226 results about "Prior probability" patented technology

Even when there is little measurement data of abnormal signals, it is an object to manage the abnormality of the signals. 【Means for solving the problem】 The abnormality management device 1 uses, as teacher data, normal data indicating a normal frequency spectrum among the frequency spectra of a plurality of signals, for each frequency component intensity, and the parameter of a probability model that outputs the posterior probability that each frequency component intensity is normal is learned by maximum likelihood estimation. And a derivation unit 12 configured to derive a probability distribution of abnormal data indicating a frequency spectrum including a frequency component with an abnormal intensity, based on the posterior probability estimated by the learned probability model, the probability distribution of the normal data, and the prior probability of being normal.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

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.

It aims to appropriately manage the bias of the signal processing amount. 【Solution means】 The abnormality management device 1 uses, as teacher data, normal data indicating the number of normal packets distributed to each device for each time zone, and learns, by maximum likelihood estimation, the parameters of a probability model that outputs the posterior probability that the number of packets distributed to each device for each time zone is normal. A learning unit 11, the posterior probability for each device for each time zone estimated by the learned probability model, the probability distribution of the normal data for each device estimated based on the normal data, and the prior probability of being normal. A derivation unit 12 that derives the probability distribution of abnormal data indicating the number of abnormal packets distributed to each device in the time zone, and the probability distribution of the normal data and the probability distribution of the abnormal data for each device for each time zone are equal. And a setting unit 13 that sets, as a threshold value for abnormality determination of the number of packets distributed to each device, the number of packets corresponding to the value of the probability distribution that becomes the value.

The invention provides an adjacent tunnel excavation disturbance vulnerability evaluation method based on Bayesian updating, which introduces a concept of system stiffness to represent uncertainty of tunnel damage caused by adjacent excavation disturbance, and brings obtained system stiffness data into an adjacent excavation disturbance model through a fitting equation. Calculating a large number of numerical values to establish a tunnel damage database; establishing an agent model between the parameters corresponding to the disturbance working condition and the tunnel damage index; collecting data for parameter characteristic statistics to obtain prior probability distribution of tunnel damage indexes, and combining engineering actual monitoring data for Bayesian updating to obtain posterior probability distribution of the tunnel damage indexes; and correcting the disturbance probability demand model by using the updated mean value and standard deviation of the tunnel damage indexes, and updating the vulnerability curve in combination with tunnel damage state threshold division and a logarithmic normal probability distribution function. The probability of different damage degrees of the tunnel structure can be made to fit the reality, and the applicability of an existing disturbance damage database is improved.

The invention provides a ship collision risk analysis method and system based on a fault tree and a Bayesian network, and relates to the technical field of ship safety. The method comprises the following steps: determining that main risk factors are human factors, ship factors, environment factors and management factors by analyzing ship collision accident cases; taking ship collision as a top event, performing deductive reasoning according to occurrence logic, and constructing a fault tree structure; the mapping relation between the fault tree and the Bayesian network is utilized, the Bayesian network of ship collision is established, and basic events, middle events and top events correspond to root nodes, middle nodes and leaf nodes respectively; the prior probability of a root node and the conditional probability of an intermediate node are determined through statistical analysis of main risk factors, and a Bayesian network model is formed; based on the model, a collision risk rate is calculated through forward reasoning, key risk factors are diagnosed through backward reasoning, and a main risk path causing collision is identified. According to the scheme, the complex relation between the risk factors can be accurately and objectively subjected to deep analysis and quantitative description.

The invention relates to a risk analysis method, and particularly provides a dynamic risk analysis method based on a Bayesian network. Converting the risk influence factors into a first group of network nodes based on a historical risk knowledge model, and constructing an initial topological structure according to a knowledge model logic relationship; secondly, acquiring multi-source real-time monitoring data, learning correlation among variables through a data mining algorithm, and generating a second group of nodes and a data-driven topological structure; then, connecting the historical information Bayesian network with the data-driven Bayesian network through a shared risk node, and constructing a comprehensive Bayesian network; meanwhile, a dynamic probability updating mechanism is established, and comprehensive network probability parameters are dynamically adjusted by adopting a weighted fusion algorithm in combination with a historical prior probability and a real-time posterior probability; and finally, performing risk reasoning based on the dynamically updated integrated network, and outputting risk quantitative indexes.

The purpose is to easily manage abnormal program operation. [Solution] The abnormality management device 1 includes a first learning unit 11 that uses normal data indicating processing resource usage data in which the processing resource usage corresponding to each process ID is normal as training data from among a plurality of processing resource usage data each including the usage of processing resources used in executing a process corresponding to each process ID, and learns parameters of a probability model that outputs a posterior probability that the processing resource usage corresponding to each process ID is normal by maximum likelihood estimation, and a derivation unit 12 that derives a probability distribution of abnormal data indicating processing resource usage data including abnormal processing resource usage based on the posterior probability estimated by the learned probability model, the probability distribution of the normal data, and the prior probability of normality.

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 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 discloses an intelligent education robot question answering system based on voice recognition and a knowledge graph, and particularly relates to the technical field of artificial intelligence. The method comprises the following steps: performing feature extraction on a user voice signal to obtain a text sequence and a multi-modal context feature; recognizing subject domain judgment and question answering intentions based on supervised classification and keyword rule fusion, and outputting a subject domain prior probability and a question answering target vector; determining polysemy words in the text sequence by using a context window, generating a semantic item sequence and semantic item confidence, constructing a subject domain sub-graph based on a subject domain prior probability, and obtaining a candidate reasoning path and a path scoring vector; determining a target teaching concept and an optimal reasoning path by combining Bayesian inference and consistency verification; generating a personalized question answering result aiming at the question asked by the user through fact retrieval and knowledge derivation in combination with the question answering target vector; accurate and efficient intelligent teaching question answering can be realized, and question answering accuracy and intelligent interaction capability of the education robot are effectively improved.

The invention provides a dam safety monitoring system abnormity diagnosis method, system and device and a medium, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the steps: evaluating a diagnosis object to which measuring point data of an abnormity label belongs through a knowledge graph engine, generating an abnormity alarm grouping list through employing a diagnosis object-monitoring abnormity format, and carrying out the abnormity alarm grouping list; searching an abnormal phenomenon entity matched with the alarm description in the knowledge graph through Cypher; and positioning matched abnormal phenomenon nodes in the Bayesian network, extracting associated abnormal reason nodes by using directed edges, obtaining a prior probability and conditional probability table of the abnormal reason nodes, performing probability reasoning, and outputting an abnormal reason diagnosis list. Through multi-source data fusion and knowledge graph driven intelligent reasoning and Bayesian network probabilistic reasoning, scientific and operable root cause diagnosis and disposal suggestions are provided, and the reliability and intelligent level of a dam monitoring system are enhanced.

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 belongs to the technical field of tunnel construction risk assessment, and provides a shield tunnel excavation face instability disaster risk quantitative assessment method and system. The evaluation method comprises the following steps: constructing a shield tunnel excavation face instability disaster risk evaluation system and converting the system into a Bayesian network topological structure; calculating the membership degree of each disaster evaluation index according to the digital feature calculation of the forward cloud generator and the to-be-evaluated data, and converting the membership degree into a Bayesian network prior probability; according to the historical data of the instability disaster of the excavation face of the shield tunnel and the Bayesian network topological structure, parameter learning is conducted on the historical data of the instability disaster of the excavation face of the shield tunnel, and the conditional probability of a Bayesian network model is calculated; according to the Bayesian network prior probability, the Bayesian network model conditional probability and the Bayesian network topological structure, calculating a quantitative evaluation result of the instability disaster risk of the excavation face of the shield tunnel; and according to the quantitative evaluation result of the instability disaster risk of the excavation face of the shield tunnel, performing adjustment decision on shield tunneling control parameters.

The invention relates to a three-dimensional continuous shape estimation method and system for a flexible robot, and the method comprises the steps: combining the arc length of the flexible robot and an unknown external load from the environment with a Cosserat elastic rod model and a Cosserat elastic string model, so as to generate a Lie algebraic coupling statics model representing tendon driving; the method comprises the following steps of: acquiring an external load, modeling the external load into Gaussian process noise to construct a prior probability model so as to obtain prior probability distribution of the flexible robot in the Lie algebraic space, and constructing a measurement noise model in the Lie algebraic space according to discrete measurement poses and measurement noise captured by a sensor so as to obtain a predicted measurement value. And fusing the prior probability distribution and the predicted measurement value to obtain posterior shape estimation of the flexible robot and the robot, and calculating continuous three-dimensional shape estimation of the flexible robot and first credibility of the continuous three-dimensional shape estimation according to the posterior shape estimation and a Gaussian process interpolation method. Therefore, accurate sensing of the continuous three-dimensional shape of the flexible robot is realized.

The invention provides a fault prediction method of an operating system and a program product, which can be applied to the technical field of servers. The fault prediction method for the operating system comprises the following steps: acquiring a Bayesian network structure for the operating system; according to the current running data, determining a plurality of target events corresponding to the operating system, and determining a target association condition among the plurality of target events; according to the Bayesian network structure, determining predicted fault phenomena corresponding to the plurality of target events, and determining respective target prior probabilities of the plurality of target events and target conditional probabilities for the predicted fault phenomena; and according to the current operation data, the target prior probability and the target conditional probability, determining the probability that the operating system has a predicted fault phenomenon.

The invention provides a crustal stress prediction method and device, electronic equipment and a storage medium, and relates to the technical field of seismic survey. The method comprises the following steps: obtaining observation seismic data of seismic wavelets in a strong VTI medium, and constructing a to-be-inverted parameter matrix based on a PP wave reflection coefficient corresponding to the strong VTI medium; constructing a posterior probability function obeyed by an inversion parameter matrix corresponding to the to-be-inverted parameter matrix based on a Bayesian inversion theory and observation seismic data, and determining a target functional based on a prior probability function and a likelihood function corresponding to the posterior probability function; determining medium density and each stiffness matrix coefficient based on an inversion parameter matrix solving result of the target functional, and determining a flexibility matrix of the strong VTI medium based on each stiffness matrix coefficient; and predicting the ground stress distribution of the target profile in the strong VTI medium based on the medium density and the positive strain matrix and the flexibility matrix corresponding to the strong VTI. Therefore, the crustal stress prediction accuracy under the strong VTI medium is improved.

The invention discloses a method for establishing a risk management model of a hydrogen-doped natural gas pipeline. Firstly, a Bow-Tie model system is used for analyzing a failure reason and an accident consequence development scene of the hydrogen-doped natural gas pipeline; then, the model is mapped into a Bayesian network by utilizing GeNIe software; and calculating the prior probability of the basic event through an expert evaluation method and a fuzzy set theory. Setting a conditional probability table of the Bayesian network by using a fault tree mapping method and a historical data calculation method; on the basis, simulating an equipment component failure process through a Markov model, introducing hydrogen corrosion and hydrogen-assisted fatigue crack propagation models to carry out hydrogen-related dynamic analysis, and finishing parameter setting of a dynamic Bayesian network; and finally, calculating the failure probability of the hydrogen-doped natural gas pipeline and a potential life loss result when an accident occurs in combination with a model output result. The method adopts the dynamic Bayesian network to predict the pipeline failure probability, is more in line with the actual operation condition, and can be widely applied to the risk management process of the hydrogen-doped natural gas pipeline.

The invention relates to the technical field of motor control and cloud computing, in particular to an asynchronous motor inertia response detection method and system based on cloud computing, and the method comprises the steps: initializing prior probability distribution according to preset parameters of a motor; generating an optimal excitation signal parameter; synthesizing an excitation signal according to the received optimal excitation signal parameter, injecting the excitation signal into a motor, synchronously collecting original dynamic response data, and generating a feature data set; uploading to a cloud inertia inference center; calculating posterior probability distribution; comparing the variance of the posterior probability distribution with a preset convergence precision threshold value; if the variance is smaller than a convergence precision threshold value, controller parameters of an edge side control execution unit are updated based on a final inertia identification result; and if the variance is not less than the convergence precision threshold, taking the currently calculated posterior probability distribution as the prior probability distribution of the next iteration, and returning to execute the step of generating the optimal excitation signal parameter. According to the invention, high-precision parameter identification under extremely low disturbance is realized.

The dynamic prediction and inducement identification method for the shaft integrity failure risk is characterized in that different influence sequences of shaft integrity failure risk factors under different working conditions are fully considered, and key risk factors are determined by adopting a principal component analysis method; constructing a shaft integrity failure fault tree comprising a top event, a middle event and a bottom event by utilizing a failure mode and an effect analysis method; determining a monitoring index according to the service condition of the predicted well; according to real-time changes of field data, dynamic updating of a prior probability, a conditional probability and a transition probability is realized in combination with a sliding window and a long-short-term memory network; combining the shaft integrity failure fault tree and the dynamic Bayesian network to establish a shaft integrity failure dynamic risk prediction model; and based on the dynamic changes of the prior probability, the conditional probability and the transition probability, utilizing the shaft integrity failure dynamic risk prediction model to realize shaft integrity failure dynamic risk prediction and inducement identification.

The invention relates to a seismic liquefaction assessment method based on conditional random field simulation, which comprises the following steps: firstly, obtaining a logarithmic normal distribution random field of a target area under a corresponding SPT-N value, then resampling through a Bootstrap method, constructing a weighted prior probability density function of the target area in combination with a likelihood function, and finally calculating the seismic liquefaction of the target area according to a Bayesian theory. A Markov chain Monte Carlo sampling method is combined, through posterior probability density distribution, an optimal horizontal direction correlation distance is determined, a covariance matrix is constructed to generate a conditional random field, and then through multiple times of simulation, the conditional random field is converged; and finally, aiming at the target area, through calculation of a cyclic stress ratio and a cyclic resistance ratio, constructing a liquefaction probability distribution diagram corresponding to the target area. According to the method, a conditional random field simulation method is inferred and improved by combining Bootstrap and Bayesian theories, the precision and reliability of geological parameter simulation are remarkably improved, and reliable data support is provided for seismic liquefaction assessment of deep and uneven site engineering.

Systems and methods are provided for subsurface characterization from seismic data. The system can receive a plurality of candidate velocity and anisotropic parameter models and seismic gather data. A subset of the plurality of candidate velocity and anisotropic parameter models can be selected to form a training data set. The system can generate a joint probability functions of depth differences and seismic semblances based on the training data set and generate a likelihood function based on the joint probability function. A Bayesian model can be defined using the likelihood function and the prior probability functions. The system can draw a plurality of samples from a posterior distribution of the Bayesian model using Markov Chain Monte Carlo sampling methods and calculate depth uncertainty values using the plurality of samples.

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 a cable tunnel bridge fire prediction method and device, electronic equipment and a storage medium. The cable tunnel bridge fire hazard prediction method comprises the following steps: establishing a cable tunnel bridge fire hazard numerical simulation model, establishing a cable tunnel bridge fire hazard simulation model based on FDS software, obtaining key parameters such as heat release rate, temperature distribution and flame spread boundary under different working conditions, and constructing a multi-time sequence sample data set; performing state discretization on continuous variables such as temperature and a spreading range, and estimating a node prior probability in combination with a statistical frequency; constructing a dynamic Bayesian network topological structure according to a causal relationship among a heat source, temperature and spread, and setting a cross-time slice node dependence path to realize time sequence modeling; inputting a training sample and performing parameter learning to generate a conditional probability table; in the prediction stage, multi-step reasoning is achieved through forward propagation, flame spreading range probability distribution and interval estimation of multiple time steps in the future are obtained, and the specific position where the flame arrives is predicted.

The object is to manage signal abnormalities even when there is little measurement data of the abnormal signal. [Solution] The abnormality management device 1 includes a learning unit 12 that uses the characteristic directions of normal data as training data to learn, by maximum likelihood estimation, parameters of a probability model that outputs the posterior probability that the intensity of each frequency component corresponding to each of the characteristic directions of the normal data is normal; a derivation unit 13 that derives a probability distribution for the characteristic directions of abnormal data that shows abnormal intensities of frequency components that deviate from a normal intensity range, based on the posterior probability estimated by the probability model learned by the learning unit 12, the probability distribution for the characteristic directions of the normal data, and the prior probability of normality; and a calculation unit 14 that calculates a first index value that indicates the degree of spatial agreement formed by the probability distribution for the characteristic directions of the abnormal data derived by the derivation unit 13 and the probability distribution for the characteristic directions of the normal data.

The invention discloses a sensitivity-based network intrusion detection resampling method, system and device and a medium, and relates to the technical field of data detection, and the method comprises the steps: obtaining network data containing normal traffic and attack traffic; prior probabilities and class condition probabilities of the two classes of data are calculated respectively, then posterior probabilities are obtained, and the sensitivity weight of each sample is calculated; carrying out putting-back undersampling on a normal flow sample according to the sensitivity weight; carrying out replacement oversampling on the attack traffic sample according to the sensitivity weight; a sampling process is repeated to generate a plurality of balance training subsets, and a base classifier is trained based on each subset; and calculating voting weights based on the performance evaluation indexes of the base classifiers on the verification set, and obtaining weighted voting results based on the voting weights to classify new data. Random undersampling neglects the importance of boundary samples to decision boundary learning, and sensitivity weighted sampling can guide the model to sample the boundary samples, so that important information is prevented from being lost.

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 discloses a system performance prediction method for a single pilot driving mode of a commercial aircraft, and the method comprises the steps: collecting original root node information, constructing a root node state model, judging the state of a root node according to a set threshold value, and carrying out the parameterization of a Bayesian network, namely obtaining the prior probability of the root node and the conditional probability distribution between the nodes; the obtained system performance prediction result is applied to adaptive automation, and the result is compared with a given adaptive decision threshold to judge whether an adaptive decision is triggered or not. According to the method, the system performance model based on the Bayesian network is established, and the machine performance influence factors are fused into the Bayesian network model currently used for predicting the system performance, so that compared with the prior art, the performance prediction uncertainty caused by the subjectivity of analysts and the individual difference between operators is removed; and the accuracy of system performance prediction is improved.

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.