375 results about "A posteriori probability" patented technology

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 embodiment of the invention provides a dynamic risk assessment method and system based on perception and causal reasoning, and belongs to the technical field of automatic monitoring. The method comprises the following steps: acquiring a video data stream in real time and analyzing the video data stream into a structured dynamic visual scene representation; through a neural network model used for executing cross-modal semantic alignment, mapping the dynamic visual scene representation into activation evidence of nodes in a pre-constructed causal knowledge graph in a multi-modal embedding space, the nodes of the graph being risk events or risk factors; and performing probabilistic reasoning on the map based on the activation evidence to calculate the posterior probability of the node corresponding to the to-be-predicted risk event. According to the method, the problems that visual perception and causal reasoning cannot be effectively integrated and the evaluation result lacks interpretability in the prior art are solved, obstacles between visual information and abstract causal concepts are overcome by constructing a perception-mapping-reasoning technology closed loop, and the accuracy, transparency and credibility of risk evaluation are remarkably improved.

The invention provides a blind image super-resolution reconstruction method and system, and belongs to the technical field of image reconstruction, and the method comprises the steps: obtaining a damaged low-resolution image; processing the obtained damaged low-resolution image by using a pre-trained diffusion super-resolution model to obtain a super-resolution image; wherein the diffusion super-resolution model constructs a blind super-resolution problem under a maximum posterior probability framework, and is expanded along with the inverse process of the de-noising probability diffusion model. According to the method, the blind degradation setting problem in single image super-resolution is solved, optimization based on the maximum posterior probability is integrated into DM, joint distribution of low-resolution observation, high-resolution data and a degradation kernel is constructed to provide prior information of the data and the kernel, and the blind SR problem is solved by expanding the maximum posterior probability method and a reverse process.

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 communication fault diagnosis method and device based on a power system, equipment and a medium. The method comprises the following steps: S1, collecting and outputting multi-source data; s2, preprocessing the multi-source data; s3, performing topological constraint attention mechanism weighting processing on the data matrix; s4, performing dynamic threshold calculation on the fusion feature matrix; s5, positioning the abnormal node list; s6, calculating to obtain a posterior probability; s7, matching the rule base to obtain a standardized disposal instruction; according to the method, the communication traffic is collected through the compressed sensing collection algorithm, the line impedance parameters are integrated into the observation matrix in combination with the topological parameters, key node data are collected preferentially, and the effective data acquisition rate is increased; the accuracy of switch false alarm identification is improved by outputting a time-space unified fusion feature matrix; the computing resources are focused to the abnormal nodes and neighborhoods thereof through the abnormal node mask matrix, so that the positioning precision of the aged equipment is improved, and the judgment omission rate caused by switch aging is prevented from being improved.

The invention relates to a direct current charging pile fault diagnosis and repair method and device and a storage medium. The method comprises the following steps: carrying out Hilbert transformation on voltage, current and temperature parameters of a charging pile to obtain envelope data of a multi-channel analysis signal; performing frequency domain conversion and maximum posterior probability matrix decomposition on the envelope data to obtain a frequency spectrum feature vector; inputting the spectrum feature vector into a dual-control law observer with a continuous item and a discontinuous item for state tracking to obtain a system residual matrix; performing threshold detection and accumulative error integral operation on the system residual matrix to obtain fault feature data; and performing lambda consensus iterative operation on the fault feature data to obtain a control parameter compensation scheme. According to the method, the recognition accuracy of fault features is improved, and the optimality and reliability of a repair strategy are ensured.

The invention belongs to the technical field of communication, and particularly relates to a multiplication active code decoding method based on an SO-ORBGRAND decoder. The SO-ORBGRAND decoder outputs soft information by calculating a correct posterior probability of a conjecture code word sequence, and high-performance decoding is realized through soft information exchange and optimization processing between the row decoder and the column decoder. In the iteration process, a method for judging the validity of the code word through the check matrix is designed by utilizing the polarization characteristic of the multiplication active code, so that the iteration is supported to jump out in advance to reduce the complexity. The invention provides a multiplication active code decoding algorithm based on an SO-ORBGRAND decoder, and compared with various non-system multiplication active code decoding algorithms and the performance of traditional long-code polarization codes, the method has remarkable advantages in error correction performance; a method for judging the validity of the code word through the check matrix by utilizing the polarization characteristic design of the multiplication positive code is provided, the complexity is reduced while the judgment success rate is improved, and the implementation overhead is reduced.

The invention relates to the technical field of intelligent substations, and discloses a safety measure checking method and system for an intelligent substation. The method comprises the steps of collecting multi-source real-time monitoring data of a target intelligent substation, and performing edge federation aggregation on the multi-source real-time monitoring data to obtain collaborative feature data; constructing a dynamic topological graph of the target intelligent substation based on the collaborative feature data, and performing short-term fault prediction on the target intelligent substation to obtain a short-term fault prediction result; performing probabilistic reasoning on each short-term fault event according to the fault probability propagation model to obtain a cascading fault posterior probability of the corresponding short-term fault event; performing multi-objective optimization sorting on each safety measure strategy to obtain a safety measure priority sequence; and performing simulation verification on the safety measure priority sequence according to the digital twin to obtain a check report. According to the method, a complete closed loop from risk analysis, rapid verification to strategy optimization is formed, and efficient and intelligent technical guarantee is provided for safe and stable operation of the intelligent substation.

The invention discloses a methane column concentration inversion method and system based on a physically-driven variational auto-encoder, relates to the technical field of greenhouse gas monitoring, and is used for solving the technical problem of low inversion reliability of an existing data-driven methane column concentration inversion mode. The methane column concentration inversion method based on the physical driving variational auto-encoder comprises the following steps: pre-training a decoder module through a loss function containing Jacobian matrix consistency constraint, and freezing network weights of all layers of the decoder module; taking the observation spectrum as the input of the encoder module, outputting posterior probability distribution by the encoder module, sampling any concentration value, and inputting the concentration value into the decoder module for forward simulation to generate a simulation spectrum corresponding to the concentration value; when the difference between the simulated spectrum and the observed spectrum is minimum, determining the optimal methane column concentration as an inversion encoder, and inputting the observed spectrum into an encoder module; and outputting the mean value and the standard deviation of the posterior probability distribution.

The application provides a polar code soft input soft output decoding method, a decoding device and a communication device; the decoding method comprises the following steps: (S1) performing fast serial cancellation list decoding under dynamic frozen bit constraint for all special nodes constituting a polar code; the types of the special nodes comprise rate zero, rate one, repetition and single parity check; (S2) calculating the approximation of the sum of the posterior probabilities of the legal code words not accessed under each special node after decoding; (S3) using the approximation of the code book probability obtained in the step (S2) to calculate the bit level soft information of the output polar code. The low-latency polar code soft input soft output decoding method provided by the application improves the parallelism by realizing fast soft output decoding based on the Rate0, Rate1, REP and SPC nodes, and significantly reduces the decoding latency without losing the soft output precision compared with the SO-SCL decoding algorithm.

The invention discloses a finite geometric code decoding method and system based on a generalized check matrix, and relates to the communication technology, and the method comprises the following steps: constructing a corresponding generalized check matrix according to an algebraic structure of a multi-step large number logic decodable finite geometric code; sending a finite geometric code sequence, transmitting the sequence after coding and BPSK modulation, and receiving a posterior probability log-likelihood ratio sequence by a receiving end; and carrying out iterative decoding on the finite geometric code received by the receiving end based on the constructed generalized check matrix. According to the invention, a new generalized check matrix is constructed according to the algebraic structure of the finite geometric code, and multi-step large-number logic iterative decoding is carried out on the finite geometric code based on the matrix, so that the iterative decoding performance of the multi-step large-number logic decodable finite geometric code is improved.

The invention provides a short-distance high-speed optical fiber communication equalization method and system, and the method comprises the steps: building a Gaussian mixture model for an optical signal subjected to the quadrature amplitude modulation of a set order, and completing the parameter initialization of the Gaussian mixture model through a particle swarm optimization algorithm of which the inertia weight is firstly increased and then decreased; the method comprises the following steps: inputting data features of two dimensions of an in-phase component and an orthogonal component of data into a Gaussian mixture model, then solving parameters of the Gaussian mixture model by using an expectation maximization algorithm, and determining the membership degree of each data point and a clustering label corresponding to each unary Gaussian distribution through the parameters; and through the maximum posterior probability, the data are distributed to the corresponding clustering labels, and nonlinear equalization is completed. According to the method, the memory property of signal nonlinear impairment can be accurately captured, the nonlinear clustering precision is improved, and the calculation complexity of a nonlinear clustering model is reduced under the same clustering effect.

Methods, systems, and apparatus for enhanced decoding using in-phase and quadrature information. In one aspect, a method includes obtaining in-phase and quadrature values for multiple measurement operations in a quantum error correction code for a quantum computation; classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values; generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes; assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph; and executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation.

The invention discloses a Bayesian network-based rainstorm-mountain torrent disaster chain trigger threshold identification method, which comprises the following steps: according to a preset fuzzy character string matching algorithm and a preset text similarity algorithm, carrying out cleaning processing on extracted historical disaster situation texts in sequence to obtain processed disaster situation texts; performing feature extraction on the processed disaster text to obtain key disaster features; constructing a target Bayesian network topology of the rainstorm-mountain torrent disaster chain; discretizing each node variable in the target Bayesian network topology to obtain a state value of the node variable; the prior probability of the node variables is calculated, and the conditional probability of the causal relationship between the nodes is calculated according to the prior probability and a preset application expectation maximization algorithm; calculating a target posterior probability that the first disaster triggers the derived disaster; and according to a preset posterior probability critical value and the target posterior probability, inverting the intensity of the first disaster, and determining an intensity threshold value of the derived disaster triggered by the first disaster.

This invention discloses a low-complexity decoding method for LDPC-Hadamard codes based on a prototype diagram, belonging to the field of digital signal processing. The implementation method is as follows: information bits are LDPC encoded, and then the constraint relationship of the check nodes of the LDPC code is encoded into Hadamard code to obtain the prototype diagram of the LDPC-Hadamard code; the encoded codewords are transmitted and received through a channel, and the PVN and HCN nodes are initialized with the received information; Hadamard decoding is performed using a symbol-by-symbol maximum a posteriori probability decoding algorithm, and DFHT approximation is used to reduce decoding complexity; the decoded information is transmitted to the PVN nodes; the updated HCN node information is summed with the information from the previous iteration, and the summation result is used to determine the decoding; a decoding decision is made on the PVN information, and decoding is performed. This invention combines LDPC encoding, Hadamard encoding, and Max-Log approximation calculation methods to reduce decoding complexity and enhance the stability and reliability of the communication system.

The invention discloses a tomography reconstruction method based on a Gaussian process, and relates to the field of data analysis, and the method comprises the steps: dispersing a physical quantity region of plasma into N rectangular grids; establishing a forward modeling calculation model of the multi-dimensional measurement data according to the calculation model of the physical quantity, the to-be-reconstructed physical quantity and a preset data error; establishing a prior probability about to-be-reconstructed physical quantity distribution by using a non-stationary Gaussian process with a hierarchical model; when the data error meets independent normal distribution, establishing a conditional probability about multi-dimensional measurement data distribution by adopting multi-dimensional normal distribution; the condition probability and the prior probability are combined, and the posterior probability of the physical quantity distribution to be reconstructed is determined; according to the method, a covariance function is established, hyper-parameters in the covariance function are optimized, a gradient method is utilized to maximize the probability about the hyper-parameters, the maximum probability value of the hyper-parameters is determined, and under the maximum probability value, the maximum posterior probability estimated value and the confidence interval of the physical quantity to be reconstructed are determined through the posterior probability.

The invention discloses a bearing fault diagnosis method based on time-frequency double-flow complementation and adaptive gating fusion, and the method comprises the steps: collecting an original vibration signal of a bearing, segmenting an original vibration signal sequence into a plurality of sample segments with fixed lengths, and forming an original data set; dividing the original data set into a training set and a test set by adopting a physical segmentation strategy based on a time axis; constructing a time-frequency double-flow feature extraction module, and extracting a time-domain feature vector and a frequency-domain feature vector; constructing an adaptive gating fusion module to dynamically generate a gating weight, and performing weighted complementary fusion on the time domain feature vector and the frequency domain feature vector to obtain a fused fault feature vector; constructing a classification decision network to map the fusion fault feature vector to a decision space, and optimizing model parameters by using a label smooth regularization loss function and a cosine annealing strategy; and inputting the test set into the trained fault diagnosis model, calculating posterior probability distribution of a sample belonging to each fault category, and outputting a bearing fault diagnosis result.

Systems and methods are provided for detecting and tracking objects. A first set of observations for a plurality of locations includes a probability that a value associated with the location is consistent with local background. A second set of observations includes a probability that the value associated with the location is not consistent with local background. A first probability tensor representing probabilities for each of a plurality of states for each of the plurality of locations is generated from the first and second sets of observations. The first probability tensor is updated according to a second probability tensor representing the probabilities for each of the plurality of states in a previous time step. It is determined that a target is present in the region of interest when a posterior probability meets a threshold value.

The invention discloses a household energy centralized management method and device, electronic equipment and a storage medium. The method comprises the following steps: collecting real-time observation data of a family environment to construct a multi-dimensional context feature vector; generating a dynamic state transition probability matrix and emission parameters of the hidden Markov model through a dynamic parameter mapping mechanism, and calculating initial posterior probability distribution of a family activity state by using a forward algorithm; when the information entropy of the probability distribution exceeds a threshold value, executing an active state exploration strategy, issuing an optimal perturbation instruction and correcting a posterior probability based on an active response observation evidence so as to determine a final family activity state; and based on the state loading control constraint set, solving a real-time optimization objective function containing energy efficiency and comfort penalty, and generating an equipment control instruction. According to the method, the uncertainty of state judgment is reduced by combining a dynamic model and an active exploration mechanism, and household energy cost optimization is realized on the premise of ensuring comfort.

Disclosed are a transceiver decoding method and system based on protograph differential chaos shift keying (DCSK), the method including acquiring an information bit sequence, performing a coded modulation on the information bit sequence via a protograph DCSK transmitter, and outputting a plurality of modulated symbols; inputting each modulated symbol into a wireless channel for channel interference, and outputting a received symbol corresponding to each modulated symbol; using a target extrinsic information-aided network to determine a target a-posteriori probability vector corresponding to each received symbol; inputting each target a-posteriori probability vector into an a-priori calculation decoder for decoding, and outputting an initial decoded bit sequence; and determining a target decoded bit sequence according to the initial decoded bit sequence and a preset check matrix. The technical problem of lower decoding accuracy caused by the existing transceiver decoding methods designed for a DCSK system is solved.

The invention provides a signal detection method for a multi-antenna orthogonal time-frequency-space sparse code division multiple access system, which is characterized in that dimensionality reduction is carried out on time domain and time delay Doppler domain signals by utilizing sparsity of a sparse code division multiple access codebook, and prior distribution of the time domain signals is approximated by using Gaussian distribution. Preliminary detection is realized by acquiring cavity distribution of a signal in a time domain, and the posterior probability of the signal is calculated by using a sparse code division multiple access codebook in a time delay Doppler domain. Based on a transformation relation in a multi-antenna orthogonal time-frequency-space sparse code division multiple access system, information exchange between a time domain and a time delay Doppler domain is realized. The sparse structure of the time domain matrix in the multi-antenna orthogonal time-frequency-space sparse code division multiple access system is utilized, and the complexity of matrix inversion in the algorithm is reduced. By using the method, the complexity of algorithm implementation can be reduced while the signal detection performance is ensured by utilizing the dual sparsity of the multi-antenna orthogonal time-frequency-space system and the sparse code division multiple access codebook.

The invention discloses a partial discharge detection method and device and electronic equipment, and relates to the field of power failure detection. The method comprises the following steps: acquiring M groups of partial discharge data of power equipment through M acquisition strategies; performing screening and dimension reduction on the features of each group of partial discharge data through a target verification strategy to obtain M groups of feature sets; determining a partial discharge detection result according to each group of feature sets to obtain M partial discharge detection results; converting each partial discharge detection result into a piece of posterior probability distribution data, obtaining M pieces of posterior probability distribution data in one-to-one correspondence with the M partial discharge detection results, and fusing the M pieces of posterior probability distribution data to obtain target probability distribution data; and determining a target partial discharge type and target discharge intensity of the power equipment according to the target probability distribution data. According to the invention, the problem of insensitive detection or wrong diagnosis of various defects in partial discharge detection by using a single detection means in the prior art is solved.

The embodiment of the invention discloses a non-contact multi-parameter elbow joint fault diagnosis method and device. Collecting multi-parameter original signals of the area where the elbow-shaped joint is located in the same time reference and the same physical microenvironment; performing denoising, zero offset correction and filtering on the multi-parameter original signal to obtain a pre-processed multi-parameter signal; performing compensation processing on the preprocessed multi-parameter signal by applying a space-time coupling compensation matrix; performing feature extraction on the compensated multi-parameter signals to obtain feature vectors, and calculating response time lag between channels of the parameter signals; calculating a weighted time distance between the typical mode of the current observation stage and each fault stage, and mapping the weighted time distance to a preset fault evolution chain, so as to calculate a posterior probability of the typical mode of each fault stage through the fault evolution chain; and according to a comparison result of the posterior probability and a preset fault threshold value, outputting a fault diagnosis result of the area where the elbow-shaped joint is located and a confidence coefficient corresponding to the fault diagnosis result.

The invention provides a speed limit information fusion method and device, and the method comprises the steps: obtaining map speed limit information and map speed limit confidence based on a map navigation system; acquiring image data in front of a road, and recognizing speed limit information in the image data through a speed limit recognition model to obtain camera speed limit information and camera speed limit confidence; taking the map speed limit confidence coefficient and the camera speed limit confidence coefficient as prior probabilities, and obtaining a posterior probability through a Bayesian algorithm based on the prior probabilities and the conditional probabilities; and the final speed limit information is determined based on the posterior probability, the map speed limit information and the camera speed limit information, so that the accuracy and reliability of the speed limit information are improved.

The invention provides a complex industrial process operation state evaluation method based on a local slow feature Siamese graph convolutional network. The method comprises the following steps: firstly, establishing a state evaluation offline model; then carrying out operation state evaluation by using online data; process data Xt and Xt-1 are obtained through data collection, and standardization processing is carried out on the process data Xt and Xt-1; and performing data division on the standardized data by using a sliding window to obtain a time sequence with the length of W. Converting the time sequence data into graph structure data through the maximum information coefficient; inputting the process data matrix and the graph structure data matrix into an operation state evaluation model, performing feature extraction on potential features of the graph structure data at two adjacent moments, constraining the potential features by using slow features, and finally obtaining posterior probabilities of the online data belonging to different operation state evaluation grades, the final evaluation result is the state level corresponding to the maximum posterior probability at the current moment; according to the method, a more accurate process operation state evaluation model can be realized.

The invention discloses a multi-dimensional student physical health monitoring method and system, and relates to the technical field of health monitoring, and the method comprises the steps: collecting data to construct a signal subset, carrying out time-frequency decomposition to construct a time-frequency graph matrix, carrying out autoregression modeling to extract an observation matrix and a design matrix, and constructing a frequency feature vector; according to the frequency characteristics of different frequency bands, calculating contribution factors and determining a dynamic health score, calculating a posterior probability of a health state based on a conditional probability of the health score, and calculating a frequency band offset to construct an abnormal frequency band set. According to the method, a health assessment model is constructed by using an observation matrix and a design matrix, so that signal values in each row are associated with lagging historical values, the time dependence of the signals can be captured, the dynamic characteristics of the health signals are reflected, and the mean value, variance, skewness and kurtosis are calculated based on PSD. And high-dimensional spectrum information is concentrated into a feature vector of a fixed dimension, so that the data complexity is reduced, and subsequent classification and health state analysis are facilitated.

The invention discloses a single-phase earth fault line selection method based on double-layer parameter adaptive optimization, and the method comprises the following steps: collecting three-phase current corresponding to each feeder line of a target system after the single-phase earth fault of the target system occurs, and carrying out the calculation to obtain zero-sequence transient current; inputting the zero-sequence transient current into a self-adaptive optimization decomposition model for self-adaptive decomposition to obtain an intrinsic mode component of a corresponding feeder line; extracting transient characteristics based on the intrinsic mode component and constructing a transient characteristic matrix; and inputting the transient characteristic matrix into a pre-trained optimization mechanism fault line selection model, outputting a fault posterior probability corresponding to each feeder line, and determining a fault feeder line according to the fault probability. According to the invention, double-layer parameter adaptive optimization is used for carrying out joint optimization on key parameters of a decomposition model and key parameters of a line selection model, so that effective feature extraction and reliable discrimination can still be realized under the conditions of complex zero-sequence transient current high-frequency components and unclear features caused by the access of the distributed power supply; therefore, misjudgment and missed judgment are reduced, and the fault line selection accuracy and robustness are improved.

The invention discloses an uncertainty reasoning state scoring and early warning system for gear machining equipment, and relates to the technical field of gear machining monitoring. Data are collected in real time through an industrial Ethernet, time synchronization is realized based on an IEEE1588PTP protocol, and a data cleaning sub-module is designed to repair an abnormal value and keep a record; the method comprises the following steps of: extracting 128-dimensional potential features of a vibration signal by adopting a variational auto-encoder, fusing a temperature drift coefficient and a current harmonic component, quantifying feature uncertainty through Monte Carlo dropout, and marking low-reliability features; and constructing a Bayesian network containing 5 state nodes and 12 observation nodes, calculating a posterior probability through a Markov chain Monte Carlo algorithm, dynamically adjusting a conditional probability table, and fusing multi-feature scores. According to the method, the state evaluation accuracy and the early warning timeliness are improved, the resource consumption is optimized, full-link monitoring is realized, the fault handling efficiency is enhanced, and reliable support is provided for precision manufacturing.

Systems and methods for processing bit-interleaved coded modulation (BICM) signals from a BICM transmitter to generate information bit estimates of information in the BICM signals, including a decoder to generate the information bit estimates of the information in the received BICM signals and a symbol a posteriori probability (APP) generator to generate first symbol a posteriori probabilities (APPs) by processing the BICM signals based on Euclidean distances derived from the BICM signals, and further based on symbol probability log-likelihood ratios (SPLLRs) provided to the symbol APP generator by an extrinsic-information-based symbol probability log-likelihood ratio (SPLLR) generator. The SPLLR generator generates the SPLLRs directly from extrinsic information based on updated symbol APPs output from the decoder, without converting the extrinsic information into log-likelihoods (LLs), and the decoder generates the information bit estimates based on the first symbol APPs output from the symbol APP generator.

The invention relates to the technical field of stereoscopic vision, and discloses a synchronous speed visual matching method and system, electronic equipment and a storage medium, and the method comprises the steps: synchronously collecting a stereoscopic video sequence with a predefined frame rate; executing multi-target hybrid tracking and motion induction detection, and outputting target motion information including position and velocity vectors; extracting hierarchical motion features of the target from continuous multiple frames of the stereoscopic video sequence, and performing unified space-time coding; under geometric constraints of stereoscopic vision, scale cosine similarity, direction similarity and trajectory consistency measurement are calculated and serve as observation evidences to be input into the probabilistic reasoning model for fusion, and a posterior probability representing matching reliability is output; the weight distribution of the speed similarity and the direction similarity is adjusted according to the motion characteristics of the targets in the scene, and the stable corresponding matching relation between the left view target and the right view target is established. According to the method, high-time-resolution information can be utilized, motion features and geometric constraints can be effectively fused, and the method has self-adaptive capacity.