104 results about "Random parameters" patented technology

The invention discloses a smart home energy management method based on deep countermeasure reverse reinforcement learning. The method comprises the following steps: (1) modeling a smart home energy cost minimization problem and designing an environment state and an action corresponding to a Markov decision process; (2) constructing n groups of random parameter historical data based on a group of random parameter historical data and a rolling generation method; (3) solving the minimization problem by using n groups of random parameter historical data and an optimization algorithm and obtaining n expert tracks; (4) training the reward network by the discriminator based on the expert trajectory and the trajectory generated by the generator agent; under the guidance of the reward network, training a generator agent by using an experience tuple and a near-end strategy optimization algorithm; (5) repeating the step (4) until an intelligent agent strategy with stable training performance is obtained; and (6) deploying the intelligent agent strategy obtained by training in an actual environment for operation. Compared with an existing method, the method can effectively reduce energy cost and improve user comfort.

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 relates to the technical field of data simulation, in particular to a bearing fault simulation method and system. The method comprises the following steps: collecting multi-modal bearing data so as to construct a bearing distributed edge data set; extracting contact power data of the bearing distributed edge data set, and calculating a bearing rigidity change curve according to the contact power data; carrying out random parameter modeling based on the bearing distributed edge data set, and carrying out physical modeling benchmark reference on the bearing rigidity change curve to obtain a bearing fault physical-data hybrid model; therefore, by integrating multi-modal data acquisition, physical-data hybrid modeling and a dynamic hyper-parameter adjustment mechanism, the defects of a traditional bearing fault diagnosis method in the aspects of accuracy and real-time performance are overcome, and the fault prediction and early warning precision and the response speed are improved.

The invention discloses a tunnel concrete ground penetrating radar forward modeling data automatic generation method and system, and relates to the technical field of radar detection. The problems of lack of data sets, low modeling efficiency and insufficient signal generalization ability in the prior art are solved. According to the method, automatic tunnel concrete internal defect model generation and radar signal forward modeling simulation based on a finite difference time domain (FDTD) method are combined, various concrete structure defect models are automatically constructed through a random parameter generator according to an electromagnetic parameter range input by a user, and visual preview and verification functions are provided; and the reasonability of the model is ensured. According to the method, diversified ground penetrating radar simulation B-scan data sets can be efficiently generated, the method has clutter suppression and envelope enhancement functions at the same time, the signal quality is optimized through the filtering and adaptive gain technology, and the interpretability of images is improved. According to the method, reliable data support is provided for subsequent deep learning model training.

The invention provides a parallel multi-target random parameter optimization method and device.The parallel multi-target random parameter optimization method comprises the steps that the number of nodes of a parallel computing cluster is configured, and a random seed sequence is generated; carrying out structure loading and compiling on the to-be-optimized model, and obtaining global configuration of the to-be-optimized model; sending the global configuration to all processes, generating an initial parameter population meeting the constraint of the global configuration by each process based on the random seed, and performing serial evaluation to obtain a target function value; independently iteratively executing shuffling complex evolution by each process until a convergence condition is met; each process outputs the respective optimal parameter vector and the corresponding objective function value to a root process, and a global optimal solution is determined through the root process. By means of the method and device, the accuracy and efficiency of parameter optimization can be remarkably improved, and the problem that the model optimization effect is poor in the prior art is solved.

The invention provides a model training method and a model training system based on reinforcement learning, and belongs to the technical field of intelligence. The method comprises the steps of performing fixed-area parameter combination on a parameter set of a to-be-trained model in a delimiting range of each parameter to obtain an initial parameter combination set; constructing a performance index-oriented performance evaluation model according to the data set and the initial parameter combination set; constructing a perception response model of reinforcement learning based on the performance evaluation model to carry out random parameter replacement iteration on each parameter in the reference parameter combination so as to determine an optimal performance parameter combination capable of generating a maximum reward value; and training the to-be-trained model by adopting the optimal performance parameter combination, and iterating the performance evaluation model to obtain an optimization target model. According to the model training method based on reinforcement learning, parameter combination setting can be carried out on models in batches, the process of completing model training and evaluation in an automatic mode is achieved, the optimal parameter combination is sought, and therefore an optimization model is output.

The invention discloses a carbon dioxide geological sequestration quantity prediction method, system and device and a storage medium, and the method comprises the steps: obtaining a mathematical expression of carbon dioxide sequestration capacity through combining a coupling model of a flow field, a mechanical field and a thermal field with random parameters, and introducing a leakage risk coefficient; taking the weighted sum of the expected value of the storage capacity and the variance as an optimization objective function, and according to the mathematical expression of the carbon dioxide storage capacity, carrying out optimal solution solving on the carbon dioxide storage capacity to obtain an optimal storage strategy; performing spatial discretization on the control equation by adopting a finite element method, discretizing time by adopting a semi-implicit difference method, discretizing random variables by combining a random Galkin method, and solving a coupling model of a flow field, a mechanical field and a thermal field to obtain a prediction result of the carbon dioxide sequestration amount; and according to a prediction result of the carbon dioxide sequestration amount, outputting an expected value and a variance of the sequestration capacity, and generating a sequestration amount distribution diagram, a risk assessment diagram and a sensitivity analysis diagram.

The invention provides a glue joint insulation joint fatigue damage evaluation method and system considering random parameters, and relates to the technical field of rail vehicle wheel-rail contact analysis, and the method comprises the steps: obtaining first information; establishing a dynamic contact model of the wheel and the cementing insulation joint according to the first information, and performing simulation to obtain a dynamic response parameter in the contact process of the wheel and the track; performing fatigue damage analysis according to the dynamic response parameters to obtain fatigue damage parameters; performing fatigue life prediction according to the fatigue damage parameter to obtain a fatigue life prediction result; performing sensitivity analysis according to the fatigue life prediction result to obtain a sensitivity analysis result; and calculating the failure probability of the glue joint insulation joint according to the sensitivity analysis result, and evaluating the reliability of the glue joint insulation joint. According to the method, the dynamic contact model of the wheel and the cementing insulation joint is established by using the finite element method and simulation is carried out, so that high-precision wheel-rail contact dynamic response parameters are directly obtained.

The invention relates to the technical field of slope monitoring, and discloses a slope stability probability assessment method and device, and the method comprises the steps: carrying out the sensitivity analysis processing of the rock and soil parameters of a slope, and obtaining m sensitive relation curves; slope change mean values corresponding to the m sensitive relation curves are calculated and sorted, and k random rock-soil body parameters are determined; constructing n groups of samples containing k random rock-soil body parameters; performing finite element simulation on the n groups of samples to obtain n slope stability coefficient simulation results; analyzing the sample group proportion frequencies corresponding to the stable and unstable states of the slope; and calculating the slope stability level failure probability and reliability corresponding to the sample group by adopting a Monte Carlo method. According to the method, the random finite element probability evaluation method is adopted, the physical and mechanical parameters of the rock-soil body are regarded as random variables, the stability state of the slope under different random parameter combinations is analyzed through finite element numerical simulation, the actual stability condition of the slope can be reflected more truly, and the evaluation accuracy is improved.

The invention discloses a road driving suitability evaluation method based on an XGBoost-SHAP and a random parameter multivariate Logit model, and relates to the field of road driving suitability evaluation analysis, and the method specifically comprises the following steps: S1, employing front automobile data recorder data and IMU data in an automatic driving automobile data set; s2, establishing a quantitative model of road environment aesthetics, and extracting related characteristic variables from four aspects of naturalness, vividness, diversity and uniformity; s3, an explainable machine learning method combining XGBoost and SHAP is adopted, occurrence of dangerous driving behaviors serves as a dependent variable, road environment aesthetics features serve as independent variables, a road driving suitability RRAV evaluation model of the automatic driving automobile is established, and the relation between road environment aesthetics and dangerous driving behavior RDBAV of the automatic driving automobile is researched; and S4, researching the influence of the random parameter heterogeneity on the RDBAV in the road driving suitability evaluation model by adopting a random parameter multivariate Logit model considering the mean value and variance heterogeneity.

The invention discloses a dynamic global sensitivity analysis method for a flap mechanism based on Vine Copula. The method comprises the following steps: firstly, performing rigid-flexible coupling simulation on a movement mechanism, identifying a stress dangerous area, a deformation dangerous area and a clamping stagnation dangerous area of the movement mechanism through a stress distribution condition, and providing a new structure failure criterion according to a dynamic multi-parameter coupling idea, namely a time-varying stress-strain-driving force combined criterion; random parameters are definitely input according to the motion characteristics of the flap mechanism; secondly, establishing a time-varying correlation model considering autocorrelation and cross-correlation based on a Vine Copula theory and a random process model, generating a condition sample by using the model, and generating a required input random variable through inverse transformation; dividing the dynamic sample into a plurality of subintervals by using a space division method, and performing Sobol index solving, so as to solve the importance measure of each input parameter in the movement process of the flap mechanism;

The invention discloses a steel production operation plan generation method and device based on reinforcement learning. According to the method, a steel production flexible operation process is converted into a partially observable Markov decision process system; carrying out representation processing on the observation space through an attention mechanism to obtain an observation space representation; performing representation processing on the action space through a heuristic decision rule to obtain action space representation; constructing a reward function according to the total electricity utilization cost data; constructing a penalty function according to a task execution constraint of steel production and an intermediate product transportation time constraint; constructing a plurality of deep neural network models with random parameters, performing iterative updating until a set number of iterations is reached, and selecting the model with the highest total return as a target deep neural network model; and generating a steel production operation plan through the target deep neural network model. According to the method, the system observation space dimension and the system action space dimension can be reduced, the search space is effectively expanded, and the model training efficiency is improved.

The invention discloses an AI model parameter initialization method based on model parameter and structure multi-modal fusion, and belongs to the technical field of artificial intelligence. The method comprises the following steps: firstly, collecting historical pre-training model data of a cross-model architecture and a cross-data set, processing model parameters into a token sequence, training a Transform codec, converting a model structure into a graph, and training GAT to extract structural features; a multi-modal feature data set is constructed after a related network is frozen, structural features serve as core conditions, a conditional diffusion model DDPM is trained through AdaLN modulation and residual module coupling, and multi-modal feature fusion of'parameter feature-structural feature 'is established. In the reasoning stage, the structural features of the unknown model are extracted, the random parameters of the unknown model are combined with the submerged space shape determined by the encoder, sampling is conducted through the conditional diffusion model, anti-token processing is conducted through the decoder, and adaptive initialized model parameters are generated for the unknown model. According to the method, cross-model structure high-quality parameter initialization is realized, the model training time is shortened, and the large model training requirement is met.

The invention discloses an automatic driving vehicle accident cause analysis method. The method comprises the steps of obtaining a data set of an automatic driving vehicle accident report; pre-processing the data set, wherein the pre-processing comprises abnormal value cleaning, error data correction and missing data repair; constructing an explanation variable system of an automatic driving vehicle accident, and carrying out statistical description on accident characteristics; an accident cause analysis model is provided by integrating existing automatic driving vehicle accident research data and conclusions based on a random parameter Logit method for solving a heterogeneity problem; based on priori knowledge and an analysis result, designing a random parametric variable, and constructing an RPL model: sampling a probability density function for multiple times by adopting a Monte Carlo method, and taking a simulated probability mean value as an integral approximate solution; and the analysis of the variable random parameter effect in the automatic driving vehicle accident is realized through the RPL model. According to the invention, the cause explanation of the heterogeneity of the important characteristics and influence factors of the accident of the automatic driving vehicle is realized.

The invention discloses a vehicle-rail-bridge system random vibration analysis method based on interlayer fatigue damage, a terminal, a medium and a program product, and the method comprises the steps: constructing random parameter vectors which comprise a vehicle random parameter vector, an interlayer fatigue damage random parameter vector and a rail irregularity random parameter vector; performing random parameter vector point selection based on a multi-distribution point selection method to obtain a random parameter point set; constructing a train-rail-bridge system space random vibration analysis model; and substituting the random parameter point set into the train-track-bridge system space random vibration analysis model for simulation, and solving by combining a numerical integration method and a probability density evolution method to obtain train-track-bridge system vibration response under random interlayer fatigue damage. And meanwhile, multiple random factors such as random fatigue damage of the CA mortar layer and random irregularity of the track are considered, so that the accuracy and reliability of vehicle-track-bridge dynamic response analysis are remarkably improved.

The invention discloses a travel destination prediction method and system based on a Logit model, and belongs to the technical field of travel destination prediction.The travel destination prediction method comprises the steps that a spatial-temporal characteristic matrix containing a POI type, a traffic state and a user portrait is constructed based on multi-modal data, a nested mixed Logit model is introduced, travel destination options are divided into a plurality of logic groups through a nested structure, and the POI type, the traffic state and the user portrait are predicted; relevance of options in a group is quantified through a containing value and an elastic coefficient, so that a real selection behavior is reflected more accurately, the fitting ability of the model to a complex decision path is further enhanced through an upper and lower layer probability separation design, and meanwhile individual preference differences are captured through random parameters, so that the algorithm is more accurate. Parameters of the mixed Logit model are dynamically updated through Bayesian maximum likelihood estimation, and a Bayesian framework corrects fixed effect parameters and random parameters in combination with new observation data in each iteration by introducing a parameter prior distribution and posteriori updating mechanism.

The invention relates to the technical field of cold chain transportation environment control, and discloses a temperature and humidity control system for cold chain transportation, and the system comprises an environment modeling module which is used for constructing a heat conduction model of a cold chain space based on a random field theory and a finite element method, and outputting a stiffness matrix containing random parameters; the self-adaptive sensing network module is used for receiving the characteristic information of the stiffness matrix, dynamically optimizing sensor distribution positions, collecting multi-node temperature and humidity data and adding space-time labels; and the distributed data processing module is used for receiving the sensing data. According to the invention, through random field theoretical modeling and parameter-state joint estimation, the temperature field prediction precision is significantly improved and the system environment adaptability is enhanced; a distributed sensing network architecture and a heterogeneous calculation acceleration technology are combined, so that the cooperative control with optimal energy efficiency is realized while millisecond-level real-time response is ensured, and the performance bottleneck of a traditional cold chain temperature control system is comprehensively broken through.

The application discloses a time-varying reliability topology optimization method for a continuum structure, and comprises the following steps: constructing a time-varying random parameter initial model of the continuum structure based on continuum structure information; constructing a time-varying reliability topology optimization mathematical model according to the time-varying random parameter initial model; obtaining displacement, velocity and acceleration responses of the continuum structure based on the time-varying reliability topology optimization mathematical model; obtaining sensitivity information about random variables according to the displacement, velocity and acceleration responses; obtaining a global time-varying reliability index based on the sensitivity information; establishing a derivative relationship between an objective function and constraint conditions and design variables according to the global time-varying reliability index and an adjoint vector, and obtaining gradient information according to the derivative relationship; and optimizing the continuum structure according to the gradient information. The application realizes high-precision and high-efficiency reliability design of the continuum structure under a dynamic uncertain environment.

The invention relates to a traffic flow speed estimation method based on a Gaussian mixture model and hierarchical Bayesian, and belongs to the technical field of intelligent traffic systems. The method comprises four steps of data preparation and preprocessing, traffic state clustering division, hierarchical Bayesian random parameter model construction, and model verification and performance evaluation. Through deep fusion of the Gaussian mixture model and the hierarchical Bayesian model, data-driven traffic state identification and random parameter estimation are realized, traffic flow heterogeneity can be effectively described, model parameter explosion is avoided, and model prediction precision and generalization ability are improved.

The present application relates to a kind of accident economic loss influence factor analysis method and system, in method part, first based on GB2 model, establish multiple stochastic parameter model, then using LOOIC value carries out effect comparison, selects suitable stochastic parameter model and obtains the positive influence or negative influence of various explanatory variables to accident economic loss;Finally, based on sensitivity analysis, the influence degree of explanatory variable to accident economic loss is excavated.Through the scheme of the present application, an objective, comprehensive, accurate analysis method for excavating accident economic loss influence factor is provided, and the application range is wider, and the influence of related factors on the economic loss caused by accident is better represented.

The invention specifically discloses an ion thruster acceleration grid probability life evaluation method based on coupling competitive failure, and relates to the technical field of aerospace propulsion system life evaluation. The method comprises the following steps: firstly, obtaining deterministic parameters and random parameters of a thruster acceleration grid assembly; then based on an ion sputtering corrosion theory, considering material loss caused by charge exchange ion bombardment of the acceleration gate, and establishing a coupling corrosion model of the thickness and aperture of the acceleration gate; thirdly, constructing a competitive failure model, and realizing numerical solution of competitive failure life by dynamically iterating a corrosion state and a failure criterion; and finally, independently repeated random sampling is carried out based on probability distribution of random parameters, the random sampling is substituted into the competitive failure model to obtain an acceleration grid simulation life sample set, and statistical analysis is carried out on the life sample set to obtain a probability life evaluation result. The method solves the problem of errors caused by independent modeling of structural failure and electronic reflux failure in the prior art, and is helpful for reducing the system operation risk and improving the reliability of a spacecraft.

The application provides a turbine blade thermal barrier coating force-thermal coupling reliability evaluation method, which comprises the following steps: measuring and constructing a density distribution function of a thermal barrier coating microstructure and thermodynamic parameters, completing macroscopic complex turbine blade thermal barrier coating temperature field and strain field simulation, constructing a Monte Carlo sample space of random parameters at different positions of the blade, establishing a micro-scale force-thermal coupling finite element model and obtaining a life data set, building and training a deep learning agent model, and completing turbine blade thermal barrier coating reliability evaluation and sensitivity analysis. The reliability evaluation method provided by the application comprehensively considers the influence of the thermal barrier coating microstructure factor and the force-thermal coupling failure mechanism, solves the problems of high dimension, high nonlinear calculation cost and the like in the reliability evaluation of the complex blade thermal barrier coating, improves the accuracy and efficiency of the reliability evaluation, and provides an evaluation means for the safe application and optimized design of the thermal barrier coating.

The present invention relates to a method for predicting the stochastic response of a vehicle-bridge system based on a temporal convolutional network and a Gaussian process, belonging to the field of stochastic response prediction of VBI systems. The method includes: establishing a vehicle-bridge interaction model; obtaining stochastic dynamic response samples of the vehicle and the bridge; fusing the Gaussian process and the temporal convolutional network to establish a GP-TCN network model, in which the weights and biases between convolutional layers are set as random parameters, and the positions of the random parameters are set in the first dilated convolutional layer of each residual connection block; using the track irregularity as the input of the GP-TCN model and the dynamic responses of the vehicle and the bridge as the output of the GP-TCN model, and training the model with the sample data obtained by numerical simulation; using the trained GP-TCN model to predict the stochastic dynamic response of the vehicle-bridge interaction system. The present invention can accurately evaluate the dynamic responses of the VBI system in the time domain and the frequency domain.

This application discloses a method for calculating the thrust envelope of a solid rocket motor based on servo uncertainty, relating to the field of solid rocket motor technology. This method focuses on the gas valve servo system of a throat-operated solid rocket motor, performing uncertainty modeling of the motor drive system and transmission system, analyzing the main influencing parameters of servo system uncertainty, including transmission clearance, eccentric shaft friction coefficient, and other uncertainty parameters and their probability distributions; generating random parameter samples based on the uncertainty model, and using the servo system control model to solve for the actual throat displacement response under random samples, obtaining the engine thrust time history under single-valve and multi-valve cooperative working modes; and fitting the thrust uncertainty envelope by calculating the mean and standard deviation of the thrust response through Monte Carlo simulation. This application quantitatively characterizes the degree of influence of internal factors of the servo mechanism on thrust performance, providing a reference for ensuring precise thrust control of solid rocket motors.

The invention discloses a target export function parameter generation method and device, electronic equipment and a storage medium. The method comprises the following steps: determining an initial parameter structure through an assembly instruction of a target dynamic link library; generating random parameter data based on the initial parameter structure; recording an instruction coverage rate in a process of simulating and executing an export function based on the random parameter data; determining target parameter data of which the instruction coverage rate meets a preset condition; executing an optimization operation based on the target parameter data to determine a parameter value distribution range used for correcting the initial parameter structure; and correcting the initial parameter structure based on the parameter value distribution range, and generating a target derivation function parameter. A dynamic execution process combining static analysis and optimization operation is realized, the problems that effective input is difficult to generate and the instruction coverage rate is low in unsigned information DLL parameter identification are solved, and efficient and accurate identification of the derived function parameter structure is realized.

The invention relates to a stochastic simulation method considering dimensional deviation and concrete material variability, and belongs to the field of concrete structure analysis. According to the method, accurate simulation of the mechanical properties of the concrete structure under the influence of construction errors and material randomness is realized by determining random factors, establishing a random field matrix, carrying out sample transformation, establishing random models in batches, solving in batches and carrying out post-processing. A random simulation result is used as an output parameter, a random sample is used as an input parameter, and a PRRM-EPR algorithm is adopted to establish a machine learning prediction model. Compared with a traditional method, the method has the advantages that the generation quality of the random sample is improved, the calculation cost is reduced, the randomness in actual engineering can be better reflected, and the established prediction model can directly predict the mechanical response (displacement, load and the like) of the component through actually measured random parameters. The method is suitable for design, evaluation and optimization of a concrete structure, and particularly has a remarkable effect on improvement of structural safety.

The invention discloses a time-varying thermal failure probability estimation method and system for a deep groove ball bearing, and belongs to the field of bearing design. The method comprises the following steps: constructing a basic parameter model containing a mixed random parameter vector and a limit state function; establishing a transient thermal network model to calculate time-varying temperature response; constructing an agent model to replace high-time-consumption heat generation rate calculation; a self-adaptive time node sampling strategy is adopted, a proxy model and a thermal network model are combined to process large-scale samples, and the time-varying thermal failure probability is estimated. The system comprises a basic parameter modeling module, a transient thermal network simulation module, an agent model construction module and a self-adaptive probability evaluation module which correspond to the system. According to the method, the time-varying and random dual uncertainty is fused, and the proxy model and the adaptive sampling strategy are utilized, so that the defects of conservative property of a traditional static evaluation method and low efficiency of a traditional probability simulation method are effectively overcome, and accurate evaluation of the thermal behavior of the bearing under the dynamic working condition and remarkable improvement of probability calculation efficiency are realized.

The invention belongs to the field of data processing, and relates to an improved estimation method for distributed random parameters under the condition of probability density overlapping, which comprises the following steps of: 1) acquiring kinetic parameters of an engineering structure; the kinetic parameters comprise structural mass, rotational inertia and running speed; 2) carrying out distributed random parameter estimation on the kinetic parameters obtained in the step 1), and improving the accuracy of the distribution probability density of the kinetic parameters under the condition of probability density overlapping; and 3) carrying out stability analysis on the engineering structure based on a result obtained in the step 2), wherein the stability analysis mode of the engineering structure is to predict or evaluate the operation state of the engineering structure. According to the improved estimation method for the distributed random parameters under the probability density overlapping condition, estimation errors can be reduced, and the data processing accuracy can be improved.

The invention discloses a degraded equipment service life prediction method based on a two-stage Wiener process, and relates to the technical field of equipment service life prediction, and the method comprises the steps: carrying out the modeling of the two-stage Wiener process; determining parameter Wiener process life prediction in two stages; predicting the life of the two-stage random parameter Wiener process; performing two-stage model parameter maximum likelihood estimation; training and optimizing the prediction model; according to the method, a novel two-stage life prediction model is constructed, the actual condition of equipment is combined, the randomness of a drift coefficient and the randomness of a failure threshold value are considered, and expressions of life prediction and residual life prediction are deduced; meanwhile, off-line identification and on-line updating of the two-stage life prediction model are realized, and the accuracy of life prediction is improved.

The invention provides a geological analysis parameter protection method and system based on logarithm mapping, and relates to the technical field of geological exploration data protection, and the method comprises the steps: obtaining an original R parameter, building a linear mapping relation g = aR + b, building logarithm transformation mapping based on the linear mapping relation, converting the original parameter into a transformed parameter value, and obtaining a parameter value; and the transformed parameter values are provided for the data processing party for geological exploration operation, and the data owner recovers the original parameters through an inverse transformation formula after receiving the data obtained by exploration of the data processing party. According to the method, through logarithm transformation, an exploration company cannot identify the stratum type through conventional linear fitting, and sensitive geological information is effectively protected; meanwhile, irreversibility of the transformation process is ensured by adopting random parameters, and a data owner can completely recover original geological parameters through an inverse transformation formula; on the premise of protecting the data security, the normal exploration operation flow and the data processing precision are not influenced.