55 results about "Bayesian formulation" patented technology

The invention discloses a shield muck modified slurry grouting reinforcement effect evaluation method based on multi-data fusion, and belongs to the field of shield construction grouting. The method comprises the steps that during field monitoring, a monitoring section is selected according to tunnel geology and structure characteristics, and a sensor is installed; acquiring data of soil pressure, displacement and pore water pressure, calculating corresponding change rate and gradient, and analyzing change trend. According to the numerical simulation, a three-dimensional model is established based on geological exploration and the like to determine parameters, shield propulsion and grouting reinforcement are simulated, and stratum deformation, soil stress and slurry diffusion and permeation data are obtained. And in the data fusion link, the two types of data are subjected to standardization processing, grouting reinforcement effect grades are divided to determine a prior probability, a probability distribution model is established for monitoring indexes, and a posterior probability is calculated through a Bayesian formula to determine an evaluation result. According to the method, various data are integrated, the reinforcement effect can be comprehensively and accurately evaluated, a scientific decision basis is provided for shield tunnel construction, the construction quality and safety are improved, and waste soil resource utilization is optimized.

The invention relates to a CVT error evaluation method and device, error evaluation equipment and a storage medium, and belongs to the technical field of mutual inductor error recognized.The CVT error evaluation method comprises the steps that a first data set is restored to a primary voltage level based on voltage proportionality coefficients of multiple CVTs to obtain a second data set, and a difference matrix is constructed based on the second data set; establishing a likelihood function based on the difference matrix, and converting the likelihood function into a posterior probability density function based on a Bayesian formula; extracting data corresponding to a preset number of CVTs from the second data set to construct a third data set, and determining a prior probability density based on the third data set; and based on a multi-chain MCMC algorithm fused with plant rhizome growth and a posterior probability density function, determining error values corresponding to a plurality of CVTs when the posterior probability density is maximum. According to the method, the accuracy of CVT error evaluation is effectively ensured, and meanwhile, the error evaluation efficiency is also improved.

The invention discloses a local knowledge base RAG method based on Bayesian reasoning, and belongs to the technical field of information retrieval. The method comprises the steps of obtaining all knowledge data of a local knowledge base, performing paragraph classification and coding on the knowledge data to obtain a paragraph set, and calculating the semantic probability of each paragraph in the paragraph set; obtaining a target professional vocabulary set of the target question, and calculating the semantic probability of each vocabulary in the target professional vocabulary set in each paragraph and the semantic probability of all vocabulary in the target professional vocabulary set in each paragraph in the paragraph set Calculating the conditional probability of each vocabulary in the target professional vocabulary set in each paragraph based on the occurrence frequency; and calculating the conditional probability of the target professional vocabulary set in each paragraph by adopting a Bayesian formula, sorting to obtain a conditional probability sorting result, selecting the paragraphs of which the total word number does not exceed a preset threshold value as a target paragraph set, and generating target retrieval information based on the target paragraph set and the target question. According to the method, the accuracy and reliability of question retrieval are improved.

The invention discloses a data analysis system and method for tracing an information management platform, and relates to the technical field of data analysis, and the method comprises the following steps: dividing full-process transportation nodes, and deploying a sensor collection environment; according to the difference of node types, different missing value filling strategies are adopted, and environmental parameters after interpolation are subjected to standardization processing; screening qualified batch data according to quality scores, constructing environmental parameter safety thresholds of each node to form a threshold pool, and iteratively updating the thresholds according to the qualified batch data; determining an effective monitoring time period by combining node circulation time, calculating a standard environmental parameter over-threshold proportion, and judging whether the node is abnormal according to a proportion threshold value; and constructing a node causal map, reversely positioning an abnormal source through a Bayesian formula, calculating a node influence weight, and screening key nodes for feedback optimization. The condition that in the prior art, effective traceability is difficult when refrigerated goods transportation is abnormal can be effectively improved.

The invention discloses an ambiguity deviation vector classification and probability decoupling collaborative protection level construction method and device, and belongs to the technical field of integrity monitoring. The method comprises the following steps: firstly, constructing a model of a positioning error under a fixed solution, then constructing an initial protection level model based on a Bayesian formula, then performing protection level parameter decoupling by utilizing an ambiguity deviation vector probability, classifying deviation vectors according to a modulus length, calculating a modulus length boundary value, and finally obtaining a protection level parameter; and finally, calculating a final protection level based on decoupling and classification results. According to the method, the influence of ambiguity error fixation on the protection level can be quantified, and the requirements of safety critical applications on reliability and interpretability are met.

The invention provides a flange sealing surface damage assessment method and device based on a leakage rate. The method comprises the following steps: obtaining a leakage rate formula reflecting the influence of a flange sealing surface defect according to a seepage principle; performing a leakage experiment on the customized damaged flange to fit correction parameters in a leakage rate formula to obtain a corrected leakage rate formula; and flange damage parameters are sampled through a Bayesian formula, sampling data are substituted into the corrected leakage rate formula to obtain a leakage rate calculation value, posterior distribution of the damage parameters under the given leakage rate is determined, and the damage grade corresponding to the posterior distribution of the damage parameter with the maximum probability is selected as an evaluation result. According to the method, the damage of the flange sealing surface can be quickly evaluated based on the direct observation data of the leakage rate, a complicated surface appearance scanning step is not needed, the evaluation process is simplified, the practical engineering application is facilitated, the sealing and safety performance of equipment can be guaranteed, and the method has high application and popularization values.

The invention provides a data processing method and device based on clinical test data, in the application, a prior probability and a historical likelihood of a symptom corresponding to target clinical test data can be determined through a historical clinical database, the prior probability reflects a basic epidemic rate of an indication in a population, and the historical likelihood of the symptom corresponding to the target clinical test data can be determined through the historical clinical database. According to the historical likelihood, association rules between symptoms and indications are mined from historical cases, the association rules and the indications are dynamically updated through a Bayesian formula, an inference chain conforming to clinical logic is formed, then, a complex multi-feature joint probability estimation problem is converted into product calculation of single-feature statistics through conditional independent assumption, and a probability estimation result is obtained. The problem of calculation feasibility under high-dimensional data is solved, probabilistic output provides a quantitative basis for auxiliary determination of indications, a data-driven statistical rule is converted into a clinically understandable auxiliary support tool, and objectivity, consistency and scientificity of diagnosis decisions are effectively improved.

The invention discloses an industrial robot kinetic parameter uncertainty analysis method, device and equipment and a medium, and relates to the technical field of robotics.The method comprises the steps that an expression of a kinetic parameter mean value and covariance is obtained through reasoning of a Bayesian formula and a kinetic equation, a robot joint torque actual value is obtained, and a first prediction error is set; obtaining a prediction error of the mth iteration based on the intermediate value and the actual value of the joint torque of the robot of the (m-1) th iteration; judging whether the difference value between the prediction error of the mth iteration and the prediction error of the (m-1) th iteration is smaller than a first precision threshold value or not, and if yes, obtaining a mean value and a covariance of the kinetic parameters according to the prediction error; calculating to obtain a robot joint torque target value; the industrial robot is controlled through the joint torque target value; and uncertainty analysis can be performed on the industrial robot according to the covariance of the kinetic parameters. The method can ensure the physical feasibility of the kinetic parameters.

The invention discloses a winter power transmission line adaptive reclosing method and system based on evidence theory fusion, and electronic equipment, and the method comprises the steps: carrying out the calculation based on a Bayesian formula model, and obtaining the posterior probability of different subdivided fault causes under corresponding weather characteristics when a winter power transmission line has a fault; the method comprises the following steps of: selecting a plurality of fault reasons, introducing the fault reasons as weight distribution into a fault reason identification classification result output by a pre-constructed waveform image identification model, subdividing a large class of faults to obtain different fault reason identification probabilities after subdivision, and fusing a posterior probability and an identification probability based on a D-S evidence theory fusion model to obtain a fault reason identification result. And obtaining different fault cause probabilities after fusion, and dynamically adjusting the operation mode, the reclosing time and the number of times of reclosing on the basis of the different fault cause probabilities. Compared with the prior art, the method has the advantages that the fault reason identification precision is greatly improved, the reclosing system can dynamically adjust according to weather changes and fault analysis, and effective decision support is provided for power transmission line adaptive reclosing in winter weather.

The present invention discloses a method for estimating formation pore pressure while drilling based on Bayesian theory, comprising the following steps: collecting target well logging data, while drilling logging data, and data from adjacent wells in the same block as the target well; predicting the target well's confidence-containing formation pressure interval profile based on the logging data and while drilling logging data, and determining the prior probability of formation pressure at any well depth; calculating the confidence-containing formation pressure interval profile of the adjacent well based on the adjacent well data, and determining the likelihood function of formation pressure at any well depth; selecting the prior probability and likelihood function of formation pressure at the same layer and substituting them into the Bayesian formula to obtain an updated posterior probability of formation pressure. The present invention realizes the while drilling update and correction of the confidence-containing formation pressure interval profile, thereby more accurately obtaining formation pressure parameters; providing more accurate formation pressure information for drilling risk assessment, reducing drilling risks caused by unclear understanding of formation pressure, and effectively improving drilling efficiency.

The invention belongs to the technical field of power system operation and planning, and discloses a power grid net load fluctuation scene generation method, system and device based on an ARIMA and Copula combined model and a medium, so as to solve the problem of poor scene generation accuracy. The method comprises the following steps: decomposing and reconstructing an original time sequence of the net load of the power grid by using discrete wavelet transform; taking permutation entropy minimization as an optimization target, adopting a variable chromosome length hybridization genetic algorithm to divide time segments for the low-frequency linear subsequences, and respectively establishing ARIMA models to generate linear trend scenes; establishing a joint probability distribution model of the high-frequency fluctuation subsequences at adjacent moments based on a Copula function, and deducing conditional probability distribution in combination with a Bayesian formula to generate a fluctuation scene; and the linear trend scene and the fluctuation scene are superposed to form an initial net load scene set, and a k-means clustering algorithm is adopted to reduce the initial net load scene set to obtain a representative scene set.

Disclosed is a natural language interaction-based method for extracting a parameter of an energy storage management system, belonging to the technical field of energy storage of new energy power systems. The method comprises parameter type division, natural language interaction corpus collection, parameter labeling, construction of a word segmentation set, natural language interaction corpus encoding, computation of a prior probability and a conditional probability, persistence of the prior probability and the conditional probability, new corpus input, loading of the prior probability and the conditional probability, computation of a posterior probability on the basis of a naive Bayes formula, and extraction of a parameter corresponding to the maximum value of the posterior probability.

This invention discloses an adaptive reclosing method, system, and electronic equipment for power transmission lines in winter based on evidence theory fusion. The method includes calculating the posterior probabilities of different subdivided fault causes under corresponding weather characteristics during winter power transmission line faults using a Bayesian formula model. These probabilities are then used as weights and incorporated into the fault cause identification and classification results output by a pre-constructed waveform image recognition model. Major fault categories are further subdivided to obtain the identification probabilities of different subdivided fault causes. The posterior probabilities and identification probabilities are fused using a D-S evidence theory fusion model to obtain the fused probabilities of different fault causes. Based on these probabilities, the reclosing operation mode, reclosing time, and number of reclosing operations are dynamically adjusted. Compared to existing technologies, this invention significantly improves the accuracy of fault cause identification and enables the reclosing system to dynamically adjust according to weather changes and fault analysis, providing effective decision support for adaptive reclosing of power transmission lines in winter.

The present invention relates to a method for reconstructing the uncertain response of a thermal protection structure based on Bayesian theory, including: constructing a reduced-order analysis model, which takes load uncertain variables as inputs and outputs the reduced-order global structural response data, and is established based on a neural network; selecting one of the uncertain variables constituting the load uncertain variables and its corresponding variable range, and randomly extracting sample points based on the Monte Carlo method so that the selected uncertain variable satisfies a Gaussian distribution within its corresponding variable range; inputting the sample point data into the reduced-order analysis model and outputting the corresponding structural response data; obtaining the prior probability density distribution of the structural response data as the response prior distribution for subsequent Bayesian inversion; experimentally obtaining the actual distribution of the selected uncertain variable, taking the response prior distribution and the actual distribution as inputs to the Bayesian formula, and performing Bayesian probability inversion to obtain the posterior distribution of the reconstructed structural response of the uncertain variable, thereby improving the accuracy and efficiency of response reconstruction.

The application discloses a kind of water supply system control methods based on flow self-distribution, specifically related to water supply system technical field, including by using historical record to construct the probability density function of monitoring feature, and by sensor determines the actual situation of current monitoring feature, based on bayes formula determines water supply probability information, and by cross entropy adjusts the weight of monitoring feature, by monitoring the steam flow of each position in boiler pipeline, based on historical data determines the influence of each position steam flow variation on water supply system water supply decision, by logistic regression model, determines water supply deviation information, by the comprehensive analysis of water supply probability information and water supply deviation information, quantifies the influence degree of each time point on water supply system decision, by comprehensively considering steam flow, water supply flow and drum water level, construct neural network model, further optimize the regulation and control of water supply system, the application is helpful to accurately adjust water supply rate, improve boiler operating efficiency and safety.

The invention discloses a new energy power generation prediction error analysis method and system considering meteorological conditions, and belongs to the technical field of computer data processing and prediction.The new energy power generation prediction error analysis method includes the steps that new energy power generation historical data, prediction error data and meteorological data are obtained and preprocessed, an initial data set is generated, and the statistical magnitude of prediction errors is calculated; combining the meteorological data in the initial data set, using a kernel density estimation method to estimate the joint probability density of the meteorological data and the prediction error data, generating joint probability density distribution, using a Bayesian formula to calculate the conditional probability distribution of the prediction error under a preset meteorological condition, and generating a conditional probability model; and performing multi-dimensional conditional probability modeling on the conditional probability model based on the climate and the position to generate a multi-dimensional conditional probability model. According to the method, kernel density estimation and the Bayesian theory are combined, and multi-dimensional space-time factors are fused to carry out refined modeling, so that the uncertainty of new energy power generation prediction can be accurately quantified, and prospective risk early warning can be realized.

The present application relates to a kind of CVT error evaluation method, device, error evaluation equipment and storage medium, belong to mutual inductor error identification technical field, wherein, the CVT error evaluation method includes: based on the voltage proportionality coefficient of multiple CVTs, first data set is restored to primary voltage level and obtains second data set, and difference matrix is constructed based on second data set;Based on difference matrix, likelihood function is constructed, and likelihood function is converted into posterior probability density function based on Bayes formula;From second data set, the data corresponding to the data of the extraction of the preset number of CVT is constructed third data set, and prior probability density is determined based on third data set;Based on the multi-chain MCMC algorithm of fusion plant rhizome growth and posterior probability density function, when the posterior probability density maximum, the error value corresponding to multiple CVTs is determined.The present application effectively guarantees the accuracy of CVT error evaluation, also improves the efficiency of error evaluation.

The invention discloses a single-base MIMO radar parameter joint estimation information amount calculation method, and the method comprises the steps: constructing a multi-dimensional joint probability density function of a uniform linear array through the property of an array receiving signal covariance matrix, and simplifying the joint estimation probability density function according to the vector form of a receiving signal; deducing a joint posterior probability density function of distance angle estimation under the condition of given received signals according to a Bayesian formula in combination with the joint probability density function, and obtaining distance direction information of multiple targets according to a distance-direction information formula; and deducing the distance direction information by using Taylor expansion under the condition of a high signal-to-noise ratio to obtain an upper bound of the distance direction information, and obtaining an entropy error, an entropy error lower bound and a Cramer-Rao bound according to the information amount and the information amount upper bound. According to the method, a posterior probability density function used for distance and angle joint estimation of multiple targets of the uniform linear array is deduced based on the information theory, and an information structure in radar observation data can be more completely described through joint distance-angle parameter modeling and mutual information theory analysis. Through introduction of mutual information indexes in an information theory, the perception capability of a radar system to target parameters can be quantified from a statistical perspective, and a theoretical basis is provided for system design.

The invention relates to a method and equipment for tracing and repairing water conservancy data quality abnormity, and a medium, and belongs to the technical field of intelligent water conservancy. Collecting multi-source water conservancy data, extracting data features and generating a standardized feature vector set; constructing a three-layer Bayesian network topological structure of circulation link-business influence factor-anomaly type, training to obtain a dynamic weight Bayesian network model adaptive to the water conservancy scene, inputting the standardized feature vector into the dynamic weight Bayesian network model obtained by training, outputting quality anomaly root cause probability distribution, and obtaining the quality anomaly root cause probability distribution. The posterior probability of each abnormal root cause is calculated through a Bayesian formula, and a root cause output traceability result is judged; performing graded repair and verification according to different traceability results; and the repaired qualified data is synchronized to a data asset module of the water conservancy credible data space for service output and generation of a visual data quality report. According to the method, the traceability precision is remarkably improved, and the service adaptability is higher.

The present invention discloses a method for predicting the electricity consumption period of residential load based on Bayesian network and fuzzy clustering analysis. The method includes: establishing a Bayesian network model with the corresponding relationship representing external environmental factors, user historical electricity consumption data, and the usage status of the load in each period; obtaining the external environmental factors affecting the residential load usage and the historical electricity consumption data of the load. The external environmental factors include environmental temperature and date information, and discretize the relevant data; obtaining the prior probability and conditional probability of the load opening status in different periods, calculating the posterior probability of the opening status in each period through the Bayesian formula to obtain the electricity consumption state probability matrix of the load, that is, the possibility of the load being used in each period; using a partial large semi-gradient membership function to conduct a fuzzy evaluation of the membership degree of the electricity consumption probability of the load in each period; using the fuzzy clustering analysis method to determine the electricity consumption time range of the load.

The invention discloses an equipment fault positioning method and system based on a fault record knowledge base, and relates to the technical field of fault detection and positioning, and the method comprises the steps: building a fault record knowledge base according to the historical fault data of each piece of equipment; judging and obtaining a fault symptom of the to-be-detected equipment through the operation parameters, and obtaining at least one piece of fault information corresponding to the fault symptom of the to-be-detected equipment based on a fault record knowledge base; on the basis of the at least one piece of fault information, through the characteristic value of the operation parameter and by using a decision tree algorithm or a Bayesian formula, calculating a prediction probability, and determining the fault information with the maximum prediction probability as target information; maintaining the to-be-detected equipment according to the initial maintenance scheme and the actual solution in the target information until the maintenance result is that the to-be-detected equipment returns to normal; by constructing a knowledge base based on historical fault data, engineers are helped to quickly and accurately diagnose equipment faults, the downtime of the equipment is shortened, and the method is suitable for the requirements of various medical equipment and complex fault scenes.

The application discloses a hydrogen energy storage system electrolytic cell selection method for complex multiple working conditions, comprising the following steps: according to existing large number platform data statistics results, determining different renewable energy hydrogen production scene probability distributions of various regions, multiple type electrolytic cell use probabilities and multiple type electrolytic cell use probabilities under different scenes, taking the data as input (prior probability matrix) of an electrolytic cell selection decision system, and then calculating electrolytic cell use probability matrix (posterior probability matrix) under different application service working conditions through a Bayesian estimation method according to the known prior probability. According to the dynamic correction of the Bayesian formula, the use probability of different types of electrolytic cells under different hydrogen production scenes is corrected, the multi-subject matching characteristics of the renewable energy hydrogen production and energy storage system are effectively ensured, the distribution problem of maximum resource utilization and maximum economic benefit in the renewable energy hydrogen production system is solved, and the economic construction of the hydrogen energy storage system applied to multiple types of scenes in the future can be effectively guaranteed.

The invention provides an arcing monitoring method and device based on acousto-optic cooperation, and relates to the technical field of arcing monitoring, and the method comprises the steps: obtaining a sound signal and an optical signal of a pantograph based on a sensor; performing multi-stage denoising and signal reconstruction on the sound signal to obtain a target sound signal; determining first data based on the target sound signal, wherein the first data is the number of arcing and the time of each arcing; determining second data based on the optical signal, wherein the second data is the number of arcing and the time of each arcing; based on a Bayesian formula, the first data and the second data, an arcing occurrence probability value is obtained through calculation; and determining an arcing result of the pantograph based on the arcing occurrence probability value. According to the invention, an acousto-optic synergistic pantograph net arcing monitoring method is adopted, monitoring is carried out by combining a sound signal and an optical signal, the defects of a traditional image detection method, a contact detection method, an ultraviolet detection method and a single sound detection method are effectively overcome, and the arcing monitoring accuracy is improved.

The application discloses a data cleaning method based on a Bayesian formula, a terminal and a storage medium, and the method comprises the following steps: acquiring original data and pre-defined prior knowledge; constructing a Bayesian network and an association relationship according to the prior knowledge, performing causal reasoning on the original data according to the Bayesian network, and obtaining a probability score of the Bayesian network; determining an association relationship score of the original data according to the association relationship, and cleaning the original data according to the sum of the probability score and the association relationship score, to obtain a cleaned data version. The application combines the user knowledge prior information which is easy to master, the modeling capability of the Bayesian network on dirty data and the association relationship of mutual information existing in the data, performs scanning and cleaning on the original data, reduces the difficulty of data cleaning, and improves the accuracy and recall rate of data cleaning.

The application discloses a kind of methods for measuring the sound reflection coefficient of material in acoustic tube, which comprises the following steps: firstly, the sample to be tested is placed in the test environment to emit and collect signals, and discrete Fourier transform is carried out to establish the sparse matrix form of sound receiving signal; then, the Bayesian formula containing hyperparameters is determined, and the hyperparameters are recursively solved to obtain the estimation result of multipath time delay; then, single-frequency PCW signal is emitted and collected, and the least square method is used to solve the amplitude parameter to obtain the estimation result of optimal multipath amplitude; finally, according to the estimation result of multipath time delay and the estimation result of multipath amplitude, the incident direct wave and the first reflected wave are extracted and separated, and the reflection coefficient of the sample to be tested is calculated. By using the sparse Bayesian learning method and the least square method, the application realizes the time delay estimation and amplitude estimation of multipath signal in acoustic tube, and achieves the purpose of separating multipath signal and calculating the reflection coefficient of acoustic material.

The invention provides a hot dry rock reservoir micro fracture fine description method integrating multi-source heterogeneous data. The hot dry rock reservoir micro fracture fine description method comprises the following steps that S1, joint fracture investigation is carried out; s2, performing fracture logging on an existing drilling well of the hot dry rock, analyzing the fracture development direction, opening degree, type and occurrence of a reservoir section, identifying the position and scale of a fracture development favorable area, counting the number of fractures, and preliminarily studying and judging the occurrence of primary and secondary fractures in combination with the rock core fracture development condition; s3, carrying out seismic inversion on high-precision seismic exploration data implemented in the hot dry rock site by adopting a Bayesian formula, and analyzing the possibility of fracture development at different positions; performing data dimension reduction, and forming a reservoir inversion initial data set after dimension reduction; multiplying each data of the reservoir inversion initial data set by a plurality of groups of 0-1 random numbers to form a plurality of groups of porosity initial data; and S4, carrying out numerical simulation on the initial porosity data, calculating the concentration of the tracer agent of the production well, and inverting parameters according to the difference between a predicted value and an actual observed value.

The present application provides a method, device, server and storage medium for recommending a financial marketing model. The method includes obtaining characteristic information of a target financial marketing model, obtaining delivery list information and converted list information of multiple historical financial marketing models from a preset database based on the characteristic information; inputting the delivery list information and converted list information of multiple historical financial marketing models into a complete preprocessing model to obtain multiple mutually exclusive delivery list information; obtaining the conversion rates of multiple historical financial marketing models based on the multiple mutually exclusive delivery list information and converted list information; determining the estimated conversion rate of the target financial marketing model based on the Bayesian formula and the conversion rates of multiple historical financial marketing models, and recommending the target financial marketing model that meets the recommendation conditions to the terminal device based on the estimated conversion rate. By normalizing the basic data, the deviation of the estimated conversion rate is reduced, and the digital financial model is accurately delivered to the target customer group.

The present invention discloses a multi-scale shared bicycle carbon emission reduction estimation and key factor identification method, equipment and medium, including: dividing an area to be analyzed into a plurality of sections and grids; calculating a shared bicycle substitution rate based on a Bayesian formula; calculating the carbon emission reduction for each trip according to the travel distance of each transportation mode used for each trip and the shared bicycle substitution rate; calculating the shared bicycle carbon emission reduction corresponding to each section and each grid; inputting input factors into a first and a second carbon emission reduction prediction model respectively, using the shared bicycle carbon emission reduction corresponding to each grid as a training label, so as to predict the shared bicycle carbon emission reduction corresponding to peak and off-peak periods; calculating the SHAP value corresponding to each input factor, and using the first K1 and K2 input factors as key factors affecting the shared bicycle carbon emission reduction during peak and off-peak periods; performing dimensionality reduction and clustering on all SHAP values, and extracting the shared bicycle emission reduction mode.

The invention discloses a rotary table multi-source data reliability prediction method based on the Bayesian theory, and belongs to the technical field of numerical control machine tools. The method comprises the following steps: constructing a mechanism reliability model based on geometric errors; according to the constructed mechanism reliability model, obtaining prior distribution and a joint prior probability density function matched with the overall Weibull reliability model under least square method line fitting; a turntable precision degradation test is carried out, and a likelihood function of life data is established according to an overall Weibull reliability model; substituting the likelihood function and the joint prior distribution probability density function into a Bayesian formula to obtain a posteriori distribution probability density function; and estimating parameters by adopting an MCMC sampling method, and carrying out reliability prediction analysis on the worm gear turntable under the condition of fusing different samples. According to the method, the prior information of the mechanism reliability model can be fully utilized under the condition that the service life sample size is small, and the reliability prediction precision in the operation process of the rotary table is effectively improved.

The invention provides a power transmission line fault reason identification method, system and device and a medium, and relates to the field of power systems, and the method comprises the steps: obtaining fault data and a to-be-identified fault, and processing the fault data and the to-be-identified fault to obtain discrete cause information; constructing posterior probability distribution of various fault causes under a given cause condition based on historical discrete cause information; fusing the posterior probability distribution into a preset Bayesian model to obtain a fault identification model; and different impedance angle information is acquired, and the fault identification model predicts and obtains a prediction category based on the impedance angle information. The method is used for identifying power transmission line faults caused by six reasons of lightning stroke, foreign matters, windage yaw, icing, pollution flashover and forest fire. Based on the Bayesian formula, the fault condition probability is calculated by using the fault cause information of the existing fault case and the impedance angle of the fault waveform, and the real fault cause is obtained.