140 results about "Density model" patented technology

The invention discloses a non-Newtonian fluid dynamic characteristic intelligent simulation and integrated measurement and control method, which comprises the following steps: sequentially reading basic data such as equipment temperature, pressure intensity, vibration, shear rate, stress, strain and the like, and carrying out anomaly detection and dynamic weight local polynomial regression cleaning by adopting a Gaussian mixture probability density model; carrying out standardization processing through a self-adaptive standardization model; performing multi-scale feature extraction and correlation analysis on the standardized data, and establishing a mapping relation between the rheological parameters and the physical quantities; carrying out multivariable coupling analysis and carrying out turbulence correction by adopting an improved intrinsic orthogonal decomposition method; and performing simulation calculation based on a non-Newtonian fluid analysis model, and analyzing and evaluating model performance and optimizing model parameters through multi-dimensional error analysis. According to the method, the dynamic characteristics of the non-Newtonian fluid under the complex working conditions can be accurately described, and the simulation precision and the control effect are improved.

The invention relates to the technical field of construction surrounding rock stability evaluation, discloses a tunnel surrounding rock stability evaluation method and system based on multi-physics field parameter inversion, and aims to solve the problems that an existing method is insufficient in data collaboration, high in parameter inversion multiplicity and poor in surrounding rock stability evaluation accuracy and timeliness. According to the scheme, the method mainly comprises the steps that an acousto-optic electromagnetic vibration drill multi-mode sensing array is arranged, and time-space synchronization is implemented; establishing a mutual interference entropy spectral density model to realize multi-physics field collaborative excitation and acquisition; a unified feature vector is obtained through data correction, feature extraction and weighted fusion; a joint inversion objective function embedded with rock physical constraints is constructed, a three-dimensional physical property parameter field is obtained through inversion, and a dynamic permeability field is calculated in combination with acoustic emission energy; and finally, dynamically updating the model by utilizing ensemble Kalman filtering, and obtaining a final risk probability based on updated parameters and seepage-uncertainty coupling correction. According to the method, the accuracy, the real-time performance and the reliability of the stability evaluation of the surrounding rock of the deep-buried tunnel are improved.

Provided in the present invention are a three-phase flow and heat transfer coupling simulation method and system based on an IBM-VOSET-DEM. The method comprises: determining a computational domain and dividing the grid of the computational domain; initializing parameters; reconstructing a liquid-liquid interface; determining a fluid volume fraction at an (n+1)th moment; determining a boundary grid, and determining a signed distance function of a grid cell at an nth moment; determining an interface curvature of the grid cell of the interface; smoothing abruptly changing physical quantities of the interface; determining the surface tension of the interface; determining a fluid intermediate velocity; obtaining fluid volume fractions and fluid intermediate velocities of all of the grid cells in the computational domain; determining the velocity of a particle; determining the displacement of a pth solid particle at the (n+1)th moment and a solid volume fraction of the grid cell at the (n+1)th moment; determining the final velocity of the computational domain at the (n+1)th moment; and solving for a temperature and an interface heat flux density. In the present invention, a DEM soft-sphere model is used to calculate a collision force of a solid, such that microscopic motion information of a solid particle can be accurately obtained and tracked; and an interface heat flux density model is used to take the conjugate heat transfer effect of an interface into consideration.

The invention discloses a gravity inversion method and system based on dual-network driving, and the method comprises the steps: obtaining a training data set which comprises an actual underground medium density model and actual gravity data; constructing a dual-network cascade architecture comprising a downlink density prediction network and an uplink physical property correction network; inputting the gravity data in the training set into a downlink density prediction network to obtain prediction model data; inputting the prediction model data into an uplink physical property correction network to obtain prediction gravity data; potential field loss constraints and model loss constraints are obtained through the prediction data and the real data; performing weighted optimization on the dual-network cascade architecture through potential field loss constraint, model loss constraint and physical loss constraint controlled by a gravity forward kernel matrix operator to obtain an inversion model; and performing inversion on the to-be-inverted gravity data through the inversion model to obtain underground medium density distribution. According to the invention, underground detail features can be reflected more accurately.

The invention relates to a heterogeneous sensor anti-interference dynamic target identification system under a multi-modal visual large model. The system comprises an anti-background interference module which generates spatial and temporal features through modal convolution, LSTM time sequence modeling and weighted fusion of heterogeneous sensor multi-modal data, and screens foreground pixels and extracts foreground features by using a Gaussian probability density model in combination with a time / space attention mechanism and Transform enhancement features; the multi-modal processing module is used for separating each modal foreground feature, refining and then splicing, extracting a consistency feature and a complementarity feature through Transform, and generating an enhanced fusion feature through fusion and pooling enhancement; and the target recognition module fuses the foreground features and the enhanced fusion features, inputs a target detection head after convolution compression and Timesformer long time sequence modeling, and outputs target classification and a bounding box. According to the method, the problems of heterogeneous sensor data interference suppression and dynamic target accurate recognition are solved, and the robustness in a complex scene is improved.

The invention relates to the technical field of pet behavior data processing, and discloses a time-space sequence data processing method and system for pet abnormal behavior recognition, and the method comprises the steps: 1, obtaining multi-modal data, carrying out the time alignment, building a multi-scale scene semantic graph, and generating a grid occupancy frequency and a region transfer matrix; 2, constructing a normal behavior template library, and generating window-level spatio-temporal features; 3, establishing group normal behavior distribution by using a generative density model, calculating a residual error in combination with a time sequence prediction model, and obtaining individual model parameters; 4, performing statistics on historical rhythm distribution and calculating differences of the day to obtain rhythm deviations; 5, fusing multi-component anomalies to obtain a comprehensive anomaly score; step 6, introducing an Internet of Things event to generate a gating coefficient and adjusting an abnormal score; and 7, comparing the abnormal score after gating with a threshold value, and outputting an abnormal alarm. According to the invention, accurate identification and stable alarm of the abnormal behavior of the pet are realized.

The invention relates to the technical field of risk analysis, and discloses a multi-area monitoring system and method based on an AI vision model.The multi-area monitoring method based on the AI vision model comprises the following steps that area information is collected; generating two-dimensional structure data based on the region information; inputting the two-dimensional structure data into a density model, wherein the density model outputs a result representing the people flow density of each region; inputting the two-dimensional structure data into a management and control model, wherein the management and control model outputs a result representing people flow management and control measures; according to the invention, by analyzing the real-time region information of each region and combining the historical people flow data of the region, the influence of people shielding on the people flow density calculation accuracy is reduced, and the people flow management and control measures are obtained according to the people flow density of each region. The system can provide management and control measures which are good in people flow density reduction effect and low in execution cost, and the safety of all areas is improved.

The invention relates to the technical field of geophysical exploration, in particular to a multi-scale three-dimensional gravitational-seismic joint frequency domain inversion method based on hybrid constraints. The method comprises the following steps: determining observation gravity anomaly on the ground by adopting discrete Fourier transform, and discretizing the observation gravity anomaly by adopting a method of weighted average of values at a plurality of Gaussian points; calculating spherical harmonics and spherical harmonic coefficients of two-dimensional density distribution of each layer in the inversion range, and calculating gravity anomalies on the observation surface based on the spherical harmonics and the spherical harmonic coefficients; converting the inversion range data into a three-dimensional gravitational field forward kernel matrix; constructing a three-dimensional gravitational field hybrid constraint inversion objective function; inputting pre-collected earthquake three-dimensional velocity model data, and converting the earthquake three-dimensional velocity model data into a reference density model; and calculating a gradient operator in the inversion objective function, simplifying the inversion objective function based on the gradient operator obtained through calculation, and then solving the simplified inversion objective function by using an alternating direction multiplier method.

The invention discloses an electromechanical industry digital process evaluation method based on data analysis, and particularly relates to the technical field of digital evaluation, a behavior path map, a time sequence causal dependence model and an operation frequency density model are constructed based on information provided by the electromechanical industry, whether structural deviation or behavior abnormity exists is analyzed, and the evaluation result is obtained. If the initial deviation label is generated, calculating a behavior consistency score and a process integrity score, inputting the scores into a fuzzy logic device for risk reasoning, and outputting a credible level; according to the credibility level, judging whether the data is suitable for digital process evaluation, and if the data does not meet the preset requirement, feeding back a corresponding optimization strategy; according to the method, digital behavior structured analysis is realized by constructing a behavior path map, a time sequence causal dependence model and an operation frequency density model; a deviation triggering mechanism is set, and abnormal operation behaviors are accurately recognized; the credibility level is output based on the scoring result, the information applicability is judged according to the credibility level, an optimization strategy is fed back, and the evaluation intelligence level is improved.

The invention relates to a gravity data inversion method and system and a medium, and the method comprises the steps: generating tens of thousands of random single abnormal training sample pairs; based on all the random single abnormal training sample pairs, selecting a part of preset quantity of data as a training data set, and selecting the other part of preset quantity of data as a verification data set; training a series of width learning networks with different network complexities by using a training data set, wherein the target of gravity inversion is to estimate an optimal density model; identifying the width learning network with the most suitable network complexity by using the verification data set; and inputting the measured gravity data vector into the width learning network with the most suitable network complexity to obtain an optimal density model. According to the invention, the precision and efficiency of density structure modeling are improved by using the width learning network and the single abnormal training sample.

Methods and systems for fully quantifying predictive uncertainty for recommender systems are disclosed. Embodiments include providing a fully Bayesian multiway neural network model, assigning a prior distribution over a plurality of weight parameters of a model parameter set of the neural network model such that a corresponding posterior distribution represents an epistemic uncertainty of the neural network model; incorporating a mixture density model for a target layer of the neural network model, wherein the mixture density model models aleatoric uncertainty of input data; estimating the posterior distribution of the weight parameters given training data by updating the prior distribution based on data likelihood of the training data; sampling a plurality of empirical samples from the estimated posterior distribution of the weight parameters; performing inference for each stochastic realization of the neural network model with the sampled weight parameters on an inference sample to generate one or more predictive distributions; drawing samples from each of the one or more predictive distributions as final predictions for the inference sample to obtain a matrix of predictions for the inference sample, wherein the matrix represents a joint distribution of both the aleatoric and epistemic uncertainty of the recommender system.

The invention discloses a supercooled boiling and boiling critical numerical simulation method and device, a medium and equipment, and relates to the technical field of heat transfer science. The method comprises the following steps: establishing a mixture model, and adding inertial acceleration volume force generated in a non-inertial system into a momentum conservation equation of the mixture model to obtain a non-inertial system mixture model; establishing a bubble dynamic model according to the various acting forces acting on the wall bubbles and the inertial acceleration volume force, and obtaining a plurality of bubble characteristic parameters according to the bubble dynamic model; establishing a wall surface heat flow distribution model, and substituting the bubble characteristic parameters into the wall surface heat flow distribution model to obtain a wall surface heat flow density model; and coupling the wall surface heat flux density model and the non-inertial system mixture model to obtain a two-phase boiling heat transfer and boiling critical calculation model which is used for simulating a boiling heat transfer working condition under a motion condition. The numerical simulation method is few in computing resources, wide in application range, good in convergence and high in prediction precision.

The embodiment of the specification discloses a self-adaptive variable grid forward modeling method for middle-deep dry hot rock mass. p , a transverse wave velocity V s and a medium density p model, pre-setting forward modeling parameters; performing self-adaptive variable grid processing on the velocity model, determining a mapping relationship between coordinate systems, and determining a wave equation under variable grid based on the mapping relationship; performing self-adaptive variable grid processing on the velocity model, determining a mapping relationship between coordinate systems, obtaining the underground medium longitudinal wave velocity, transverse wave velocity and density after variable grid, determining a wave equation under variable grid based on the mapping relationship, and generating final regular grid horizontal direction longitudinal wave field, horizontal direction transverse wave field, vertical direction longitudinal wave field and vertical direction transverse wave field information by using the mapping relationship reversely according to the wave field information in each direction at a specific moment after the variable grid, so that the memory occupation is small and the calculation speed is faster.

The invention provides a Bayesian fusion-based 4D millimeter wave radar point cloud enhancement and target detection method, and the method comprises the steps: 1, projecting a 4D millimeter wave radar point cloud to an image plane, carrying out the matching of an image instance segmentation result, and obtaining an image semantic category and image segmentation confidence; 2, constructing a class conditional probability density model based on the physical characteristics of the radar points; based on a Bayesian probability framework, carrying out weighted fusion on the image segmentation confidence and the class probability density to obtain fusion confidence; 3, taking the fusion confidence and semantic category codes as augmented features, and performing feature splicing on the augmented features and the 4D millimeter-wave radar point cloud to generate a semantic enhanced point cloud; and sampling the semantic enhanced point cloud by adopting a sampling method fusing confidence guidance, and inputting the sampled semantic enhanced point cloud into a detection network for training and reasoning. According to the method, the technical problems of unidirectional dependence, shallow geometric matching and insufficient utilization of radar physical characteristics in the existing image and radar point cloud fusion technology are solved.

The invention discloses a dry method dense medium coal dressing intelligent process prediction control method which comprises the following steps: firstly, collecting data of raw coal to be washed, and counting prior selectability information of the collected data; establishing a bed density dynamic model based on evolution time, and forming stable bed density; then a sorting density model is obtained through calculation, an empirical distribution function model based on the sorting density model, possible deviation of a sorting system and boundary control parameters is established, and the product distribution rate corresponding to each density level interval is calculated; and finally, predicting and calculating the product yield and ash content of the coal dressing system according to the priori selectability information and the product distribution rate corresponding to each density level interval, and correcting the product yield and ash content. According to the method, different dry-method dense medium separation mathematical models are fused, the description capacity of the models for the steady-state and dynamic characteristics of the separation process is enhanced, the prediction precision of the clean coal ash content and the yield is improved, and the problem that dynamic control is difficult to implement due to lack of theoretical models in the dry-method dense medium field is solved.

The invention discloses a transformer loss calculation method and structure optimization method based on a finite element method.The transformer loss calculation method comprises the steps that firstly, the finite element method is used for conducting loss calculation on a transformer, and the transformer loss calculation method comprises the steps that S1, transformer parameters needed by transformer loss are calculated according to the finite element method, and a transformer three-dimensional model is established on finite element analysis software; s2, setting circuit constraint and magnetic circuit constraint of the three-dimensional model of the transformer, coupling a circuit interface of the three-dimensional model of the transformer with a magnetic circuit interface of the three-dimensional model of the transformer, solving iron core loss by utilizing a Steinmetz model, and solving transformer winding copper loss by utilizing a resistance thermal model; s3, applying a physical field to the three-dimensional model of the transformer, solving the current density, the volume loss density and the magnetic flux density model of the transformer by utilizing a circuit interface and a magnetic circuit interface of the three-dimensional model of the transformer, improving the speed and the precision of transformer loss calculation, and then optimizing the structure of the transformer according to the volume loss density to obtain the transformer loss. And the transformer loss is effectively reduced.

The invention relates to the technical field of bridge monitoring, and provides a Copula-based bridge response and load joint probability modeling method, which comprises the following steps: S1, acquiring a structure response index and a load parameter through a structure health monitoring system, and carrying out probability density modeling on the structure response index and the load parameter through a Gaussian mixture model to obtain a probability density model; screening and fitting an optimal probability distribution model of each structure response index and load parameter through a Bayesian information criterion; s2, analyzing the correlation between variable pairs formed by the structure response indexes and the load parameters by adopting a Pearson correlation coefficient, and determining the strength and direction of the correlation; s3, constructing a joint probability density function between variables through a Copula probability density modeling method in combination with the fitted variable probability distribution marginal model; and S4, verifying the constructed joint probability density function by adopting a Monte Carlo sampling method. According to the method, the multivariable joint probability model between the key load and the response is generated, and the uncertainty is quantified.

The invention discloses a privacy protection image compression method and device, equipment and a storage medium, and relates to the technical field of image privacy protection, and the method comprises the steps: obtaining an original image, and determining a retrieval reference image according to the original image; performing image compression quantization potential characterization on the retrieval reference image, and determining a first quantization feature and a second quantization feature; performing entropy loss modeling on the probability distribution of the second quantitative feature by using a factorization density model to determine entropy loss; embedding the online semantic features of the original image into the first quantitative features based on the entropy loss to generate a reconstructed image; calculating a signal retention loss and a semantic embedding loss based on the original image, the retrieval reference image, and the reconstructed image; and restraining the potential representation of the reconstructed image based on the entropy loss, the signal retention loss and the semantic embedding loss, and generating a target reconstructed image. Privacy is effectively protected, and high-efficiency and high-quality image compression is realized.

The present invention discloses an inversion method for arbitrary variable density interfaces, and specifically relates to the field of gravity exploration technology. Based on the method of three-dimensional prism partitioning, the present invention constructs a three-dimensional density interface model in a three-dimensional space grid, and by performing Taylor series expansion on the density interface forward function and retaining the first-order term, the linear function of the density interface model forward is approximately obtained and brought into the regularization objective function, and the density interface inversion iterative equation is obtained by minimizing the objective function, and the density interface inversion model is solved using a linear conjugate gradient algorithm. By iteratively correcting the density interface multiple times, the density interface and density model of the study area are inverted. The present invention realizes the accurate acquisition of density interface distribution under complex geological structures, and based on the fast algorithm of three-dimensional gravity inversion, realizes batch rapid inversion of gravity-density interfaces, laying a foundation for the exploration and development of complex geological structures.

The invention belongs to the technical field of geophysical inversion, and relates to a gravity anomaly three-dimensional inversion method based on multi-scale feature fusion, which comprises the following steps: randomly generating density anomaly bodies with different numbers, densities, sizes and shapes to obtain an underground medium three-dimensional density model; carrying out forward modeling on the three-dimensional density model of the underground medium to obtain two-dimensional gravity abnormal data of the earth surface; a multi-scale feature fusion model is trained with the underground medium three-dimensional density model as output and the two-dimensional gravity anomaly data as input, and the multi-scale feature fusion model comprises a two-dimensional encoder, a three-dimensional decoder, a dimension converter and a multi-scale feature fusion device, wherein the dimension converter and the multi-scale feature fusion device are located between the two-dimensional encoder and the three-dimensional decoder; and inputting actually measured two-dimensional gravity anomaly data into the trained multi-scale feature fusion model, and performing inversion to obtain a three-dimensional density body constrained by a physical range. According to the method, the expression capability of the multi-scale features of the underground structure is enhanced, and the multi-solution problem and the deep resolution reduction problem of inversion can be relieved.

The application discloses a land geophysical exploration method, an electronic device and a readable storage medium, and belongs to the technical field of land geophysical exploration. The land geophysical exploration method comprises the following steps: detecting a first seismic wave generated by vibration at a preset position of the underground or the ground and transmitted to the ground; detecting a second seismic wave transmitted by the vibration to the air above the ground; calculating a ground parameter in a set range of the preset position according to the first seismic wave and the second seismic wave; and calculating an underground velocity model and a density model according to the ground parameter. The second seismic wave transmitted to the air from the ground is detected by utilizing the characteristics that the sound speed and the density of the air are consistent under the same meteorological condition, so that the subsequent inversion algorithm is more accurate, and the underground velocity model and the density model are more accurate.

The invention discloses a method for simulating particle transport and related equipment, and relates to the technical field of radiotherapy, and the method comprises the steps: constructing an equivalent density model along a particle transport direction based on the structure parameters of an energy regulator combination, and building a matched boundary information table, the boundary information table at least records the position information of the downstream boundary of each energy regulator piece in the energy regulator combination, the equivalent water thickness accumulated from the incident end to the boundary, and the air gap length between the boundary and the upstream boundary of the next energy regulator piece; obtaining a particle current position and boundary information table; the medium area where the current free path of the particles is located is judged, and differential calculation is carried out on offset generated by movement of the particles in different areas according to the medium area until the particles pass through the energy regulator combination to be transported; according to the method, rapid and accurate judgment and differential offset calculation of the boundary are realized, so that the boundary processing complexity is remarkably reduced and the calculation efficiency is improved on the premise of ensuring the physical simulation precision when the particles pass through the energy regulator.

The invention discloses a power consumption abnormity identification method based on density and a clustering algorithm, and belongs to the field of power supply and consumption management. Comprising the steps of data collection; preprocessing the data; analyzing user energy consumption characteristics; constructing an energy consumption characteristic index library; screening power utilization abnormity suspected users based on a density calculation model; performing normal user clustering analysis; and identifying a real abnormal user. A density and clustering algorithm is applied to an enterprise user electricity consumption abnormity identification process in a power supply management process, abnormal suspicion users are preliminarily screened by using a density model, and then FCM clustering analysis is performed on residual normal electricity consumption users, so that electricity consumption characteristics of the normal users are obtained; and finally, through similarity analysis of power utilization characteristics of suspected users and normal users, real power utilization abnormal users are screened out. The system aims to monitor illegal power consumption behaviors of key energy-consuming enterprises, supervise and urge the key energy-consuming enterprises to produce and operate according to rules, and guarantee economic benefits of power grid enterprises. The method can be widely applied to the fields of operation management of power supply companies and power utilization monitoring of enterprise users.

The invention discloses a hot dry rock type geothermal resource prediction method and device, a medium and a program product, and the method comprises the steps: firstly carrying out the gravity inversion through the gravity measurement data of a to-be-researched region, and constructing a three-dimensional density model of a hot dry rock body; constructing a hot dry rock volume model according to the geothermal geological data; gridding the hot dry rock volume model to obtain a hot dry rock space grid model; performing geological attribute assignment on each cell in the hot dry rock space grid model according to the geological attribute of the to-be-studied area to obtain a hot dry rock attribute model; performing temperature numerical simulation on each thermal reservoir in the hot dry rock space grid model by using temperature data of the to-be-researched area to obtain a hot dry rock temperature model; and on the basis of the hot dry rock space grid model, the attribute model and the temperature model, the hot dry rock geothermal resource quantity is predicted, and a hot dry rock type geothermal resource quantity prediction result is obtained. According to the embodiment of the invention, the prediction accuracy of the hot dry rock type geothermal resources can be effectively improved.

The invention relates to a negative Poisson's ratio metamaterial enhanced seamless expansion joint design analysis method, which comprises the steps of collecting geometric construction parameters and mechanical property data of a negative Poisson's ratio metamaterial, and constructing a training data set; training a deep learning reverse design model and a mechanical property prediction model; the design parameters of the expansion joint device are optimized through a multi-objective optimization algorithm; an optimized expansion joint device is manufactured by adopting a 3D printing technology, the durability of the expansion joint device is evaluated by combining mechanical property data and a fatigue life prediction model, and iterative optimization is performed through a deep learning reverse design model. The expansion joint device has the beneficial effects that the impact resistance and the energy absorption capacity of the expansion joint device are remarkably enhanced through the design of the concave hexagonal honeycomb structure of the negative Poisson's ratio metamaterial and the nonlinear strain energy density model optimization geometric parameters.

The embodiment of the application relates to the technical field of sandstone type uranium ore exploration, and particularly relates to a method for determining a favorable area of a sandstone type uranium ore, which comprises the following steps: collecting well logging data of an exploration area of the sandstone type uranium ore, and gradually determining argillaceous content, total porosity of the exploration area, sandstone content of a sand and shale mixed matrix, longitudinal wave time difference, transverse wave time difference and density, equivalent elastic modulus of dry rock of the exploration area, and conversion relationship between longitudinal wave velocity and transverse wave velocity of rock under saturated fluid condition and a transverse wave velocity prediction curve; then, longitudinal wave impedance, velocity ratio of longitudinal wave and transverse wave and a density model are established; according to the model, three-dimensional elastic data bodies of impedance, density and shear modulus of longitudinal wave and transverse wave are determined by inversion; and according to the three-dimensional elastic data bodies, the favorable area of the sandstone type uranium ore is determined. The method for determining the favorable area of the sandstone type uranium ore according to the embodiment of the application can effectively improve the prediction accuracy of a favorable area of a sandstone type uranium ore.

Approaches presented herein provide for the reconstruction of implicit multi-dimensional shapes. In one embodiment, oriented point cloud data representative of an object can be obtained using a physical scanning process. The point cloud data can be provided as input to a trained density model that can infer density functions for various points. The points can be mapped to a voxel hierarchy, allowing density functions to be determined for those voxels at the various levels that are associated with at least one point of the input point cloud. Contribution weights can be determined for the various density functions for the sparse voxel hierarchy, and the weighted density functions combined to obtain a density field. The density field can be evaluated to generate a geometric mesh where points having a zero, or near-zero, value are determined to contribute to the surface of the object.

The invention relates to a method and apparatus for single epoch position bounding. A method for determining a protection level for a position estimate using a single epoch of GNSS measurements, the method comprising: specifying a prior probability density of states x P(x); specifying a system model h(x) relating states x to measured observations z; quantifying a quality metric q associated with the measurements; specifying a non-Gaussian residual error probability density model f(r|θ,q) and fitting the model parameters θ using a set of experimental data; defining a posterior probability density P(x|z,q,θ); estimating states x; and calculating the protection level by integrating the posterior probability density P(x|z,q,θ) over states x.

The application provides a continuous casting crystallizer vibration device residual life prediction method, comprising the following steps: S1. obtaining the vibration deflection of the continuous casting crystallizer vibration device to be measured and taking the vibration deflection as degradation data; S2. constructing a continuous casting crystallizer vibration device degradation model; S3. determining a drift coefficient in the degradation model and a parameter vector of the drift coefficient based on a nonlinear function regression algorithm; S4. estimating the prior parameters of the continuous casting crystallizer vibration device degradation model based on an expectation maximization (EM) algorithm and determining optimal prior parameters θ; S5. constructing a residual life probability density model, substituting the prior parameters into the residual life probability density model to calculate residual life probability values, and taking the residual life corresponding to the maximum probability value as the final prediction value of the residual life of the continuous casting crystallizer vibration device; only the degradation data of the continuous casting crystallizer vibration device to be measured is needed to accurately predict the residual life of the vibration device.

A method for 3D inversion of Full Tensor Gradiometry (FTG) data comprising receiving observed FTG data, performing a kernel matrix calculation on a subset of the observed FTG data and the gravity anomaly data, performing one-time forward modeling in a wavenumber domain to produce predicted FTG data and predicted gravity anomaly data for the reference density model, performing a residual between the observed data and predicted data, performing a depth-weighting function, a model covariance matrix, and data error covariance matrix on the observed FTG data and observed gravity anomaly data, obtaining a model update based on the depth-weighting function, kernel matrix, the model covariance matrix and the data error covariance matrix, and the residual between the observed FTG data and the predicted FTG data in the wavenumber domain, and performing inversion by directly obtaining an inverted model based on the model update and reference model.