87 results about "Posterior probability" patented technology

The invention discloses a watershed water resource supply and demand risk prediction and evaluation method based on a multilevel Bayesian causal network, and relates to the technical field of water resource supply and demand risk management.The watershed water resource supply and demand risk prediction and evaluation method comprises the steps that a water resource supply and demand risk diagnosis knowledge graph is constructed according to key variables and interrelations input by a user; constructing a multi-level Bayesian causal network structure; estimating conditional probability distribution among the nodes, and performing parameter learning and structure training on the Bayesian causal network; carrying out risk path identification through a reverse Bayesian reasoning method; outputting a posterior probability of water resource supply and demand risk prediction; based on a preset fuzzy character string matching algorithm, typical risk events and risk features are extracted; and according to the posterior probability and the risk characteristics, comprehensively evaluating the water resource supply and demand risk level. The method can improve the systematicness and scientificity of risk identification, is suitable for multi-link and multi-scale risk assessment and scheme comparison and selection in a complex drainage basin, and has high practical value and popularization prospect.

The mold injection control method comprises the steps that three-dimensional point clouds of a current injection molding part and an adjacent previous injection molding part are obtained, the global difference degree between the current injection molding part and the adjacent previous injection molding part is calculated through overall registration, if the global difference degree exceeds a threshold value, a difference mask is generated and covers a real-time image of a cavity, a difference image is formed, and the difference image is subjected to injection molding. And performing hybrid clustering on the difference image and a known defect type database to obtain candidate defect types and membership degrees, calculating posterior probability contribution degrees of the sensing nodes to the candidate defect types by taking the membership degrees as input and combining a Bayesian reasoning algorithm, if the contribution degree of any sensing node exceeds a threshold value, outputting a defect root cause report, and if the contribution degree of any sensing node exceeds the threshold value, outputting a defect root cause report. And a machine table or a mold execution mechanism is driven to carry out correction. According to the invention, the problems of low detection efficiency, large subjective deviation and lagging root cause judgment due to the fact that automobile part defect detection and root cause analysis depend on manual visual detection or single-dimensional automatic detection are solved.

The invention discloses an intelligent fishing point dynamic prediction method and system based on multi-source marine environment data fusion. The method comprises the following steps: step 1, access, space-time alignment and pre-screening of multi-source heterogeneous marine environment data; step 2, priori knowledge base construction and suitability modeling based on target fish ecological habits; 3, constructing a fishing point prediction model fusing the multi-time-sequence environmental characteristics and deep learning; step 4, fusing two-channel prediction results under the Bayesian framework and quantifying uncertainty; 5, generating a dynamic mask of a real-time sea condition safety threshold value and fishery regulation space constraint; 6.1, constructing a comprehensive scoring function of the risk perception function. According to the method, multi-source heterogeneous data is constructed, a target fish ecological suitability model and a depth time sequence prediction model are combined, the fishing point posterior probability is generated through a Bayesian fusion mechanism, the prediction uncertainty is quantified, and dynamic fishing point recommendation with risk perception and compliance safety is realized.

The invention discloses a post-earthquake railway structure state evaluation method and system based on multi-source data fusion. The method comprises the steps that a finite element model is constructed according to the structure of a regional railway network, a simulation data set is generated, a physical information neural network model is trained, and a proxy model library is formed; acquiring multi-source observation data, and performing inversion according to the Bayesian theory, the structural damage parameters of the physical information neural network agent model and the multi-source observation data to obtain complete posterior probability distribution of the damage parameters; according to the method, statistical characteristics of damage parameters are extracted from posterior probability distribution, probability grading is carried out on the damage degree of the structure, driving constraint suggestions are generated according to the combination of damage probability grading and specifications, and Bayesian inversion time is reduced from several days to several hours through a physical information neural network agent model, so that the evaluation efficiency is improved; the output of the Bayesian method is probability distribution, the uncertainty range of the evaluation result is clearly displayed, and the uncertainty is quantified.

The invention discloses a super-set deterministic weather forecast method and device based on machine learning, and the method comprises the steps: obtaining multi-source meteorological data of a target region, carrying out the meshing of the multi-source meteorological data, carrying out the historical static feature analysis and dynamic feature analysis of the meshing features, obtaining the feature weight of each mode, and carrying out the recognition of the multi-source meteorological data. The method comprises the following steps: constructing a prediction sub-model according to an extreme event, obtaining enhanced numerical prediction data, obtaining posterior probability distribution of grid points through a conditional generative adversarial network and a Bayesian neural network based on the numerical prediction data, a gridding feature and a feature weight, taking a maximum probability value as a deterministic weather forecast, and calculating a confidence interval. And obtaining a joint probability product including wind speed and rainfall joint distribution and the characteristic contribution degree. According to the method, numerical forecasting set products of different mode centers are utilized to fuse probability forecasting information, deterministic weather forecasting is obtained, smoothing of extreme events is reduced, deterministic maximum value output is provided, and meanwhile good interpretability is achieved.

The invention discloses a multi-source three-dimensional geological data fusion and management method and related equipment, and relates to the technical field of geological information, and the method comprises the steps: obtaining standardized geological data; fusing the standardized geological data based on a Bayesian fusion framework; constructing a multi-scale three-dimensional geologic model by adopting a potential field interpolation method based on probability constraints provided by posterior probability distribution; performing uncertainty quantification on the multi-scale three-dimensional geologic model by adopting a Monte Carlo simulation method; and storing the multi-scale three-dimensional geologic model and the uncertainty quantification result to a data management platform conforming to an OGC standard, and providing visualization, query and access of a geologic map layer through a Web service interface. According to the method, the sharing efficiency, the reuse value and the application convenience of the geological result data are greatly improved, and the intelligent level and the decision support capability of geological data analysis are enhanced on the whole.

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.

The invention discloses a full-life-cycle management method for electromechanical equipment of a mountainous road, and relates to the technical field of intelligent traffic infrastructure management, and the method comprises the steps: inputting a standardized equipment operation data set into a Bayesian network evaluation architecture, analyzing the posterior probability of each node under the dynamic constraint of environmental parameters through a multi-level fault propagation path, and carrying out the analysis of the posterior probability of each node; generating an equipment health assessment report; and fusing the equipment control execution state record with historical electromechanical equipment operation data, constructing an equipment full-life-cycle degradation curve through space-time correlation analysis, performing trend feature extraction and signal separation processing, and generating an equipment full-life-cycle analysis report. According to the method, the pertinence and timeliness of the maintenance decision are improved, and the refined monitoring and prediction of the full-cycle operation performance of the equipment are realized, so that a reliable basis is provided for scientifically formulating a maintenance plan, reducing the full-life-cycle cost and prolonging the service life of the equipment.

The invention relates to the crossing field of computer technology and hydraulic engineering, discloses a reservoir dam safety monitoring method and system based on multi-source sensing fusion, and aims to solve the problems of inaccurate safety assessment, early warning lag, high false alarm and missing report rate and the like caused by single data source and loose fusion in the prior art. The method comprises the following steps: deploying distributed optical fiber strain, osmotic pressure, inclination angle and acceleration sensing arrays, and synchronously obtaining sub-meter SAR satellite deformation data and regional hydro meteorological information; performing unified space-time reference alignment on the four types of heterogeneous data; extracting characteristics such as a strain evolution rate, an osmotic pressure deviation degree, an inclination over-limit section and a deformation rate; and constructing a Bayesian network inference engine based on physical constraints, calculating a dam section safety state posterior probability, and triggering three-level early warning according to a threshold value. According to the invention, through intelligent diagnosis driven by multi-source collaborative perception and physical knowledge, the monitoring precision is obviously improved, false alarm and missing alarm are reduced, and minute-level real-time early warning is realized.

The invention provides a Bayesian-based aviation equipment residual life uncertainty prediction method and system. The method comprises the following steps: S1, constructing an observation function equation and a state parameter description equation of residual life of key or important systems, modules and parts of aviation equipment; s2, constructing an agent model; s3, solving joint posterior probability distribution and performing residual life prediction; and S4, according to the prediction probability distribution of the remaining life of the aviation equipment, generating an optimization control instruction or a maintenance instruction for the aviation equipment, and acting the optimization control instruction or the maintenance instruction on the aviation equipment. The method provided by the invention not only provides a theoretical basis for solving the problem of prediction, analysis and prediction of the service life of the aviation equipment, but also clearly defines how information and uncertainty thereof flow and convert between a physical world and a digital world, and provides a brand new thought for prediction of the uncertainty of the residual service life of the aviation equipment.

The application discloses a method for designing and calibrating a detection site of an unmanned aerial vehicle based on high-precision positioning, comprising the following steps: S1, setting a main control point and a standard point; S2, performing static relative measurement and free station measurement, and outputting a reference coordinate data set; S3, introducing a Boolean sand model, calculating accurate three-dimensional coordinate values of the main control point, evaluating residual errors, and generating reference data of the main control point; S4, performing multi-station redundant observation on the standard point, and outputting accurate coordinate data of the standard point; S5, constructing an improved Hamilton Monte Carlo-based Bayesian inference model, introducing a Gaussian mixture prior guide factor, calculating a posterior probability distribution and an extended uncertainty, and generating a calibration data report; and S6, performing independent measurement and comparative analysis, and outputting a final calibration result. The application realizes accurate measurement of point coordinates of a detection site and scientific evaluation of uncertainty, and effectively improves the reliability and robustness of value traceability.

The invention relates to the technical field of fog potential forecasting, and discloses a plain area fog potential forecasting method based on weather typing and meteorological element change, which comprises the following steps: constructing a low-visibility event instance library, and establishing a high-altitude circulation and ground situation typing library; and then matching the real-time forecast field with the typing library to determine a weather background, and extracting meteorological elements such as temperature inversion and quiet wind. The method is characterized in that a movable boundary conceptual model is integrated, an air mass boundary is identified by calculating a horizontal temperature gradient and is quantified into discrete state characteristics representing cold and warm air mass effects, and finally, a weather background, conventional elements and the discrete state characteristics are jointly input into a Bayesian probability model, so that the air mass effect is obtained. And calculating and outputting the posterior probability of each fog potential level. According to the method, the key physical process is quantified and then fused into the statistical model, so that the capturing capability of a specific fogging mechanism is improved, and the forecasting result has high accuracy, strong interpretability and refined probability characteristics.

The application relates to the technical field of intelligent driving and discloses an abnormal environment sensing method and system based on multi-source information fusion, which comprises the following steps: collecting vehicle surrounding environment information and constructing a millimeter wave radar characteristic vector; constructing a convolution network model to obtain a target layer fusion characteristic vector and a fusion target position; splicing to obtain a multi-source fusion characteristic vector; designing a deep network architecture, performing data layer fusion through an attention mechanism to obtain a data layer fusion characteristic vector and an abnormal environment state category probability; constructing Bayesian inference observation data, calculating an abnormal environment state posterior probability, outputting an abnormal environment sensing result, and adjusting parameters of the convolution network model and the deep network architecture based on the abnormal environment state posterior probability. The application effectively breaks through the limitation of a single sensing source, significantly improves the sensing accuracy and reliability of an autonomous driving vehicle on an abnormal environment, and better meets the safe driving demand of autonomous driving in a complex scene.

The invention relates to the technical field of logging evaluation in oil-gas exploration, and discloses a lithology identification method and device based on logging information and electronic equipment. The method comprises the following steps: determining sensitive logging information; preprocessing logging information of the source research area and the target research area; establishing a lithology prediction model Mo based on the convolutional neural network for the source research area; finely adjusting the lithology prediction model Mo based on a small number of samples in the target research area to obtain a lithology identification model Mt of the target research area; calculating a Markov prior probability and a transition probability matrix between different lithologies for the sample set of the target research area; predicting a lithology result of the test set sample of the target research area by adopting a lithology prediction model Mt; using Markov prior probability to correct the predicted lithology result to obtain a posterior probability; and determining a final lithology category. According to the technical scheme of the invention, effective lithology identification can be carried out on a complex lithology reservoir, and the lithology identification precision is high; petroleum geology research and dessert optimization are facilitated.

The invention discloses a terminal anomaly detection system based on a graph neural network, and the system comprises the steps: collecting a terminal side process, a file, a registry, an account, network connection and an external IP, and constructing a terminal heterogeneous behavior graph sequence; executing causal disturbance baseline extraction to generate a causal constraint set; forming a dynamic security semantic meta-path set according to the causal constraint set; generating a node pre-scoring sequence; performing Bayesian online change point detection to generate a state mutation posterior probability sequence, an operation length sequence and a receptive field modulation parameter; generating a node graph anomaly score sequence based on an improved FreeGAD algorithm; constructing an anti-fact normal evolution diagram and generating a verification score sequence; and generating a terminal abnormity alarm result according to the node graph abnormity score sequence, the state mutation posterior probability sequence, the verification score sequence and the operation length sequence. According to the method, collaborative anomaly detection of terminal heterogeneous correlation modeling, time sequence change point feedback, adaptive graph scoring and anti-fact verification is realized.

The invention relates to the technical field of aero-engines, and discloses a cartridge receiver quality problem tracing method, which improves the tracing analysis efficiency and reduces the influence of human subjective factors. A data confidence coefficient formula is adopted to quantify data reliability, so that interference of low-quality data on a traceability result is avoided, and data support is more scientific; through a Bayesian network posterior probability formula, quantitative sorting of problem reasons is realized, manual subjective judgment errors are reduced, and traceability precision is improved; by integrating data of a casing design stage, a processing stage and a detection stage, a'data island 'is broken, a constructed knowledge graph realizes historical experience reuse, and a systematic knowledge accumulation and reuse mechanism is established, so that a historical solution can be quickly called when similar problems appear repeatedly, and the tracing efficiency is improved; through dual verification of an evidence chain and a relation chain, accuracy and interpretability of problem positioning are provided.

The application discloses a bearing-frequency-based target passive tracking trajectory division method, and belongs to the technical field of underwater multi-target tracking of passive sonar.The application aims to solve the problem of poor tracking effect of the existing method in the presence of target trajectory intersection scenes.The application introduces a frequency measurement value into a target state to obtain a bearing-frequency augmented state vector under the condition of passive tracking of a single observation platform in the presence of the underwater multi-target tracking trajectory intersection scenes.Bearing-frequency joint information can improve the discrimination degree of the solution of the posterior probability of the measurement value corresponding to the target, so that the correctness of batch division of the target in the trajectory intersection case is improved.Combining with an RB particle filter, a multi-hypothesis tracking technology and a Kalman smoothing filter technology, good target tracking effect is achieved, and the correctness of the tracking result is ensured.The method can be applied to the technical field of underwater multi-target tracking.

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

The invention provides a carbon emission reduction accounting method and system based on multi-modal uncertainty quantization, and relates to the technical field of carbon emission reduction accounting, and the method comprises the following steps: collecting and preprocessing the multi-modal data of a reconstructed building, and obtaining a preprocessed multi-modal data set; uncertainty modeling is carried out on the preprocessed multi-modal data set, and uncertainty distribution is obtained; based on the preprocessed multi-modal data set and uncertainty distribution, performing inversion by adopting a Bayesian framework to obtain a multi-modal data posterior probability; and performing Monte Carlo simulation on the posterior probability of the multi-modal data to obtain an uncertainty weight, and calculating the carbon emission reduction based on the uncertainty weight. According to the method, quantitative modeling is carried out on the uncertainty of the multi-modal data, the probability transmission and fusion of the uncertainty are realized by using the Bayesian framework, and finally the carbon emission reduction amount accounting result with the confidence interval is output, so that the scientificity and the credibility of the accounting are remarkably improved.

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.

This invention discloses a method for identifying dust components in electronic dismantling plants based on polarization scattering imaging. The method includes: acquiring multi-angle polarization scattering images of dust areas in the electronic dismantling plant using a UAV system equipped with a multi-angle polarization imaging module; calculating Stokes vector parameters based on polarization optics theory to extract the linear polarization degree matrix and polarization angle matrix of the dust scattered light; constructing a multi-dimensional polarization scattering fingerprint feature, fusing statistical features, distribution features, and contrast features to form a polarization scattering fingerprint feature vector; inputting the polarization scattering fingerprint feature vector into a pre-trained deep neural network for classification calculation, outputting the probability distribution of various dust components; and applying a decision function based on Bayesian posterior probability calculation to output the final identification result of the main dust components in the electronic dismantling plant. This invention can provide reliable technical support for environmental monitoring, resource recycling, and safety protection in electronic dismantling plants.

The application relates to a mountain stability evaluation method, device, computer equipment and storage medium, and relates to the technical field of mountain stability. The evaluation method comprises the following steps: dividing a mountain into a plurality of voxels; constructing an adjacency structure graph and a density field posterior probability distribution of the mountain, wherein the adjacency structure graph is constructed by muon data recorded by a muon detector arranged in a grotto, and the density field posterior probability distribution is a probability distribution of all density models of the mountain under the muon data and the adjacency structure graph; sampling from the density field posterior probability distribution according to the adjacency structure graph to obtain a plurality of density field samples of the mountain; obtaining a density value of each voxel according to the plurality of density field samples; and obtaining a stability abnormal area in the mountain according to the density values of the plurality of voxels. The mountain stability evaluation method can accurately determine the stability abnormal area in the mountain, and improves the accuracy of mountain stability evaluation.

The invention relates to a mountain stability evaluation method and device, computer equipment and a storage medium, and relates to the technical field of mountain stability. The evaluation method comprises the following steps: dividing a mountain into a plurality of voxels; an adjacent structure diagram and density field posterior probability distribution of the mountain are constructed, the adjacent structure diagram is constructed through muon data recorded by muon detectors arranged in the grotto, and the density field posterior probability distribution is probability distribution of all density models of the mountain under the muon data and the adjacent structure diagram; sampling from the density field posterior probability distribution according to the adjacent structure diagram to obtain a plurality of density field samples of the mountain; obtaining a density value of each voxel according to the plurality of density field samples; and according to the density values of the voxels, obtaining a stability abnormal region in the mountain. According to the mountain stability assessment method, the stability abnormal area in the mountain can be accurately determined, and the accuracy of mountain stability assessment is improved.

The invention relates to a Bayesian theory-based virtual-real fusion life and reliability evaluation method, which introduces a Bayesian theory to carry out life and reliability evaluation research aiming at the problems of limited sample size, incomplete test data, more uncertainty factors and the like in life evaluation of electronic products. According to the technical scheme, the method comprises the following steps: firstly, dividing prior distribution and sample data according to obtained simulation data and test data; secondly, combining prior distribution with sample information according to a Bayesian theory, and deriving posterior probability distribution of parameters; and finally, comparing and verifying the precision of the fusion evaluation method according to actual test data. The patent mainly realizes effective fusion of electronic product simulation priori knowledge and newly acquired test data, and improves the evaluation accuracy and robustness under the conditions of small samples and high uncertainty.

A system applies Bayesian inference techniques to determine parameters of a camera and an illumination profile for an inhomogeneously-illuminated sample from observation data. The system learns full posterior probability distributions over all parameters involved in the imaging process that works in both high illumination and low illumination regimes.

The application discloses a post-earthquake railway structure state evaluation method and system based on multi-source data fusion, which comprises the following steps: constructing a finite element model according to the structure of a regional railway network, generating a simulation data set, training a physical information neural network model, and forming a proxy model library; collecting multi-source observation data, inverting the complete posterior probability distribution of the damage parameters according to the Bayesian theory, the structure damage parameters of the physical information neural network proxy model and the multi-source observation data; extracting the statistical characteristics of the damage parameters from the posterior probability distribution, probabilistically grading the damage degree of the structure, generating driving constraint suggestions according to the damage probability grading and combining the specifications, and reducing the Bayesian inversion time from several days to several hours through the physical information neural network proxy model, thereby improving the evaluation efficiency; the output of the Bayesian method is a probability distribution, which clearly shows the uncertainty range of the evaluation result and quantifies the uncertainty.

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

The invention discloses a complex stratum exploration method and device based on geological radar and drilling data fusion, and relates to the technical field of geological exploration, and the method comprises the steps: collecting geological radar waveform data and acoustic logging data, obtaining the prior geological information of a work area, carrying out the uncertainty quantification processing, and generating posterior probability distribution; performing Bayesian inversion on the posterior probability distribution and the prior geological information to generate posterior statistics, and generating an interpretation confidence cloud picture based on the posterior statistics; and constructing a spatial uncertainty weighting matrix by using the interpretation confidence cloud atlas, fusing the anisotropy parameters at the drill hole and the anisotropy characteristics of the geological radar data, establishing anisotropy constraint terms of the geological radar data, and generating an anisotropy full-waveform inversion target function. According to the method, fusion of different types of data and quantification of space-time uncertainty are realized, and the underground structure prediction precision and the model reliability are improved.

The invention provides a non-reference damage imaging method based on nonlinear Lamb waves and Bayesian inference, and relates to the field of damage imaging, and the method specifically comprises the following steps: collecting nonlinear Lamb wave signals of a plurality of propagation paths; an enhanced second harmonic signal is obtained through a pulse inversion technology; extracting a flight time feature of the envelope signal; the frequency spectrum of the nonlinear Lamb wave signal is obtained through fast Fourier transform, and nonlinear coefficient damage index features are obtained through calculation; feature level fusion is carried out based on Bayesian estimation; and solving a posterior probability density function of the damage position parameter by using Hamiltonian Monte Carlo sampling, and carrying out damage probability imaging positioning according to posterior probability distribution. According to the technical scheme, the problems that in the prior art, a large number of sensors usually need to be deployed, and the dual requirements for high-precision damage detection and sensor number minimization in engineering practice are difficult to meet are solved.

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.