97 results about "Prior probability" patented technology

The invention relates to a land surface temperature and emissivity joint inversion method and system and a medium. The inversion method comprises the following steps: acquiring radiation brightness vectors observed by a satellite sensor in a plurality of thermal infrared bands as observation radiation brightness vectors; constructing a standard emissivity spectrum dictionary database as a dictionary matrix; defining a generation rule of the surface emissivity; establishing a forward physical model, and taking an analog value of the observed radiance vector as model output; defining a likelihood function of an observation radiance vector, defining sparsity prior probability distribution for the sparse coefficient vector, and defining uniform prior probability distribution for the surface temperature; joint posterior probability distribution of the surface temperature and the sparse coefficient vector is calculated, and multiple groups of samples are extracted; calculating a posteriori estimation value and an uncertainty interval of the surface temperature; and calculating a posteriori estimation vector of the sparse coefficient vector, and multiplying the posteriori estimation vector with the dictionary matrix to obtain a posteriori estimation value of the surface emissivity. The automation level of remote sensing information extraction and the information output efficiency are improved.

A method of decentralized collaborative target searching, target tracking, or both, includes initializing a probability distribution over a probabilistic search area with an a priori probability distribution of target locations, transmitting the probability distribution to a plurality of vehicles, capturing, using a plurality of sensors on the plurality of vehicles, sensor data relating to locations of a target, updating the a priori probability distribution based on the sensor data or based on data observations outside of the plurality of vehicles, or both, to provide an updated probability distribution, determining optimal trajectories for the plurality of vehicles using the updated probability distribution and using an ergodic principle to define optimality and based on each vehicle of the plurality of vehicles calculating its own optimal trajectory to enable decentralized calculations, and detecting or tracking, or both, the target based on the determining of the optimal trajectories.

The invention relates to the technical field of purchase demand prediction, and discloses a part purchase demand prediction method and system based on machine learning, and the method comprises the steps: obtaining modular product basic data, and generating a BOM graph structure; calculating prior probability distribution through a Bayesian inference algorithm; executing an adaptive probability pruning algorithm to solve the problem of combinatorial explosion; quantizing uncertainty by using a Bayesian deep learning network; calculating a differentiated safety inventory coefficient based on the value-at-risk model; executing an importance sampling algorithm to carry out Monte Carlo simulation on high-risk low-frequency configuration, and verifying a demand coverage rate in an extreme scene; an incremental learning mechanism is utilized to update probability distribution and a pruning threshold according to the new order data, and a self-adaptive optimization demand prediction result is output; according to the method, the inventory cost is remarkably reduced, the stockout risk is reduced, and the balance between the calculation efficiency and the prediction accuracy is realized.

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.

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.

This invention discloses a method for assessing the quality of carbon emission data, relating to the field of carbon emission data quality assessment. The method employs sensitivity analysis to determine the main parameters affecting total carbon emissions; it scores the quality of these main parameters across four dimensions: numerical accuracy, source reliability, evidence standardization, and the degree of digitization of evidence, converting the scores into parameter uncertainty U-values; it performs two rounds of Monte Carlo simulations based on the parameter uncertainty U-values ​​to construct the likelihood function corresponding to the current observation; it calculates historical uncertainty U-values ​​based on multi-period historical carbon emission data and fits the prior probability distribution of the uncertainty; and it applies a Bayesian update formula to fuse the likelihood function corresponding to the current observation with the prior probability distribution, deriving the posterior distribution of the current carbon emission data uncertainty to determine whether the current carbon emission data meets quality requirements. This method improves the scientific rigor, transparency, and operability of carbon emission data quality management.

The invention relates to a lithium ion energy storage prefabricated cabin risk toughness assessment method based on a fuzzy Bayesian network model. According to the method, real-time operation data, including risk factors such as battery voltage, current, temperature, available capacity, dew point temperature, dust concentration, air velocity and altitude, of the energy storage prefabricated cabin and equipment state data of a battery management system, a fire prevention system and a gas monitoring system are collected. Through processing and calculation of the data, key risk indexes related to the fire risk are determined, and the fault probability of each system is calculated. A risk level of a risk index is converted into a fuzzy number by using a fuzzy set theory, and then the fuzzy number is converted into a prior probability through a left-right fuzzy sorting method. In combination with historical statistical data of the energy storage prefabricated cabin, a Bayesian network total probability formula is used to calculate the probability of fire occurrence, and a reverse reasoning mechanism is used to identify a risk factor which is most likely to cause the fire. And quantitative and dynamic evaluation of the fire risk of the energy storage prefabricated cabin is realized.

The application relates to a three-dimensional continuous shape estimation method and system of a flexible robot, wherein the method combines the arc length of the flexible robot and unknown external loads from the environment with a Cosserat elastic rod model and a Cosserat elastic string model to generate a Lie algebra coupled statics model representing a tendon-driven model, models the external loads as Gaussian process noise to construct a prior probability model, obtains a prior probability distribution of the flexible robot in a Lie algebra space, constructs a measurement noise model in the Lie algebra space according to discrete measurement poses captured by a sensor and measurement noise, obtains a predicted measurement value, fuses the prior probability distribution and the predicted measurement value to obtain a posterior shape estimation of the flexible robot, and calculates a continuous three-dimensional shape estimation of the flexible robot and a first credibility of the continuous three-dimensional shape estimation according to the posterior shape estimation and a Gaussian process interpolation method. Therefore, the application realizes accurate perception of the continuous three-dimensional shape of the flexible robot.

The application discloses a kind of based on crowdsourcing data to the method, device and equipment of extracting ground feature element, and relates to map data technical field.The method comprises the following steps: obtaining first identification result;First identification result is the result obtained by identifying the information collected by first collection device at target position at first time;When first identification result indicates that ground feature element is identified in target position based on the collection information of first collection device, obtain first preset condition probability parameter and first prior probability of first collection device;According to first preset condition probability parameter and first prior probability, first posterior probability is calculated;When first posterior probability is not less than first threshold, ground feature element is extracted.The application can timely and accurately extract ground feature element, so as to update map timely and accurately.

In view of the problems of complex and diverse combat tasks of reconnaissance and attack integrated unmanned aerial vehicle, difficult to obtain test data, strong subjectivity of safety evaluation, etc., a safety evaluation method of reconnaissance and attack integrated unmanned aerial vehicle based on improved Bayesian network is proposed, which aims to evaluate the safety of reconnaissance and attack integrated unmanned aerial vehicle through the model. The method mainly includes: step 1. Constructing the safety evaluation index system of reconnaissance and attack integrated unmanned aerial vehicle; step 2. Improving the Bayesian network evaluation model. The present application firstly proposes to use the entropy method to improve the G1 method to obtain the prior probability of the root node, and uses the EM algorithm to obtain the conditional probability of the child node. The improved Bayesian network has the unique advantages of processing complexity and multivariate conditional probability distribution, which significantly enhances the adaptability and accuracy of the model in the environment of incomplete data, and can be applied to more extensive application scenarios.

This invention belongs to the technical field of logistics management, specifically relating to a method and system for predicting the arrival time of goods in transit based on real-time environmental data. It addresses the technical problems of existing methods, such as difficulty in quantifying uncertainty and neglecting real-time data quality and vehicle status. The prediction method includes: S1, calculating Shannon entropy based on the prior probability distribution of arrival time at the current moment; S2, adjusting the basic transition probability matrix to obtain a spatiotemporally correlated transition probability matrix; S3, generating evidence update weights by comprehensively considering the quantification index of prediction uncertainty and the credibility of observational evidence; and S4, obtaining the posterior probability distribution of arrival time at the current moment through Bayesian updating. This invention can improve the accuracy of arrival time prediction in complex traffic environments.

The invention discloses a cooperative spectrum prediction method based on minimum Bayesian risk, and relates to the technical field of cognitive radio and wireless communication. According to the method, each secondary user independently completes local spectrum sensing and power prediction; the fusion center performs system perception judgment by using a historical optimal fusion rule, calculates a channel state prior probability, and fits a predicted power condition probability density function of each user in two system states; then, according to a minimum Bayesian risk criterion, through alternative iteration optimization of each user judgment threshold and a system fusion rule, an optimal threshold and a fusion rule are obtained; and finally, obtaining a system-level cooperative spectrum prediction result through threshold comparison and fusion. The method does not depend on prior channel information, has good flexibility and expansibility when the number of users changes dynamically, improves prediction precision and robustness in a complex environment, and can balance main user protection and spectrum access efficiency through a cost factor.

The application relates to a sparse map construction method based on clustering Bayesian optimization, which comprises the following steps: 1) constructing an initial data set, and constructing and training an initial agent model; 2) distributing sampling points in a map to initial clustering centers according to a hard clustering algorithm and a self-defined distance judgment standard, and iteratively calculating new clustering centers; 3) calculating a prior probability distribution, and obtaining a Bayesian optimization collection function based on spatial clustering; 4) selecting a spatial point with the maximum collection function value as a new optimal sampling point, obtaining information of the optimal sampling point and minimizing uncertainty; 5) expanding the initial data set, and updating the initial agent model; 6) if the error of the new agent model is greater than a threshold value, step 4) is executed, if the error is less than the threshold value or the maximum iteration number is reached, step 7) is entered; and 7) constructing a complete sparse map according to the new agent model. Compared with the prior art, the application has the advantages of higher precision, stronger robustness and the like.

The invention provides a hydraulic structure periodicity reliability evaluation method and device based on monitoring and early warning, and relates to the technical field of hydraulic structure health monitoring. The method comprises the following steps: establishing a dynamic model containing a basic random variable set, defining a concerned physical quantity vector and setting prior probability distribution; fuzzy fusion is carried out on the denoised multi-source monitoring signals to obtain real-time graded early warning; carrying out representative time period division; extracting a time sequence feature vector from a denoised signal; correcting prior probability distribution and selecting a representative point; obtaining a generalized speed according to deterministic dynamics analysis; and performing graded early warning according to the comprehensive membership degree. According to the method, physical interpretability and data assimilation capability are considered, and large-scale Monte Carlo is replaced by representative point approximation and numerical evolution, so that the calculation amount can be reduced, and the real-time performance and precision of dynamic reliability evaluation can be improved.

The application discloses an industrial equipment fault prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining fault data and steady-state data in the operation process of the industrial equipment; using a pre-set naive Bayes model to process the fault data and the steady-state data to obtain an accuracy coefficient; wherein the accuracy coefficient comprises overall classification accuracy and a Kappa coefficient; determining a fault prior probability and a fault conditional probability associated with the accuracy coefficient, and calculating a posterior probability of the industrial equipment failure according to the fault prior probability and the fault conditional probability. The technical scheme can discover potential fault risks of the equipment in time, so that the effect of solving the fault in advance is achieved, the maintenance cost is reduced, and economic losses are avoided.

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 is suitable for the technical field of oil depot major accident concept evaluation, and discloses an accident evaluation method and device based on threatened data and a Cloud-Leaky Noisy-OR Bayesian network, and the method comprises the steps: constructing an oil depot major accident bow-tie model based on the risk factors and consequences of an oil depot major accident; the method comprises the following steps: constructing a Cloud-Leaky Noise-OR Bayesian network model on the basis of a Leaky Noise-OR logic gate Bayesian network model and a cloud fuzzy comprehensive evaluation algorithm, and constructing the Cloud-Leaky Noise-OR Bayesian network model; on the basis of the bow tie model, prior probability data and a hierarchical Bayesian analysis method, a Cloud-Leaky Noisy-OR Bayesian network analysis model is constructed; and substituting the threatened data into the Cloud-Leaky Noise-OR logic gate Bayesian network analysis model, and carrying out dynamic probability evaluation on the major accident of the oil depot to obtain an evaluation result. According to the technical scheme of the invention, the method achieves the probability updating and dynamic evaluation of the major accident based on the given threatened data in combination with the Cloud-Leaky Noise-OR Bayesian network model, and provides a theoretical basis for the control of the major accident risk of the oil depot.

This invention discloses a sampling design method, sampling method, and sampling analysis method for post-construction pile foundation inspection. The design method includes: obtaining the total number of piles to be inspected and the number of batches to be inspected; initializing the current batch to be designed as the first batch, and initializing the prior probability of the quality risk coefficient of the current batch before inspection as a uniform distribution; calculating the minimum number of inspections required to make the quality risk coefficient of the current batch closest to the target quality risk coefficient; calculating the prior probability of the quality risk coefficient of the current batch after inspection based on the expected inspection results and a Bayesian model; using the next batch as the new current batch, and returning to the calculation operation of the inspection quantity, until the inspection quantity for all batches has been calculated. This embodiment improves the reliability and scientific rigor of post-construction pile foundation sampling inspection.

Methods for determining a physical property of a subterranean target volume, as well as systems and computer-readable media for performing the methods, are provided. According to the method, clues are mined from geographic data, and the method further relates to the improvement of sustainability and environmental development: we create a safe and livable world together. The method includes performing tomography inversion on the obtained empirical propagation time data. Performing the tomographic inversion includes obtaining a posterior probability density function corresponding to each of the first plurality of cells, where obtaining the posterior probability density function includes obtaining a prior velocity model, and defining, for each receiver pair in the subset, a prior probability density function indicative of a velocity of each of the first plurality of cells, a modeled propagation time is determined using initial velocity model values associated with two or more cells traversed by the surface wave path, and a posterior probability density function of the velocity model is determined based on the modeled propagation time and a prior probability density function, wherein the prior probability density function depends on the distance of each cell from a first point on the surface.

This invention discloses a method for updating a long-service hydraulic twin model based on multi-fidelity Bayesian methods. It involves collecting non-contact, full-field micro-vibration time-history data of hydraulic structures and extracting high- and low-fidelity data observations. Combining macroscopic physical aging equations, a time-varying degradation prior probability distribution with historical memory is constructed. Based on the high- and low-fidelity data observations, a spatially and mechanistically heterogeneous multi-fidelity twin architecture is built. A spatial covariance matrix of full-field measurement points is introduced, and the Bayesian heterogeneous MCMC method is used to infer parameters of the multi-fidelity twin architecture until convergence. The high-confidence posterior parameters of the structural physical parameters and boundary nonlinear stiffness are mapped to the long-service hydraulic twin model, enabling high-frequency adaptive evolution and synchronous updating of the full life-cycle state of the long-service hydraulic structure digital twin under complex uncertainties.

The invention discloses a big data diagnosis method and system based on auscultation data, and the method comprises the steps: firstly obtaining and preprocessing an original auscultation sequence, and constructing a basic sequence through normalization, artifact removal and noise suppression; and slicing the time sequence signal on a plurality of downsampling scales by using multi-scale chaos measurement, and extracting pathological sensitive fragments. Then, feature extraction is carried out on the segments through a multi-branch encoder including time domain convolution, time frequency convolution and time sequence modeling, and an auscultation feature sequence is formed; and constructing a time topological structure based on the sequence, evaluating a node causal relationship by using a Bayesian network, and adaptively combining low-quality fragments in combination with an ontology value and an overall value to obtain a stable fragment structure. And further generating a pathological acoustic causal strength index, and converting the pathological acoustic causal strength index into a causal state sequence in a disease mode. And finally, in combination with the prior probability of positive and negative correlation between diseases and the prediction trend of the LSTM, state transition and attention enhancement are carried out, and disease diagnosis of the target individual is realized.

The invention belongs to the technical field of intelligent manufacturing and industrial Internet of Things, and provides an automobile welding workshop early warning method and system based on historical and real-time data, and the method comprises the steps: constructing phase sensing features of different scales based on a collected phase control signal of a welding process; based on the phase perception characteristics of different scales and the constructed phase perception depth time sequence prediction model, a process parameter prediction value is obtained; extracting causal relationship prior information based on a phase perception depth time sequence prediction model, mapping the extracted causal relationship prior information into an edge prior probability of a Bayesian network structure, and constructing a hierarchical Bayesian network of prediction-diagnosis bidirectional coupling according to the edge prior probability; and calculating a joint likelihood according to the real-time observation value and a predicted value of the phase perception depth time sequence prediction model, obtaining a joint likelihood anomaly probability in combination with an anomaly posterior probability deduced by a hierarchical Bayesian network, and generating an anomaly trigger decision strategy in combination with the joint likelihood anomaly probability and a joint likelihood anomaly criterion. And the early warning accuracy is improved.

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 relates to an X-ray chest radiography spine offset detection method and system based on a prior probability graph, and the method comprises the steps: obtaining a to-be-detected X-ray chest radiography image, and carrying out the normalization of the transverse pixels of the image; obtaining a corresponding prior probability value through linear interpolation in pre-constructed one-dimensional prior probability distribution based on the normalized position of each column of transverse pixels of the image, constructing a one-dimensional array, copying the one-dimensional array in the vertical direction, and constructing a two-dimensional prior probability graph; inputting an X-ray chest radiograph image to be detected into the GPU accelerated segmentation network to obtain a spine binary mask; and based on the two-dimensional prior probability graph, calculating an average prior probability and an offset probability in the mask area of the spine binary mask, comparing the average prior probability and the offset probability with a preset offset threshold value, if the offset probability is greater than the offset threshold value, determining that the spine is offset, otherwise, determining that the spine is basically centered. Compared with the prior art, the method has the advantages that the offset detection result has better interpretability, stability and quantificaiton.

The application belongs to the technical field of radio detection and sensing, and relates to a Kalman tracking method, device, equipment and medium for radio direction finding. The method comprises the following steps: establishing a main tracking chain and a backup tracking chain for radio direction finding; obtaining current observation data; performing model prediction on the state of each mode of the main tracking chain and the state of each mode of the backup tracking chain; calculating the likelihood probability of the current observation data belonging to each mode of the main tracking chain and the backup tracking chain, the prior probability of each mode of the main tracking chain, and the prior probability of each mode of the backup tracking chain, and calculating the posterior probability of the current observation data belonging to each mode of the main tracking chain and each mode of the backup tracking chain; updating the state of each mode of the main tracking chain and the state of each mode of the backup tracking chain and traversing, and taking the mean value corresponding to the mode with the maximum heat as the tracking information of the current observation and outputting. The method can effectively resist outliers and adapt to the maneuverability of unmanned aerial vehicles.

The present application belongs to the technical field of city pipeline facility safety monitoring, and discloses a pipeline damage ultrasonic guided wave monitoring and probabilistic bending positioning system and method. The piezoelectric sensor for excitation and the piezoelectric sensor for receiving are installed on the pipeline section to be detected and positioned, the received signals are transmitted to the local control system, the signals are preprocessed in the local control system, and then transmitted to the central control system through wireless transmission. The received signals and the non-damage received signals under the same working condition are compared, and the signal correlation coefficient is extracted. The damage probability function distribution obtained by the probabilistic bending positioning damage is used as the prior probability distribution and the likelihood function, respectively. Finally, the posterior probability distribution is obtained by Bayesian correction as the final positioning result. The method used in the present application can accurately realize the positioning of the damage on the pipeline wall, and has high precision.

The invention belongs to the field of three-dimensional geological modeling, and particularly discloses a bilinear interpolation and multipoint statistical simulation combined three-dimensional stratum modeling method, which comprises the following steps: acquiring two parallel geological sections A and B discretized according to geological attributes; generating a plurality of middle sections through bilinear interpolation based on A and B according to a plurality of preset different positions; dividing each generated middle section into a condition data part and a prior probability part; for each middle section, on the basis of the condition data part and the prior probability part of the middle section, the section A and the section B are used as training images, multi-point statistical simulation is executed, and through a Bayesian probability fusion mechanism, a multi-point statistical simulation result after probability distribution is fused with the prior probability part to obtain a multi-point statistical simulation result; obtaining a final geological attribute of each position in the middle section; and synthesizing all the obtained middle sections into a three-dimensional geologic model. According to the method, the calculation efficiency and the real description capability of the complex geological structure can be considered in geological modeling.

The application provides a HTI medium elastic parameter and fracture weakness seismic inversion method and system, and comprises the following steps: establishing a seismic forward model based on a longitudinal wave reflection coefficient equation of the HTI medium and a convolution model of a seismic wavelet; according to the fact that a prior distribution of model parameters of the seismic forward model conforms to a Gaussian distribution, analyzing a prior probability distribution function and the seismic forward model by using a Bayesian theory, and then obtaining a posterior probability distribution function of the model parameters; and inversely solving the posterior probability distribution function of the model parameters to obtain the HTI medium elastic parameter and the fracture weakness. In combination with prior information of the model parameters, the uncertainty of an inversion result can be analyzed through the posterior probability distribution, and useful reference information is provided for subsequent seismic interpretation work.

The invention discloses a traditional Chinese medicine menstrual prescription clinical teaching evaluation method based on information entropy. The method comprises the following steps: constructing a conditional probability knowledge base; initializing a diagnosis space and a feature space, determining each diagnosis feature in the diagnosis space and each symptom problem distribution initial prior probability in the feature space based on a conditional probability knowledge base, and calculating to obtain an initial information entropy; acquiring an input symptom feature, calling a corresponding conditional probability according to a conditional probability knowledge base, executing Bayesian updating, and calculating a diagnostic posterior probability; after each Bayesian update, calculating and diagnosing spatial information entropy, and determining entropy decrement according to the initial information entropy; recording the diagnosis probability distribution and entropy decrement; and traversing the candidate symptom features which are not input, calculating expected information gains of the candidate symptom features, and outputting recommendation questions according to the expected information gains. According to the invention, objective quantitative evaluation of student thinking paths is realized, and dynamic optimization and guidance of inquiry strategies are realized through information gain calculation.