1767 results about "Estimation methods" patented technology

The invention relates to a power distribution network state estimation method based on an adaptive space-time diagram neural network, and the method mainly comprises the following steps: S1, collecting historical and real-time measurement data of a power distribution network, and carrying out the preprocessing of the data, so as to guarantee the integrity of the data, provide high-quality input data for a model, and improve the estimation precision and stability of the model; s2, discrete wavelet transform is carried out on historical measurement data, multi-scale decomposition is achieved, and low-frequency and high-frequency components are extracted; a double-branch time sequence fusion module is constructed, global trend and local fluctuation features are respectively captured through a dynamic attention mechanism and a time convolution network, and the features are efficiently fused by means of adaptive weights. S3, in the real-time data processing process, branch measurement features are extracted through a multi-layer perceptron (MLP) and mapped to nodes of the whole network, dynamic integration of historical data and real-time data is achieved, and therefore the real-time performance and accuracy of state estimation of the power distribution network are improved.

The invention relates to a meter state adaptive estimation method supporting data incomplete completion. The method comprises the following steps: acquiring continuous measurement data of a meter under a unified time reference to form an original measurement data sequence; generating a state parameter set for describing the current working state based on the original measurement data sequence; dividing an original measurement data sequence into a plurality of data windows, and predicting an expected measurement value in each window according to the variation amplitude and trend direction of collected data in adjacent time periods; comparing the predicted measurement value with an actual measurement value, and identifying positions with difference values exceeding a set threshold value to form an incomplete position index set; aiming at the incomplete position index set, in combination with the change trend and continuity of adjacent data, searching a complementation value in a preset reasonable numerical value interval, and generating a complemented measurement data sequence; according to the invention, the integrity and accuracy of the meter data under the condition of loss or abnormity are improved.

The invention relates to a data processing method and system for multi-source complex biological information data. According to the method, expression profile data, gene variation data and clinical survival data are collected, and unified standardization processing is carried out on the collected data. Feature alignment is performed on different source data based on sample identifiers, a joint feature expression matrix is constructed, and a context dependency relationship across data types is maintained. On the basis, multi-stage feature screening is carried out through Lasso regression and information gain evaluation in sequence, and an optimal feature subset used for modeling is obtained. And further training a risk scoring model by adopting a Cox proportional risk regression method, and calculating a risk scoring value of the sample by utilizing the constructed continuous scoring function. And finally, dividing the score value into a plurality of risk levels, and generating a survival curve of each level in combination with a Kaplan-Meier estimation method so as to verify the risk layering effect and prediction significance of the model. According to the method, the accuracy of biological information modeling can be improved, and the method has good universality and practical value.

The invention discloses a three-dimensional attitude estimation method combining global modeling and local refinement, which comprises the following steps of: firstly, extracting a two-dimensional attitude sequence by using a human body video data set; secondly, inputting the two-dimensional attitude sequence into a structural modeling main branch, modeling a spatial topological relation and a time sequence dynamic state between joints, and outputting a global three-dimensional attitude sequence; and inputting the two-dimensional attitude sequence into a local refining branch, modeling dynamic change and detail information of a local area, and outputting a local three-dimensional attitude sequence. And finally, fusing the global three-dimensional attitude sequence and the local three-dimensional attitude sequence, generating a three-dimensional attitude sequence output, and completing three-dimensional attitude estimation. According to the method, the problem of insufficient cross-frame information transmission in a traditional method is relieved, and the accuracy and robustness of attitude estimation in a dynamic complex scene are remarkably improved.

The invention discloses a shield muck volume estimation method and system based on image processing, and particularly relates to the technical field of tunnel engineering monitoring, and the method comprises the steps: obtaining muck RGB and depth images through an image collection device, generating high-precision three-dimensional point cloud data through the combination of laser scanning and a multispectral technology, and calculating the muck volume through a slicing method. And fusing the mass flow and water content data of the belt weigher, dynamically generating a calibration factor, and correcting a volume estimation result in real time. The system comprises an image acquisition module, a point cloud processing module, a volume calculation module and a dynamic calibration module, has the characteristics of automation, high precision, real-time feedback and the like, effectively solves the problems of large error and slow response of a traditional manual metering and single weighing mode, and is suitable for continuous and accurate monitoring of the volume of muck in the shield construction process.

The invention belongs to the technical field of three-dimensional reconstruction in computer graphics, particularly relates to a sparse view outdoor scene three-dimensional reconstruction method based on multi-view stereo and grids, and aims to solve the problems that input depends on dense views, rendering results are prone to generating fuzzy artifacts, large-scale scene memory occupation is high and the like in the existing reconstruction technology. The method is especially suitable for high-precision reconstruction of an outdoor boundless scene under sparse view input. The method comprises the following implementation steps: acquiring a multi-view image of a scene to be reconstructed and a corresponding camera pose, and determining a scene center based on a K-means clustering algorithm; extracting input view features, generating a cost body based on a multi-view three-dimensional framework, and obtaining scene depth distribution priori by combining a probability depth estimation method; guiding voxel grid sampling based on depth distribution, and mapping light rays exceeding a grid boundary into a limited grid range by using a spatial contraction function; extracting image local features and voxel grid global features for adaptive fusion; and finally, generating a reconstructed image through volume rendering.

The invention discloses a smart power grid state estimation method and system based on a space-time diagram convolutional network, and a medium, and the method comprises the steps: building a topological graph model of a power system according to the topological structure and measurement data of a smart power grid; an STGCN model containing a plurality of space-time convolution blocks is constructed, each space-time convolution block captures time dependence of time sequence data through a time gating convolution layer, and space features of the smart power grid are extracted through a space graph convolution layer; and establishing a power grid state estimation model based on the STGCN model to perform power system state estimation. According to the method, the accuracy and the reliability of an estimation result are ensured by fusing a space and time convolution technology. State estimation challenges under various topological structures can be effectively handled, stable estimation performance can be maintained, and a powerful guarantee is provided for safe operation of a smart power grid. And the operation efficiency and the response speed of the smart power grid are greatly improved.

The invention discloses an image feature matching model, estimation method and system based on spatial geometric constraints. The image feature matching model at least comprises a feature extraction module, a differentiable soft sampling module and a spatial pose regression module. The feature extraction module uses a U-Net backbone network as a feature encoder to output feature maps under different scales, combines the global attention module as a feature decoder to obtain a preliminary matching result, and adjusts the matching result through the local attention module to realize a high-precision sub-pixel matching relationship; the differentiable soft sampling module introduces epipolar geometric constraint in a model training process, supervises a feature point extraction and matching process in an end-to-end manner, and ensures differentiability of a sampling process from two-dimensional matching to three-dimensional pose estimation; and the spatial pose regression module is used for regression to obtain a basic matrix for camera pose estimation. According to the method, the camera pose can be directly output, and the method shows higher precision in indexes such as F1 score, interior point proportion and average epipolar error.

The invention discloses a DOA (Direction of Arrival) estimation method and system based on a deep complex value convolution attention residual network, belongs to the technical field of array signal processing, and solves the technical problems of low precision and poor robustness of the existing DOA estimation method under the severe conditions of low signal-to-noise ratio, limited snapshot number and the like. The method comprises the following steps: acquiring data by using a co-prime array and preprocessing to obtain SCM data as original input information of DOA estimation; constructing a deep complex value convolution attention residual network to directly process covariance matrix information of a complex field, and utilizing an initial two-dimensional complex value convolution layer, a cascaded complex value convolution block attention network and a cross-layer residual connection structure to deeply extract complex value features related to a space angle; the DOA estimation module is responsible for finally mapping the extracted high-dimensional complex value feature vector to a representation space directly related to a DOA estimation task, and the output module converts the internal feature representation output by the DOA estimation module into a DOA estimation result which can be explained by a user.

The invention discloses a multi-model coupled regional terrestrial ecosystem carbon reserve estimation method, which is suitable for the field of carbon cycle and carbon reserve estimation. The method comprises the following steps: firstly, collecting remote sensing data, and then estimating overground, underground and soil annual carbon density mean values of a regional terrestrial ecosystem by adopting an ARIMA model; the method comprises the following steps: clustering regions with different carbon densities by adopting a CatBoost model, dividing the carbon densities of a regional ecological system into low, medium and high levels, and avoiding the influence of spatial heterogeneity among different density levels on subsequent model training; an RNN model is constructed to evaluate the carbon density conditions of different levels of unit grids, and interannual dynamic carbon density parameters of different types of ecosystems are obtained based on multi-source data; and finally, calculating the carbon reserves of the unit grids, analyzing the annual average change trend and the spatial distribution condition of the carbon reserves, and realizing accurate evaluation of the regional terrestrial ecosystem carbon sink. The method is simple in step and good in using effect, and the change condition of the carbon sink capacity in the corresponding time can be obtained according to the sampling time interval of the input data.

The invention relates to the technical field of mineral resource estimation, and discloses a mineral reserve estimation method based on artificial intelligence, comprising the following steps: step 1, collecting and preprocessing data, acquiring multi-source data, and performing data standardization and feature extraction; 2, carrying out ore body modeling through a 3D convolutional neural network, constructing the 3D convolutional neural network, and carrying out ore body prediction through three-dimensional convolution calculation; step 3, optimizing ore body space prediction in combination with Kriging interpolation, preliminarily predicting ore body space distribution through Kriging interpolation, and further optimizing by using a deep learning model; and step 4, estimating reserves based on Bayesian optimization. The technical scheme of combining multi-source data fusion, a 3D convolutional neural network and Kriging interpolation is adopted, the effect of accurately capturing ore body space distribution under the complex geological condition is achieved, and compared with an existing scheme depending on a traditional geological statistical method, the problem that the fault zone and heterogeneous region modeling capacity is insufficient is solved.

A DoA estimation method and device based on steering vector matrix reconstruction, related to the field of array signal processing. The method includes: obtaining an array sampling covariance matrix according to an array received signal; setting a target variable, and limiting a feasible domain of the target variable by using two operators to determine a first constraint condition; characterizing an estimation error based on the target variable and the array sampling covariance matrix, and using the characterized estimation error as a second constraint condition; establishing an initial optimization model according to a preset norm based on partial sum of singular values and constraint conditions of the target variable; determining a multivariable optimization model according to the initial optimization model; and solving the multivariable optimization model to obtain an optimal result; analyzing the optimal result to obtain a DoA of the target incident signal.

The invention discloses an attitude estimation method based on multi-class object dynamic key point learning and progressive optimization. The attitude estimation method comprises the following steps: carrying out outlier filtering on dense point clouds converted from depth images by adopting 3D image convolution to obtain filtered point clouds; obtaining key points and feature representation of the object by using the filtered point cloud coordinates, the RGB features of the object and the geometric features of the filtered point cloud through a multi-modal key point querier; dense point cloud geometric features are extracted by using a dynamic graph convolutional network, and in combination with local multi-level features based on key points and a feature reconstruction model, the feature expression ability in a complex scene is enhanced by using attention weighted fusion; and finally, through a progressive optimization module, on the basis of a pose residual prediction network and a parameter dynamic updating mechanism, object pose information is output after three times of iterative computation. According to the method, a dynamic key point learning mechanism based on three-dimensional geometric feature guidance is constructed, and the generalization ability and geometric perception precision of attitude estimation are effectively improved by fusing multi-modal perception information and an iterative optimization strategy.

Embodiments of the present application relate to the field of computer vision. Disclosed are a target six-degree-of-freedom pose estimation method and apparatus based on binocular vision and particle swarm optimization. The method comprises: obtaining a binocular image of a target, wherein the binocular image at least comprises a rotation matrix, a displacement vector and geometric features of the target; decomposing the rotation matrix into a rotation vector and a rotation angle by means of a rotation matrix decomposition method, and performing six-degree-of-freedom uniform sampling on the rotation vector and the rotation angle to obtain a data point set; establishing a mathematical relationship between the rotation matrix and the displacement vector on the basis of the geometric property of the geometric features and the data point set; and performing target pose estimation on the binocular image of the target by designing an objective function and an application optimization algorithm to obtain a pose estimation result. The present application solves the problems in the related art of low accuracy, poor universality and poor robustness.

The invention discloses a pose estimation method and device based on point pair feature matching, a medium and a product. The method comprises the following steps: performing coarse registration on a fusion scene point cloud and each template point cloud, and calculating point pair features; according to the corresponding hash key values, retrieving from each hash table to establish registration point pairs; calculating a translation vector and a rotation quaternion matched with each registration point pair according to the reference point coordinate system and the template coordinate system, and performing clustering processing on each registration point pair to obtain each initial corresponding set; screening out a target pose candidate transformation matrix and a matched target template point cloud from the pose candidate transformation matrixes matched with the initial corresponding sets respectively; a target optimization function between the fusion scene point cloud and the target template point cloud is constructed, iterative optimization is performed on parameters in the target pose candidate transformation matrix, and the target pose of the fusion scene point cloud is obtained at the end of iteration, so that the precision, real-time performance and reliability of six-dimensional pose estimation are improved.

The invention discloses a nonlinear aerodynamic damping estimation method based on LSTM, and the method comprises the following steps: 1, building a nonlinear state space model of a structure based on structural response, including a state equation, an observation equation and a relation between nonlinear aerodynamic damping and structural vibration amplitude; 2, performing updating and covariance prediction on response data by using unscented Kalman filtering; 3, correcting the Kalman gain in real time by using a long short-term memory network; 4, performing state updating and covariance updating based on the corrected Kalman gain; 5, training the long-short-term memory network through an unsupervised learning mode, optimizing the filtering performance, and defining a mean square error of a posterior observation predicted value and a real observation value as a loss function; and 6, calculating the nonlinear aerodynamic damping according to the estimated nonlinear aerodynamic damping parameters. The invention further discloses a nonlinear aerodynamic damping estimation system based on the LSTM and a storage medium.

The invention relates to an equipment residual life prediction method based on a physical-data model, and the method comprises the steps: building a degradation model based on a physical degradation mechanism and a standard Wiener process model; obtaining fixed parameters in the degradation model by using a maximum likelihood estimation method in combination with a nonlinear regression method; acquiring an equipment state observation value in real time, and updating random parameters in the degradation model by using a weight optimization particle filter algorithm; and utilizing the degradation model, the fixed parameters in the degradation model and the random parameters in the degradation model to obtain a probability density function of the residual life of the equipment under a random failure threshold value, and performing numerical integration on the probability density function of the residual life of the equipment to obtain an expected value of the residual life of the equipment, and outputting the expected value as the residual life of the equipment. Compared with the prior art, high-precision and high-reliability residual life prediction is realized by fusing a physical mechanism and a data driving method.

The invention discloses a human body posture estimation method based on radar point cloud imaging and multi-dimensional feature fusion, and relates to the cross technical field of computer vision and radar signal processing, and the method comprises the following three key technical links: firstly, improving the target resolution through spatial energy distribution estimation; reconstructing target three-dimensional space distribution by using the positive correlation between radar signal energy and a target reflection area and adopting a least square estimation algorithm; secondly, constructing a structured multi-dimensional point cloud matrix, and converting sparse radar point cloud into high-information-density imaging representation through a distance-speed hierarchical sorting strategy; and finally, designing a multi-dimensional feature fusion attitude estimation network, integrating three-dimensional convolution, a multi-head attention mechanism and a gating circulation unit, and realizing collaborative extraction of spatio-temporal features. According to the method, the problems of sparse target features, noise sensitivity and poor universality in traditional millimeter wave radar attitude estimation are solved.

An estimation apparatus comprises: a collection unit configured to collect a plurality of pieces of update information used for updating reference information, which indicates measurement values in accordance with a position or an attitude of a second portion against a first portion which is measured by at least one magnetic sensor while changing at least one of the position and the attitude of the second portion against the first portion over a predetermined movable range; a clustering unit configured to perform clustering on the plurality of pieces of update information according to a predetermined classification condition; and an updating unit configured to update the reference information based on at least one piece of update information included in a maximum class into which a highest number of pieces of information are classified among the plurality of pieces of update information.

The invention provides a single tree segmentation-biological parameter estimation method based on a vehicle-mounted LiDAR point cloud, and belongs to the technical field of urban landscaping intelligent monitoring. The point cloud individual tree segmentation algorithm based on tree geometric feature constraint is designed for solving the problem that individual tree segmentation is difficult due to crown overlapping in an urban scene, and the method takes a tree geometric structure as a constraint, combines point cloud reflection intensity information, a clustering algorithm, a main direction index and other means, and obtains the individual tree segmentation algorithm based on the tree geometric feature constraint. And accurate extraction of trunks and crowns in the scene point cloud is realized. Aiming at the problems of high feature redundancy, poor model interpretability and the like in an existing estimation method, a random forest model is taken as a basis, an adaptive feature selection algorithm is introduced to improve variable screening efficiency, hyper-parameters are dynamically adjusted by utilizing pigeon inspired optimization, and generalization and stability of the model are enhanced; an estimation model used for estimating biological parameters such as leaf area index, biomass and carbon reserve is designed.

The invention provides a power system state estimation method and system based on an edge aggregation graph attention network, and high-precision and high-robustness power system state estimation is realized by combining a graph attention mechanism, edge feature aggregation and physical equation constraint. The core target of the invention is to enhance the contribution degree of edge features in state estimation; dynamically distributing attention weights of the nodes and the lines; and model output is ensured to strictly meet physical equation constraints of the power system.

The invention relates to the technical field of glacier thickness inversion, particularly discloses a glacier thickness inversion method fusing a laminar flow model and random unit interpolation, and aims to solve the problems that a traditional glacier thickness estimation method based on the laminar flow model excessively depends on a glacier center streamline, and the glacier center streamline in a complex area is difficult to accurately obtain. A random unit interpolation method is introduced, the dependence of glacier thickness estimation on a central streamline is avoided, and glacier terrain and flow velocity data are fully utilized. Besides, the difference of different glacier shape factors is considered in the model, specific kinetic parameters are distributed for the glacier in combination with temperature information, and manual parameter determination can be effectively avoided. Experimental results show that glacier thickness distribution which is more practical can be obtained through the improved method.

The invention aims to solve the problem of insufficient robustness of a traditional power grid state estimation method in post-disaster recovery and emergency communication scenes, and provides a power system emergency state estimation method based on an unmanned aerial vehicle collaborative graph neural network. According to the method, unmanned aerial vehicle dynamic communication scheduling and a space-time diagram neural network are fused, and the problems of data missing, topology time varying, communication interruption and the like are solved. A node voltage estimation and confidence thermodynamic diagram is generated by constructing a simulation data set, designing a space-time diagram convolutional network and combining a diagram feature reconstruction module, and a priority recovery strategy is formulated. Meanwhile, an unmanned aerial vehicle emergency communication optimization model is established, and efficient distribution of communication resources is realized. The method has the advantages that an information-physical system collaborative optimization method is provided, a rapid and reliable communication alternative scheme is provided, and the problems of measurement missing and topology time varying are solved. By establishing a closed-loop feedback model, dynamic optimization of communication resource allocation scheduling is realized, and the method adapts to a real-time dynamic mode of a power system.

The invention discloses a sparse Bayesian direction of arrival estimation method based on subspace compression and dictionary optimization, and belongs to the field of array signal processing. According to the method, the data dimension is reduced through the subspace compression technology, and the noise immunity is improved; signal power and noise power are automatically estimated in combination with a sparse Bayesian model, and dependence on information source number information is avoided; the calculation efficiency and the numerical stability are improved through a support set adaptive pruning strategy; and finally, a dictionary fine tuning mechanism is introduced, direction continuous optimization is realized on the basis of an original discrete grid, an off-grid error is eliminated, and direction-of-arrival estimation with sub-resolution precision is realized. The method is a novel method combining subspace compression, sparse Bayesian inference, adaptive pruning and angle optimization, can give consideration to estimation precision, calculation efficiency and application robustness, and is especially suitable for direction estimation under the complex actual conditions of low signal-to-noise ratio, few snapshots, unknown signal source number and the like.

The invention discloses a traffic flow state estimation method considering charging influence factors of a highway service area, and belongs to the field of traffic flow evaluation of energy fusion, and the method comprises the steps: obtaining a sample data set, carrying out the feature extraction of the sample data set, and obtaining the time sequence features of the sample data set; wherein the time sequence characteristics comprise indexes indirectly reflecting charging influence factors; performing time interval division on the time sequence characteristics of the sample data set to obtain an input sequence sample; a prediction model is constructed, the prediction model is trained in a data enhancement mode according to the input sequence samples, and in the training process, a loss function of the prediction model is a dynamic weighted loss function; the prediction model is a deep learning model representing a traffic flow sequential relationship; according to the method, the traffic data are obtained, the time sequence characteristics corresponding to the traffic data are predicted through the trained prediction model, the predicted traffic flow state is obtained, and accurate traffic flow state estimation can be achieved.

The present invention is a parameter estimation method for compartment model based on physics-informed neural networks. Starting from a physical model, the method extracts information from an AIF and a small amount of measurement data to obtain kinetic parameters, thereby greatly improving the scanning efficiency of a measuring instrument, and reducing occurrence of inaccurate estimation results due to patient movement. In addition, the present invention has the robustness to AIF noise and measurement data noise, and can flexibly arrange the time of data acquisition, reduce an error of inaccurate estimation caused by long time 10 acquisition and the patient movement, and improve the efficiency of data acquisition of the instrument. Experimental results show that the present invention is more stable and has less errors. Meanwhile, the present invention does not require the setup of training datasets, and is superior to an end-to-end supervised reconstruction method U-net network with fewer samples.

The invention provides a rare earth element content quantitative estimation method and device, equipment and a storage medium. The method relates to the technical field of hyperspectrum, machine learning and quantitative estimation. The method comprises the following steps: acquiring hyperspectral data of each sample, eliminating gross error points, performing data dimension reduction and K-means clustering analysis to obtain effective spectral data of each sample, performing denoising processing, averaging to obtain an average spectral curve, and extracting spectral characteristics; analyzing the correlation between the characteristic data of each wave band and the element content by utilizing Pearson correlation, selecting a high-correlation wave band range, carrying out importance test on the high-correlation wave band range by utilizing a random forest, and screening out a characteristic wave band corresponding to each element; and constructing a data set by using the characteristic wave bands corresponding to the elements and the chemical test contents of the elements, and training and evaluating the machine learning model based on the data set to obtain a quantitative estimation model. The rare earth element content can be quickly scanned and evaluated in time.

The invention discloses a generator state estimation method and system considering noise and parameter uncertainty constraints. The method comprises the following steps: acquiring model parameters and dynamic state vectors of a generator, and establishing augmented state vectors; performing unscented transformation on the augmented state vector to obtain a particle set; improving to obtain robust mixed Kalman particle filtering, and in the process of performing unscented Kalman filtering on an augmented state vector, taking correlation entropy maximization of a measurement information sequence as a target function, and solving by adopting a fixed point iteration method to obtain a filtering gain; determining a filtering gain according to the updated state of the measurement information; robust mixed Kalman particle filtering is executed, physical constraints of model parameters serve as a feasible region, after resampling, projections of the model parameters in new-generation particles exceed the feasible region, the model parameters are set to be closest boundary points, and then resampling is conducted again; a weighted average value of the particle set is an optimal joint estimation value, a dynamic state estimation value and a model parameter identification result are separated, and reliable uncertainty quantization is provided for state and parameter estimation.

The invention provides a laser cutting parameter estimation and quality feature optimization method, and belongs to the technical field of laser cutting. The laser cutting parameter estimation method comprises the steps that machining parameters and quality features corresponding to the machining parameters are collected, a data set is constructed, and the data set is used for training a constructed machining quality prediction model; processing parameters input by a user are obtained, the processing parameters are input into the trained processing quality prediction model to obtain quality feature prediction values, the optimal quality feature prediction value is screened, and a corresponding optimal processing parameter combination is determined; and the cutting process is controlled according to the optimal machining parameter combination. Machining parameters are accurately optimized, cutting defects are reduced, and the laser cutting quality is improved; rapid pre-estimation and parameter optimization are achieved, trial and error are avoided, and the machining efficiency is improved; material waste and reworking are reduced, the equipment debugging time is shortened, and the production cost is reduced; the cutting process is ensured to be stable and reliable through virtual sample piece visualization, and a user can visually evaluate the cutting effect.

The invention provides a petroleum drilling geomechanical characteristic estimation method based on deep learning, and aims to solve the problems of high measurement cost and poor real-time performance of shear wave velocity Vs in traditional geomechanical analysis by fusing logging while drilling LWD data and real-time drilling engineering parameters. According to the method, a Transformer model is adopted to carry out depth correction on logging-while-drilling data, depth offset of a drill bit and a logging sensor is eliminated, and real-time drilling parameters such as torque T, bit pressure WOB and drilling speed ROP are combined and input into a multi-layer perceptron MLP network to predict the shear wave speed Vs. And further calculating key geomechanical parameters based on the predicted shear wave velocity Vs. In practical application, the method improves the shear wave velocity prediction accuracy to 97.2%, the mean absolute error MAE of the shear modulus is reduced from 0.186 to 0.059, and the bulk modulus is reduced from 0.189 to 0.040. The method can be used for outputting stratum elastic parameters, optimizing bit pressure, pre-warning well wall instability and adjusting a well track, well drilling safety and efficiency are improved, and meanwhile dependence on an expensive well logging technology is reduced.