122 results about "Bayes' theorem" patented technology

The invention discloses a multi-source environment monitoring data fusion method based on adaptive Bayesian reasoning. The method comprises the following steps: S1, collecting an environment monitoring data set; s2, obtaining a preprocessed environment monitoring data set; s3, carrying out uncertainty evaluation on the preprocessed environment monitoring data set; s4, forming an initial prior probability; s5, calculating the posterior probability distribution of each environment monitoring data source through the Bayesian theorem, and dynamically updating the weight of each data source in the environment monitoring data fusion process according to the posterior probability distribution; s6, performing weighted fusion on each environment monitoring data source according to the dynamically updated weight to form fused environment monitoring data; and S7, carrying out post-processing on the fused environment monitoring data. According to the invention, the data fusion process can adapt to environmental changes in real time, so that the accuracy of data fusion is improved.

The invention discloses a bridge network anti-seismic performance rapid evaluation method based on capability spectrum analysis. The method comprises the following steps: firstly, carrying out finite element modeling on bridges in a regional bridge network needing to be analyzed by using OpenSees, extracting piers, and carrying out vulnerability analysis on the piers; a bridge vulnerability curve is obtained based on a capacity spectrum method, then the failure probability of the bridge in a certain damage state serves as a post-earthquake damage index, and the bridge failure probability is replaced by the post-earthquake failure probability of the corresponding road section; after the post-earthquake failure probability of each road section exists, all paths between a starting point and an ending point, namely, to-be-researched OD pairs in a network area are analyzed, the passing probability of each path after the earthquake is calculated based on the Bayesian theorem and an event independence calculation method, all the paths under the OD pairs are sorted according to the passing probability, and therefore the optimal passing path is obtained; and the path with small passing probability is avoided. According to the invention, a scientific basis is provided for regional bridge network anti-seismic planning and emergency management.

The invention discloses an online intelligent monitoring and analyzing system for wear of a circuit breaker contact, which relates to the field of contact wear monitoring and comprises a visual module, an acoustic module, a current module, an acquisition module, an analysis module, an alarm module, a processing module and a display module. The method comprises the following steps of: analyzing and establishing a quantum driving feature selection model; constructing a contact motion mechanical control model; setting parameter prior distribution; fusing a likelihood function and prior by utilizing a Bayesian theorem; a vibration chaos non-linear loss value is calculated based on recurrence plot line segment probability distribution analysis, a jump diffusion model is constructed, the number of jumps is counted to calculate Poisson intensity, corresponding display and alarm are carried out, the accuracy of contact abrasion state evaluation is guaranteed, and early abrasion abnormity is found in time.

The invention relates to the technical field of accident responsibility judgment, solves the problem that intelligent driving accident analysis and real-time responsibility judgment cannot be carried out in the prior art, and provides an accident responsibility judgment method, device and equipment in an intelligent driving scene and a storage medium. The method comprises the following steps: analyzing related parameters of an accident in an intelligent driving scene, and determining a plurality of key variables in the related parameters and a causal network corresponding to each key variable; according to a pre-collected traffic database and the causal network, the occurrence probability of each key variable is quantified, and a prior knowledge base related to an intelligent driving accident is determined; and according to the real-time data of the intelligent driving accident and the prior knowledge base, in combination with the Bayesian theorem, accident causes are analyzed, and an accident responsible party is determined. According to the method, man-machine responsibility division of the intelligent driving accident can be realized, and the emergency processing capability and the judgment efficiency of the intelligent driving system after the accident are remarkably improved.

The invention discloses a Bayesian traffic density estimation method based on data driving in a mixed traffic environment, and the method comprises the steps: S1, building a probability relation model between traffic density and vehicle travel time, and between a vehicle and a front vehicle space-time region area based on measurement data; s2, setting prior probability distribution for the vehicle travel time and the model noise variance; s3, calculating joint posteriori distribution of the vehicle travel time and the model noise variance by using the Bayesian theorem, and performing marginalization processing to obtain marginal posteriori distribution of the vehicle travel time; and S4, probability estimation is carried out on the traffic density of the unobserved space-time region through posterior prediction distribution. The technical problem of traffic density estimation in the mixed traffic environment is effectively solved by establishing a complete Bayesian probability framework, and the estimation precision is remarkably improved by adopting the steps of prior distribution setting, posterior distribution calculation, marginalization processing and the like based on CAV dynamic data acquisition.

The invention relates to an InSAR-based underground excavation space geometric parameter inversion method and system. The method comprises the steps of obtaining a ground surface deformation field of a research area where an underground excavation space is located based on an InSAR technology; underground space positioning prior information is collected according to the earth surface deformation field; geological prior information of the underground space is collected; fusing to obtain a priori information set of underground space geometric parameters; according to the prior information set, constructing a function relationship between InSAR observation deformation and ground surface three-dimensional deformation induced by underground space through a probability integral method; establishing a likelihood function based on the InSAR observation data and the earth surface deformation data; inverting the underground space geometric parameters based on the Bayesian theorem and a likelihood function to obtain underground space geometric parameter posterior distribution; and solving the underground space geometric parameter posterior distribution by using a differential evolution adaptive Metropolis algorithm to obtain an inversion result. And the accuracy and precision of the inversion result are improved.

The invention relates to the technical field of insurance pushing, in particular to an insurance product intelligent pushing mode based on a deep neural network, which comprises a user portrait construction module, a causal reasoning module, a personalized pushing module and a feedback optimization module, and is characterized in that a user portrait is constructed through extracted features and a modeling result, and the user portrait is pushed to the user. The causal reasoning module is based on mass user data, determines a variable to be deduced and a conditional probability through the model establishment module, establishes a model for Bayesian reasoning, and calculates a posterior probability based on the Bayesian theorem through the calculation analysis module; and through the posterior probability, the client with the higher purchase intention for the insurance product can be analyzed and judged. According to the invention, on the basis of existing massive customer data, portrait customer characteristics and customer touch data are analyzed through customer purchase, and characteristics, demands and behavior preferences of customers are described through deep analysis, so that targeted sales strategies and high-quality services are provided for marketing personnel of a company.

The invention discloses a bridge support displacement online probability prediction method, which comprises the following steps: acquiring historical monitoring data of main beam temperature and support displacement, and introducing a group of false points to construct a probability prediction model based on a flow sparse Gaussian process, according to the approximate posterior distribution and the real posterior distribution, constructing a variational free energy boundary as a lower bound of a logarithmic marginal likelihood function, deriving an optimal variational free energy boundary and optimal approximate posterior distribution, and obtaining a specific expression of a probability prediction model; when new monitoring data is obtained, new monitoring data and old historical monitoring data are summarized in combination with new false points and old posterior distribution, new approximate posterior distribution is derived, new real posterior distribution is derived by using the Bayesian theorem, then model parameters and posterior distribution are updated online, and a new specific expression of the probability prediction model is obtained. According to the method, massive health monitoring flow data of the bridge is processed based on the flow sparse Gaussian process model, online probability prediction of bridge support displacement is achieved, and the method has high engineering application value.

The invention discloses an elastomer layer carbon fiber composite material damage positioning method based on elliptic probability fusion, which comprises the following steps: taking different sensors as excitation sources, and collecting Lamb wave signals of a plurality of sensing paths; performing continuous wavelet transform by using a complex Morlet wavelet, and extracting actually measured flight time; introducing a layered wave velocity correction model to optimize an elliptical orbit method, and calculating theoretical flight time and damage probability; introducing a dynamic short-axis optimization model to optimize a probability weighting method, and calculating a damage probability; generating prior distribution by using results calculated by an elliptical orbit method and a probability weighting method; constructing a likelihood function based on the difference between the actually measured flight time and the theoretical flight time; the posterior distribution of the damage position is obtained through the Bayesian theorem in combination with the prior distribution and the likelihood function; sampling the posterior distribution by using an adaptive MCMC algorithm, and generating a probability cloud picture of the damage position; and outputting a final imaging result of the damage position, and positioning the damage position.

The invention provides a biomedical causal relationship inference method and system based on a Bayesian knowledge graph, and relates to the technical field of biomedical data mining and artificial intelligence, and the method comprises the steps: carrying out the multi-source evidence fusion of biomedical data, and obtaining a structured triple containing Bayesian confidence; analyzing the triple by using priori knowledge and obtaining a conditional probability table through parameterized filling; carrying out posteriori updating by using the Bayesian theorem; and analyzing the updated knowledge graph state by using a graph neural network model to obtain an inference result. Wherein the Bayesian inference module is combined with the graph neural network model, the former provides priori knowledge with confidence, the latter provides a fine path dependency relationship, and the accuracy and robustness of inference are remarkably improved. According to the method, the problems of evidence isomerism fragmentation, causal inference subjectization and knowledge discovery inefficiency are solved, and intelligent and automatic inference of the causal relationship is realized.

The invention discloses an intelligent segmental arc correlation method based on a minimum optical admissible domain, belongs to the field of intelligent calculation optimization of space target monitoring, solves the problem of large calculation amount of segmental arc correlation, and comprises the following steps: constructing the minimum optical admissible domain by a first observation segmental arc; uniformly sampling in the minimum optical tolerance domain, performing orbit propagation on each sampling point under a J2 perturbation model to obtain theoretical optical observation data of a second observation arc section, and calculating an angle root-mean-square error of the second observation arc section to obtain an angle loss function; using a DBSCAN clustering algorithm to identify wave troughs; selecting an initial value of a simplex method in each wave trough to obtain an angle optimal solution; a probability loss function is obtained through Bayesian theorem calculation; substituting the angle optimal solution into a probability loss function, and obtaining a probability optimal solution by using an LM algorithm; a threshold value is set according to the probability loss function obeying chi-square distribution, and when the probability optimal solution is smaller than the threshold value, the two arc segments are associated successfully; according to the invention, the calculation cost is saved, and the space target observation accuracy is improved.

The invention provides an urban gas pipeline leakage detection method and system, and relates to the technical field of pipeline leakage localization, and the method comprises the steps: building a gas pipeline mathematical model between adjacent pressure regulating stations; discretizing the gas pipeline inlet and the gas pipeline outlet to obtain a discretized data model for describing the gas pipeline inlet and the gas pipeline outlet; actual signals are collected; de-noising correction is carried out on the actual signal; inputting the corrected actual flow into the discretization data model according to the position of the discrete node to which the actual flow belongs, and outputting model prediction pressure; calculating a pressure difference value between the model prediction pressure of the gas pipeline outlet and the corrected actual pressure; calculating an average pressure difference value by combining a dynamic time window determined based on the Bayesian theorem under the condition that the pressure difference value is greater than a preset difference value, entering the next step under the condition that the average pressure difference value is greater than the preset difference value, and otherwise, returning to collect the actual signal again; and determining the leakage position of the gas pipeline. And the gas pipeline monitoring efficiency and the leakage position positioning accuracy are improved.

The invention discloses a neural network architecture search method based on an existing knowledge reasoning generation structure and coefficient, and the method comprises the following steps: 1, extracting part information according to the features of a data set and reference expert knowledge, and constructing a hierarchical part structure; step 2, simplifying the hierarchical part structure; step 3, selecting a part of components in the simplified and hierarchical component structure, training through a neural network architecture, and obtaining related structure and path information; 4, according to the path information, reasoning is conducted in combination with a hierarchical component structure and the Bayesian theorem, and a beta value in a new structure is obtained through calculation; and 5, through a normal distribution function, generating a corresponding weight for each beta value in each layer of network, and completing architecture search in the neural network structure search model. According to the method, the calculation consumption is reduced, and the improvement of the search efficiency is paid attention to.

The invention discloses an occupation grid map construction method based on distance weighting, and belongs to the field of intelligent vehicle digital maps and high-precision positioning. The method comprises the following steps: acquiring pose data of an intelligent vehicle and sensing data of a surrounding environment; constructing a distance weight factor; generating an occupation grid map according to the obtained pose data of the intelligent vehicle and the perception data of the surrounding environment, dividing the occupation grid map based on the Bayesian theorem and a distance weight factor, and endowing each grid with state information; and dynamically updating the occupied grid map by using a double-end linked list structure. According to the method, the distance weight factor is constructed, and the grid map is divided through the Bayesian theorem and the distance weight factor, so that the obstacle state can be accurately expressed in a dynamic non-structural environment, and the problem of track noise is avoided.

A rock burst risk dynamic assessment method based on a Bayesian method comprises the steps that a stope working face is divided into M * M regular grids, and each grid serves as an independent assessment unit; collecting geological and mining data, selecting a plurality of influence factors based on historical data and expert knowledge, and quantifying the plurality of influence factors by adopting a static evaluation model; determining weights of a plurality of influence factors by using an analytic hierarchy process, and calculating an initial risk score of each evaluation unit by using a comprehensive index method; normalizing the initial dangerousness score into prior distribution by adopting an S-type function, and constructing an initial dangerousness prior probability field; a power law attenuation model is introduced to calculate influence weights among the evaluation units, and an influence weight matrix is constructed; real-time monitoring data are obtained through a micro-seismic monitoring system, the risk probability of each evaluation unit is updated in real time based on the Bayesian theorem, and dynamic evaluation and evaluation result output are achieved. According to the method, the accuracy and the real-time performance of rock burst risk assessment can be effectively improved.

The invention discloses an SRAF placement method based on a Bayesian model. The method comprises the steps that historical SRAF configuration parameters and photoetching simulation data are collected and preprocessed; based on the preprocessed data, using kernel density estimation to construct prior probability distribution; constructing a likelihood function of multiple photoetching result indexes in combination with a Hopins photoetching model; calculating approximate distribution of a posterior probability through variation inference by using the Bayesian theorem; and finally selecting an optimal SRAF configuration parameter according to the posterior distribution, and verifying the manufacturing feasibility of the process window and the mask. According to the method, historical prior knowledge and real-time photoetching data are organically integrated through the Bayesian model, and high-precision, high-efficiency and high-robustness optimization of the SRAF layout is realized by combining specific technical characteristics such as preprocessing, kernel density estimation, a Hopkinson physical model and variation approximation; the problems of complex rule base, time-consuming calculation and insufficient adaptability in the prior art are effectively solved.

The invention discloses a user energy storage auxiliary settlement method and system, and relates to the field of electricity markets and energy management, and the method comprises the steps: obtaining and preprocessing user side data; based on the user electricity consumption data, the user electricity consumption habit index data and the energy storage equipment use duration data, the user load fluctuation degree, the user electricity consumption fluctuation degree and the workday and weekend use duration ratio are obtained through calculation; acquiring power consumption time data of each time period, and outputting and correcting a power consumption predicted value of each time period based on the power consumption time data, the user load fluctuation degree, the user power consumption fluctuation degree and the workday to weekend use duration ratio in combination with a linear regression model and a Bayesian theorem; and on the basis of the corrected electricity consumption predicted value of each time period and the electricity price of each time period, calculating the energy storage settlement cost of the user. According to the invention, the final electric charge of the user can be predicted more accurately.

This invention relates to a method and system for optimizing the quenching of large-diameter bearings based on multi-model set membership estimation. The method includes: determining multiple state parameters of the target quenching measurement system; constructing a system model based on the fusion of multiple set membership estimation models using the state parameters and a fully symmetric multi-cell method, employing a Kalman filter; determining the predicted state equation and probability density function of each set membership estimation model; updating the probability density function of the predicted set of each set membership estimation model according to the dimension of the output matrix of the real-time quenching measurement system; calculating the predicted set of the system model using Bayes' theorem based on the probability density function of the predicted set of each set membership estimation model; and solving for the optimal gain matrix of the target quenching measurement system by minimizing the set radius based on the predicted set and error set of the system model. This invention improves the stability and robustness of the optimized control for quenching large-diameter bearings through multi-model set membership estimation and Bayes' theorem.

The present invention discloses an intelligent arc segment correlation method based on the minimum optical tolerance domain, belonging to the field of intelligent computing optimization for space target surveillance, which solves the problem of large computational volume of arc segment correlation, and includes: constructing a minimum optical tolerance domain from the first observation arc segment; uniformly sampling in the minimum optical tolerance domain, and propagating the orbit of each sampling point under the J2 perturbation model to obtain the theoretical optical observation data of the second observation arc segment and calculating the root mean square error of the angle of the second observation arc segment to obtain an angle loss function; using the DBSCAN clustering algorithm to identify the wave valleys; selecting the initial value of the simplex method in each wave valley to obtain the optimal angle solution; calculating the probability loss function by the Bayesian theorem; substituting the optimal angle solution into the probability loss function and using the LM algorithm to obtain the optimal probability solution; setting a threshold according to the chi-square distribution of the probability loss function, and when the optimal probability solution is less than the threshold, the two arc segments are successfully correlated; the present invention saves computational costs and improves the accuracy of space target observation.

The invention provides a user satisfaction prediction method and device and a medium, and relates to the technical field of computers. The method comprises the following steps: based on a first index, using a user satisfaction model obtained by training according to user complaint data in advance to obtain a first probability that a first user is not satisfied; based on a second index, obtaining a second user who has a poor quality event in the first users, and correcting the first probability of the second user by using the Bayesian theorem to obtain a second probability that the second user is not satisfied; and predicting an unsatisfied fourth user according to the first probability of the third user who does not generate the poor-quality event in the first user and the second probability of the second user. According to the method, the probability that the user is not satisfied according to the user complaint prediction is corrected through the Bayesian theorem according to the poor-quality event, so that user satisfaction evaluation errors caused by the fact that some users are not satisfied with commodities or services actually but do not complaint are avoided.

The invention discloses a tourism investment project risk assessment method and early warning system, and the method comprises the steps: S1, determining tourism investment project risk assessment indexes which comprise external risks, investment subject internal risks and project engineering risks; s2, based on the Bayesian theorem, constructing a risk assessment model used for calculating the occurrence probability of each risk index and the influence on the overall risk of the project, defining a prior probability for each risk index according to historical data or expert experience, continuously collecting latest data related to the risk assessment indexes, and according to the newly collected data, establishing a risk assessment model; using a Bayesian algorithm to update the posterior probability of each risk index; s3, comprehensively evaluating the posterior probability of each risk index, and calculating the overall risk level of the project; according to the method, the external risk, the internal risk of the investment subject and the project engineering risk are covered by defining the risk assessment indexes, and the comprehensive coverage of the risk points is ensured, so that the problem of incomplete risk assessment caused by missing important risk points can be reduced.

The invention provides a method for establishing a high-dimensional feature posterior probability estimator based on a sparse Bayesian method. The method comprises the following steps: (1) constructing a sparse Bayesian posterior probability estimation model; (2) model parameter representation based on the Bayesian theorem; (3) performing model parameter solving by using the sample data; and (4) realizing model sparsification based on automatic correlation confirmation. The invention designs a high-dimensional feature posterior probability estimator by using a sparse Bayesian method in order to solve the problem that the feature category affiliation probability is difficult to solve due to the fact that the high-dimensional feature probability distribution is difficult to estimate.

A multi-source heterogeneous data driven large rotating machinery fault diagnosis method, which first collects one-dimensional data composed of low-frequency vibration signals, medium-frequency vibration signals and temperature signals, and two-dimensional data composed of operation images at the same time, then fuses the one-dimensional data and two-dimensional data by Bayes theorem to obtain a diagnosis result, at the same time, obtains a diagnosis result B according to the low-frequency vibration signals and medium-frequency vibration signals, and obtains a diagnosis result C according to the temperature signals, then sums up the diagnosis results A, B and C according to the weighted summation method to obtain the final diagnosis result, if the final diagnosis result is greater than or equal to 0.5, it is judged that there is a fault, if it is less than 0.5, it is judged that there is no fault. The design not only monitors multiple signals, but also has good fault diagnosis effect.

The present invention provides a method and device for determining porosity by using logging interpretation and image recognition, which relates to the technical field of petroleum exploration and development, including: obtaining structural parameters of shale samples by using logging interpretation and image recognition respectively; the structural parameters include TOC content, mineral content and porosity data; determining a first linear relationship based on the first type of structural parameters obtained by logging interpretation, and determining a second linear relationship based on the second type of structural parameters obtained by image recognition; the first linear relationship and the second linear relationship respectively characterize the mapping relationship between the porosity data and TOC content and mineral content at the macro level and the micro level; combining the first linear relationship and the second linear relationship by using Bayesian theorem to obtain the target linear relationship corresponding to the porosity data. By obtaining logging data and scanning electron microscope images, the pore characteristics of shale are comprehensively analyzed from both macro and micro levels, thereby improving the accuracy and comprehensiveness of porosity calculation.

The present invention provides a method for disambiguating personal names based on multi-relationship deep retrieval text matching, which relates to the technical field of personal name disambiguation. The present invention obtains cross-modal data related to enterprises and people, performs data alignment and data fusion on the cross-modal data through an entity alignment algorithm to form a person-enterprise structured data set, generates semantic vectors by establishing a multi-relationship deep retrieval model based on a pre-trained language model, calculates semantic similarity according to the semantic vectors, and generates a personal embedding vector according to the co-occurrence frequency and spatio-temporal correlation features calculated from the person-enterprise structured data set. An anti-disambiguation recognition model is established based on an adversarial neural network, personal name disambiguation is performed according to the personal embedding vector, a structure update model is established through a graph attention network to update the graph structure in real time, the confidence levels of semantic similarity and co-occurrence frequency are calculated according to Bayes' theorem, and the weights of semantic similarity and co-occurrence frequency are updated according to the confidence levels.

This invention relates to the field of seismic data processing technology, specifically disclosing a method for removing strong reflection shielding in hidden river channels based on adaptive hybrid L0-L1 norm. The method includes: first, establishing an optimization objective function based on Bayes' theorem using hybrid L0-L1 norm; second, dynamically adjusting the L0-L1 norm weights using an adaptive weight function driven by the seismic signal, combined with reflection coefficient amplitude and residual information, enhancing sparsity constraints in strong reflection zones and reducing constraint strength in weak reflection zones; then, constructing a convex upper bound for the objective function using a minimization framework, and solving it iteratively in stages using an accelerated rapid iterative threshold shrinkage algorithm, while incorporating prior knowledge of seismic wave propagation laws and river channel deposition patterns to ensure the geological rationality of the solution. This invention solves the technical problems of traditional sparse processing methods, such as fixed parameters, lack of geological constraints, and inability to simultaneously address strong reflection suppression and weak signal protection, significantly improving the separation accuracy of strong reflections and the recovery rate of weak signals, effectively overcoming the strong reflection shielding effect.

The invention discloses a heading machine hob abnormal state early warning method and device. The method comprises the steps that real-time heading parameter data, hob vibration data, hob infrared video data, hob temperature data and rock slag image data are acquired; acquiring historical tunneling parameter data according to a specified mileage, inputting the historical tunneling parameter data and the real-time tunneling parameter data into a first hob damage early warning model, and outputting a first early warning result; inputting the hob vibration data into a second hob damage early warning model, and outputting a second early warning result; inputting the hob infrared video data and the hob temperature data into a third hob damage early warning model, and outputting a third early warning result; inputting the rock slag image data into a fourth hob damage early warning model, and outputting a fourth early warning result; and based on the Bayesian theorem, fusing the first early warning result, the second early warning result, the third early warning result and the fourth early warning result to obtain a hob state early warning result. The accuracy of early warning of the abnormal state of the hob can be improved.

The present application belongs to the field of software analysis, and specifically discloses a Bayesian verification method, system and device for reliability of discrete software, the method comprising: obtaining a discrete data set for reliability growth testing of discrete software; determining a reliability growth model of discrete software by combining the data set and a non-homogeneous Poisson process in a discrete time domain; estimating the probability of discrete software successfully executing a preset specified task again based on the reliability growth model; setting the prior distribution of reliability to a beta distribution, and estimating the parameters of the beta distribution based on the probability of successfully executing a preset specified task and the maximum entropy method; determining the posterior distribution of reliability based on the prior distribution and Bayes' theorem; determining the number of test cases required for reliability verification of discrete software under a preset tolerable number of failures based on the posterior distribution; and performing reliability verification based on the number of test cases and the tolerable number of failures. Through the present application, an effective reliability verification scheme for discrete software is proposed.

The invention discloses a Bayesian model averaging-based power prediction model evaluation method and a related device, and belongs to the technical field of power system prediction. The method comprises the following steps: constructing a candidate model set comprising a plurality of power prediction models; secondly, historical data are used for training all the models, the posterior model probability of each model is calculated based on the Bayesian theorem, the posterior model probability serves as the scientific weight of the model, and the goodness of fit and complexity of the model are considered in the weight at the same time; and finally, for a new prediction input, performing weighted average on the prediction distribution of each candidate model by taking the posterior probability as the weight to generate comprehensive probability prediction distribution. According to the method, the advantages and disadvantages of each candidate model are scientifically evaluated through the posterior probability, and a comprehensive and probabilistic prediction result is finally generated, so that the robustness and reliability of prediction are improved, and richer decision information is provided for power grid dispatching.

The present invention provides a network data asset attribution identification method and system based on Bayesian causal reasoning, and relates to the field of data processing technology. The method includes: obtaining a network data asset data set; using heuristic rules, preferentially selecting asset features with high significance and high confidence as preliminary nodes of the Bayesian network; constructing an undirected preliminary Bayesian network based on the preliminary nodes; adding the remaining asset features as new nodes to the undirected preliminary Bayesian network to form an undirected intermediate Bayesian network; based on the point-to-point conditional causality of the node pairs composed of two parent nodes in the potential causal structure, determining the direction of the undirected edge, and converting the undirected intermediate Bayesian network into a directed final Bayesian network; obtaining the network data asset to be identified; based on the Bayesian theorem, calculating the posterior probability of each category label under a given feature combination; taking the category with the largest posterior probability as the final category of the network data asset to be identified, and outputting it.