152 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 fast robust sparse Bayesian image reconstruction method based on generalized approximate message passing. The method comprises the following steps: S1, selecting an image data set; s2, setting the pixel sizes of all the images; s3, performing sparse preprocessing on each image; s4, establishing a sparse Bayesian model; s5, calculating a likelihood function, prior distribution of a sparse coefficient x and a marginal likelihood function; s6, calculating the posterior density of x according to the Bayesian theorem; s7, calculating a cost function; s8, iteratively updating the hyper-parameters by adopting a block coordinate descent method; s9, introducing a generalized approximate message passing algorithm to update a mean value and a variance; s10, introducing a damping factor to enhance convergence; s11, judging whether an iteration termination condition is met or not, if yes, outputting posteriori estimation of x, and if not, executing the step S8; and S12, performing inverse wavelet transform on the output x to obtain a reconstructed image. According to the method, reconstruction can be completed in a short time, the reconstruction error is low, and better reconstruction precision and reconstruction efficiency are achieved.

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 article storage cabinet intelligent management method and system based on the Internet of Things and artificial intelligence, and relates to the technical field of storage intelligent management, and the method comprises the steps: collecting the information of articles stored in an article storage cabinet, distributing RFID tags for the stored articles, and binding the information of the stored articles with two-dimensional codes; binding user information and recording interaction information between the user and the stored article; analyzing article demands of the article storage cabinet according to interaction information of the user and the stored articles for supplementation, and monitoring the validity period of the articles in the article storage cabinet; and displaying information of articles stored in the article storage cabinet in real time and recording the information in a database. The efficiency of the article storage cabinet for collecting the stored article information is improved, the Bayesian theorem is adopted for identity verification by registering the user identity and recording the transaction information of the user and the stored article to predict the article demand, the accuracy of user identity verification is effectively improved, and it is guaranteed that the number of articles in the article storage cabinet meets the demand.

The invention discloses a multi-energy load prediction and power distribution network planning method based on data driving. The method comprises the following steps of 1, collecting and sorting historical data and real-time data for load prediction and power distribution network planning; 2, constructing a model for predicting a multi-energy load based on GCN-LSTM deep learning by combining GCN and LSTM; 3, introducing the Bayesian theorem into the model for predicting the multi-energy load based on GCN-LSTM deep learning constructed in the step 2, and establishing a load prediction model; 4, establishing a modeling thought of power distribution network extension planning based on MDP, dividing a planning period into a plurality of stages, defining a current state and an optional action in each stage, and selecting an optimal decision for realizing a long-term planning target according to the current state and the optional action; and 5, on the basis of the step 4, constructing a power distribution network extension planning model based on the MDP, and obtaining the optimal action and the corresponding reward of each stage by solving the power distribution network extension planning model based on the MDP.

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 provides a cyanobacterial bloom prediction method and system based on satellite remote sensing and weather forecast. The method comprises the following steps: selecting an optimal meteorological factor according to different contributions of meteorological factors accumulated in 1-7 days to cyanobacterial bloom outbreak; taking the optimal meteorological factor and the ATBI wave band as prediction model input; and constructing a prediction model based on a Bayesian theorem principle. According to the scheme provided by the invention, a Chinese large lake cyanobacterial bloom short-term prediction model based on the Bayesian theorem can be constructed by taking a cyanobacterial bloom monitoring result of a large lake and meteorological factor data as main data sources, and pixel scale prediction of cyanobacterial bloom in multiple days in the future is realized.

The invention belongs to the technical field of equipment degradation prediction, and particularly discloses a degradation prediction method based on a random process and deep learning interaction, electronic equipment and a storage medium, and the method comprises the following steps: S1, constructing a neural network based on a multi-scale residual module, a bidirectional long-short-term memory network and a cross attention mechanism; s2, constructing a Wiener degradation model with a drift coefficient in normal distribution, wherein the Wiener degradation model is used for reflecting a current degradation state; s3, adopting a maximum likelihood estimation method to obtain a drift coefficient in the Wiener degradation model; s4, obtaining two parameters of drift coefficient normal distribution according to the Bayesian theorem; s5, performing interaction between the Wiener degradation model and the neural network, updating the two parameters in the step S4 by using data output by the neural network, and obtaining a drift coefficient through the step S3; and S6, setting a failure threshold value, and obtaining the residual life by using the neural network.

The invention relates to a method for eliminating an off-axis digital hologram zero-order item, which comprises the following steps of: acquiring hologram data of off-axis digital hologram, and acquiring frequency spectrum information through Fourier transform; pixel points with the intensity higher than a certain value in the frequency spectrum are selected as initial data objects, clustering categories of the initial data objects are divided through a K-means clustering method, and a clustering center is calculated. Taking the clustering center as an initial mean value of the GMM, calculating the covariance of each cluster, calculating the mixing coefficient of the GMM according to the proportion of the data points in each cluster, calculating the posterior probability that each data point belongs to each Gaussian distribution through the Bayesian theorem, and updating the GMM model to obtain a final clustering result. And eliminating the clustering result at the central position of the frequency spectrum to eliminate zero-order frequency spectrum information. Compared with the prior art, the method has the advantages of high accuracy, high universality and the like.

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.

Method for determining protection levels (7) during the journey of a vehicle with a GNSS-based localization system using a copula-based Bayesian framework, wherein a plurality of environment classes (10, 11) and a plurality of GNSS quality indicators (9) are predefined, and wherein for each environment class (10, 11) and each GNSS quality indicator (9) a copula model (15, 16, 17) is provided offline and stored in memory, comprising the following steps: a) Classifying the environment while driving based on the GNSS quality indicators (9) and determining to which environment class (10, 11) the environment belongs, b) Reading one of the copula models (15, 16, 17) corresponding to the determined environment class (10, 11) for the corresponding quality indicators (9) from memory, c) Extracting at least one likelihood function (1, 2, 3, 4, 12) from the read-out copula model (15, 16, 17) according to a quality indicator value (13), d) Determining a posterior distribution (6) for positional errors by multiplying the at least one likelihood function (1, 2, 3, 4, 12) based on Bayes' theorem with a prior distribution (5) for errors, and e) Determining Protection Levels (7) from the Posteriori Distribution (6) .

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.