to be solved, and solving the initial residual stress in the reverse solution model using the Tikhonov regularization method. This invention avoids the drawback of excessive inference errors in the initial residual stress of existing technologies, and improves the accuracy of inferring the initial residual stress of titanium alloy structural components.

This application relates to an adaptive constraint method for determining global mass change using gravity satellite data. First, a noisy global mass change signal is obtained using gravity satellite observation data and a background force model. An initial constraint matrix is ​​constructed using zero-order Tikhonov regularization to obtain the initial constraint solution for global mass change. Second, considering land-sea signal leakage, strong and weak signal constraint matrices are used to calculate the strong and weak signals of global mass change, respectively. Adding the strong and weak signals yields the complete global mass change signal. Finally, considering land signal leakage, the root mean square (RMS) value of the obtained complete global mass change signal is used as prior information to construct a leakage error correction constraint matrix to solve for the leakage error-corrected global mass change signal.