176 results about "Prior probability" patented technology

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 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 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 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 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 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.

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 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 relates to a gas leakage probability dynamic evaluation method and device based on a Bayesian network, and the method comprises the steps: constructing a gas leakage risk accident tree based on the historical data of gas leakage, and constructing a Bayesian network evaluation model of the gas leakage risk based on the gas leakage risk accident tree; calculating a prior probability of each node in the Bayesian network evaluation model according to statistical analysis of gas leakage historical data; and based on the prior probability, reversely deducing the posterior probability of each node in the Bayesian network evaluation model so as to identify key risk factors having influences on the gas leakage risk, and obtaining a gas leakage probability dynamic evaluation result. Therefore, the problems that the related technology depends on a static probability model, lacks dynamic modeling ability and is difficult to accurately reflect risk evolution and dominant factors under multi-factor coupling, so that the evaluation result is lagged, and the overall evaluation ability is limited are solved.

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

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

The invention relates to the technical field of power system fault protection, in particular to a power grid secondary equipment defect identification method based on knowledge graph and Bayesian network fusion, and the method comprises the steps: carrying out the semantic extraction of multi-source heterogeneous data to construct a defect knowledge graph, breaking through the limitation of a data island, and achieving the full-dimensional correlation analysis of equipment defect features; mapping the power grid secondary equipment defect knowledge graph to a Bayesian network framework, and quantifying the prior probability and conditional probability of defect occurrence based on historical data prior knowledge; and finally, providing accurate fault mode positioning by means of defect sub-graph search of the knowledge graph, and completing defect propagation path prediction by means of probability reasoning of the Bayesian network, so that the method can comprehensively utilize multi-source heterogeneous data of the secondary equipment of the power grid, and the fault propagation path prediction accuracy is improved. The complex relation and probability influence between the defect phenomenon and multiple layers of reasons are accurately described, the defect recognition accuracy and response timeliness are remarkably improved, and support is provided for safe and stable operation of a power grid.

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.

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 invention provides an automobile collision simulation model parameter identification method and system, and the method comprises the steps: collecting the dynamic response data of a physical collision experiment, building a finite element model of an experimental vehicle based on the dynamic response data, and carrying out the recognition of the parameters of the experimental vehicle; according to the method, prior probability distribution of to-be-identified parameters is set according to the parameter range of the to-be-identified parameters, a corresponding parameter sample set and a corresponding response simulation matrix are generated, the response simulation matrix is simplified through principal component analysis so as to reduce the calculation amount, posterior probability distribution is calculated according to the response principal component matrix, and then quality evaluation indexes of a finite element model are calculated. And the final to-be-identified parameter is determined according to the quality evaluation index, so that the accuracy of the to-be-identified parameter is improved, and then the accuracy of the automobile collision simulation model is improved.

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.

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.

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.