38 results about "Bayesian filtering" patented technology

This invention relates to the field of hydraulic engineering, and more particularly to a method for safety assessment and optimization design of large-flow aqueduct structures. The method includes: collecting displacement, strain, and temperature monitoring sequences based on a triple probe, completing grouping, coordinate and time registration, and establishing a monitoring baseline; constructing a spatial model including support nonlinearity and fluid-structure boundary conditions, applying loading conditions, and obtaining response predictions; aligning monitoring and prediction in time and location, performing hierarchical Bayesian filtering inversion, and obtaining parameter posteriors and structural health; under the integration of in-service monitoring and accelerated degradation characteristics, using multi-task learning to generate material evolution and lifespan intervals, and automatically generating and simulating optimization schemes under reliability and maintainability constraints. This application forms a closed loop from assessment to design, effectively enhancing data comparability, traceability of uncertain information, and adaptability of the scheme to engineering projects.

The invention relates to the technical field of satellite communication, and discloses a low-orbit satellite communication Doppler frequency offset processing method, system and device and a medium, and the method comprises the steps: separating a pilot signal from a received signal; taking the Doppler frequency as a first system state, taking the received signal as first observation data, and performing Doppler frequency offset estimation on the pilot signal by adopting a frequency offset estimation algorithm to obtain a frequency offset estimation value; taking the Doppler frequency as a second system state, taking the frequency offset estimation value as second observation data, and adopting a frequency offset tracking algorithm to perform Doppler frequency offset tracking on the pilot signal to obtain a frequency offset tracking value; and performing frequency offset compensation on the received signal according to the frequency offset tracking value to obtain the received signal after frequency offset compensation. According to the method, the Doppler frequency offset is subjected to streaming processing through Bayesian filtering in the estimation and tracking stages, the complexity of data storage and calculation is effectively reduced, the data updating rate is increased, and therefore the accuracy of low-orbit satellite data is effectively improved.

The invention discloses an environment situation scanning three-dimensional reconstruction method based on infrared and SLAM, and belongs to the technical field of computer vision and three-dimensional reconstruction. According to the method, thermal radiation information and environment image information of a target environment are obtained through infrared sensing and visual imaging respectively, after feature extraction and matching processing are conducted on the two kinds of information, synchronous positioning and map construction are completed, and environment pose data and environment map data are generated; a sensing uncertainty probability model is established, after multi-source data denoising is completed through Bayesian filtering, feature level fusion is carried out to obtain fusion environment data, and finally a three-dimensional reconstruction model of a target environment is constructed based on the fusion environment data. According to the method, the full-process closed loop of environment situation scanning and three-dimensional reconstruction is realized, the application limitation of single sensing information is made up, the comprehensiveness of environment sensing data and the scene adaptability of three-dimensional reconstruction are improved, and the digital reconstruction requirements in various complex environments can be met.

The invention discloses a digital twin co-evolution method, and belongs to the field of structural health monitoring and prediction. Comprising the following steps: constructing life prediction digital twins; carrying out online load-damage hybrid monitoring and fusion diagnosis; the digital twin co-evolution based on the Bayesian filtering model is driven; collaborative updating of the crack propagation simulation finite element model and the stress intensity factor agent model is achieved; and performing crack propagation and life prediction based on the digital twin updated by co-evolution. Through a multi-model closed-loop co-evolution mechanism, the problem of model misalignment under the influence of a strong uncertain environment is effectively solved, the accuracy and reliability of structural damage diagnosis and life prediction are remarkably improved, and a core technical support is provided for implementing accurate condition-based maintenance.

This invention relates to the field of satellite communication technology and discloses a method, system, device, and medium for processing Doppler frequency offset in low-Earth orbit (LEO) satellite communication. The method includes: separating a pilot signal from a received signal; using the Doppler frequency as a first system state and the received signal as first observation data, performing Doppler frequency offset estimation on the pilot signal using a frequency offset estimation algorithm to obtain an estimated frequency offset value; using the Doppler frequency as a second system state and the estimated frequency offset value as second observation data, performing Doppler frequency offset tracking on the pilot signal using a frequency offset tracking algorithm to obtain a tracked frequency offset value; and performing frequency offset compensation on the received signal based on the tracked frequency offset value to obtain a frequency offset-compensated received signal. This invention uses Bayesian filtering for streaming processing of the Doppler frequency offset during the estimation and tracking stages, effectively reducing the complexity of data storage and computation, increasing the data update rate, and thus effectively improving the accuracy of LEO satellite data.

The present disclosure provides a system (104) and a method (800) for vehicle localization in a tunnel, utilizing sensor data and tunnel-specific features. The system (100) is configured to determine the lateral and longitudinal positions of the vehicle (101) in real time. The lateral position is identified by updating a light probability distribution and applying Bayesian filtering to establish the vehicle's ego lanes. Furthermore, a real-time landmark identifier (ID) for lights is generated based on normalized tunnel features, including distances, light intensity, and tunnel geometry. The longitudinal position is determined by matching the generated landmark ID with predefined map data, enabling robust localization for navigation and thus allowing the vehicle (101) to be located in tunnels.

This invention discloses an intelligent tracking method for maneuvering swarm targets based on deep neural networks. This method incorporates deep learning into a Bayesian filtering framework, extracting the motion characteristics and process noise statistical characteristics of the swarm targets from measurement data using multiple deep neural networks. It then estimates the motion state transition matrix and process noise variance matrix of the swarm targets online, and uses Bayesian filtering to accurately estimate the motion state of the swarm targets' centroid. By modeling the swarm target contour as an ellipse centered at the centroid, the high-precision centroid motion state estimation results improve the contour estimation accuracy, ultimately achieving swarm target tracking. Compared to existing swarm target tracking methods, the proposed method does not require prior information to establish a target motion model, enabling high-precision tracking of non-cooperative maneuvering swarm targets in complex environments where prior information is lacking.

A computer includes a processor and a memory, and the memory stores instructions executable by the processor to associate detected objects from sensor data with tracked objects in a tracking list stored in the memory by executing an association model, determine kinematic states of the tracked objects by executing a Bayesian filter, and update which of the tracked objects are stored in the tracking list by executing a track-management model. The association model includes association weights and outputs probabilities that the detected objects are the same as the tracked objects. An association-model gradient of the association model is computable. The association-model gradient is of the probabilities with respect to the association weights. The Bayesian filter includes Bayesian parameters. A Bayesian-filter gradient of the Bayesian filter is computable. The Bayesian-filter gradient is of the kinematic states with respect to the Bayesian parameters. A track-management gradient of the track-management model is computable.

The invention relates to the technical field of hard capsule polishing, and discloses an automatic polishing system based on hard capsules and a device thereof. A data acquisition module of the system acquires surface characteristic data such as hard capsule surface roughness, stain distribution, capsule size and the like; the data analysis module receives the data and then obtains a polishing demand analysis result through random forest regression analysis; the optimization module adopts a genetic algorithm to optimize parameters and rules of the polishing controller according to the result; the control module carries out fuzzy logic calculation on the polishing amount through particle swarm optimization according to the optimized parameters and rules and converts the polishing amount into polishing control signals; after the execution module receives the signal, the polishing amount constraint condition is adjusted through a near-end gradient method, and integer programming is conducted on the polishing process in combination with a branch and bound method so as to automatically adjust the polishing amount; and the detection module detects surface characteristic data after polishing through Bayesian filtering and feeds back the processed data to the control, data analysis and optimization module so as to adjust a polishing strategy.

The application relates to a danger judgment and active guidance method and system for power construction safety supervision, and belongs to the technical field of power construction safety. The method comprises the following steps: collecting multi-modal data of a construction scene through intelligent glasses; analyzing the data at an edge computing end to obtain a scene semantic category and an interactive behavior of a worker; according to the scene semantic category, dynamically scheduling and hot loading a matched risk identification model set from a model library to identify a risk factor and a three-dimensional position thereof; acquiring a three-dimensional voxelized static danger level base map; mapping the risk factor into a dynamic risk observation point cloud in a voxel space; taking the static base map as a priori and taking the dynamic point cloud as observation evidence, performing recursive Bayesian filtering through a dynamic Bayesian network to generate a dynamic danger level three-dimensional distribution; generating and rendering graded AR early warning information; when a violation trend occurs, generating and rendering a three-dimensional safety path of a risk avoidance area. The method realizes adaptive, accurate and active construction safety supervision.

The present application relates to the technical field of blood oxygen detection, and more particularly to a pulse blood oxygen saturation detection method based on image processing, which introduces a transfer learning strategy and a domain adaptation network, can be adjusted according to the individual physiological parameters of the user, thereby improving the applicability to different populations. In particular for dark-skinned population, the present application significantly reduces the measurement error, reduces the error rate by more than 30%. The present application designs a set of real-time calculation mechanism of current signal quality index Q, and combines the Bayesian filtering model for dynamic calibration, so as to ensure that reliable blood oxygen saturation estimation value can be obtained even under low signal-to-noise ratio condition. When the signal quality decreases, the historical data is compensated for the current detection value through the LSTM network prediction module, so as to further ensure the accuracy and continuity of the result.

The application discloses a skipping counting detection method based on laser radar area recognition, and relates to the technical field of motion monitoring, which comprises the following steps: continuously scanning each effective detection ROI area to obtain original point cloud data, and acquiring time sequence point cloud data through preprocessing; training a multiple hidden Markov model based on the time sequence point cloud data, and independently modeling the state of a rope in parallel, and solving the optimal state sequence of each rope through a Viterbi algorithm; based on the optimal state sequence, applying Bayesian filtering to dynamically optimize cycle parameters, and acquiring a complete counting sequence through interpolation counting; performing event counting statistics on the complete counting sequence, aligning through a time window, mapping the effective detection ROI area, and outputting the independent counting value of each person. Through the parallel independent modeling mechanism of the multiple hidden Markov model, the application realizes accurate separation and recognition of a multi-person skipping scene; through the Viterbi algorithm, the optimal state sequence is solved, and the recognition accuracy and stability are maintained.

A method is for tracking an object using an environment sensor. The object is represented by an object status. The method includes detecting a sensor value of the environment sensor, predicting a future object status of the object, and updating the object status using a Bayesian filter. The updating includes using an artificial intelligence module (“AI module”). The AI module is trained such that the detected sensor value is associated with the object and the object status of the object is updated based on the predicted future object status of the object and the detected sensor value.

The invention discloses a rope skipping counting detection method based on laser radar area identification, and relates to the technical field of motion monitoring, and the method comprises the steps: carrying out the continuous scanning of each effective detection ROI area, obtaining original point cloud data, and obtaining time sequence point cloud data through preprocessing; a multi-hidden Markov model is trained based on the time sequence point cloud data, parallel independent modeling is carried out on the states of the ropes, and the optimal state sequence of each rope is solved through a Viterbi algorithm; based on the optimal state sequence, applying Bayesian filtering to dynamically optimize periodic parameters, and obtaining a complete counting sequence through interpolation counting; and performing event counting statistics on the complete counting sequence, and outputting an independent counting value of each person through time window alignment and effective detection ROI mapping. According to the invention, through a parallel independent modeling mechanism of the multi-hidden Markov model, accurate separation and identification of a multi-person rope skipping scene are realized; the optimal state sequence is solved through the Viterbi algorithm, and the recognition accuracy and stability are kept.

This application relates to a method and apparatus for characterizing the quantity-time relationship of pharmacokinetics of monomeric components of traditional Chinese medicine (TCM). Addressing the problem that traditional methods, which directly utilize noisy experimental data to invert pharmacokinetic parameters of TCM monomeric components, suffer from significant errors, making it difficult to reliably reveal metabolic patterns in different experimental individuals, this application proposes a method using modern statistical parameter estimation techniques to dynamically track and analyze the ADME process of TCM monomeric components. Specifically, the metabolic patterns of TCM monomeric components in vivo are modeled as state variables of a dynamic system, and the measured random experimental data are modeled as noisy observation data. Bayesian filtering techniques are then used to dynamically track and analyze the ADME process of TCM monomeric components, achieving accurate, robust, and reliable characterization of the pharmacokinetic behavior of TCM monomeric components.

The invention discloses a multi-target tracking method and system based on tandem joint probability data association and variational Bayesian filtering, electronic equipment and a medium, and the method comprises the following steps: 1, predicting each target, and calculating the interconnection probability between each measurement and the target; step 2, generating equivalent fusion measurement and equivalent covariance for each target; step 3, performing dynamic correction on the fusion measurement noise covariance of each target; 4, updating and iterating each target by using Kalman filtering, and returning to the step 3 if the number of iterations is less than the set number of iterations; otherwise, storing the optimal estimation value and the posterior error covariance of each target. According to the method, the calculation complexity and the resource consumption are remarkably reduced, and meanwhile, the multi-target state estimation precision and the system robustness are synchronously improved.

The application discloses a low-altitude target tracking method and system based on heterogeneous radar and radio frequency fusion, and belongs to the technical field of target tracking. The application collects radar and radio frequency data, realizes cross-modal feature fusion relying on a prediction network guided by a radio frequency signal, completes trajectory decoding in combination with an attention mechanism with a soft time attenuation constraint, obtains predicted coordinates through residual regression, and after three-dimensional virtual measurement is reconstructed through constraint filtering, inputs a back-end Bayesian filter to obtain a stable tracking track. The application solves the problems that a pure deep learning scheme lacks physical constraints and is poor in real-time performance, and that a traditional fusion method has information barriers and cannot effectively fuse data, can fully play the detection advantages of the two types of sensors, effectively resists low-altitude complex clutter interference, adapts to the high-maneuvering motion characteristics of unmanned aerial vehicles, balances tracking accuracy, running real-time performance and trajectory smoothness, can realize accurate and stable tracking of low-altitude, low-speed and small multi-targets, and is suitable for low-altitude security monitoring, airspace control and other scenes.

Embodiments of the present application disclose a multi-sensor anomaly detection method and device, computer equipment and a storage medium. The method comprises: acquiring signals of multiple sensors to obtain a signal to be detected; using a multi-dimensional time series data interpolation algorithm based on a random forest and an SR algorithm to preprocess the signal to be detected to obtain a preprocessing result; and using a neural network and a Bayesian filter to perform anomaly monitoring on the preprocessing result to obtain an anomaly monitoring result. The method can maintain good anomaly detection results for a long time, and increase the robustness of the model by simulating the possibility of observation measurement values.

A system and method for quantum error correction are disclosed. A stream of redundant quantum bits (qubits) is processed using a multi-stage pipeline to reduce noise and improve fidelity. The system applies a Bayesian filter to model probabilistic errors, a majority voting mechanism to enhance consistency, and an artificial intelligence (AI)-based filter to detect and correct residual errors. The method may include validating accuracy with a dataset and retraining the AI-based filter or updating the Bayesian model when accuracy falls below a threshold. Redundancy is introduced by repeating qubit measurements. The system is designed for integration with quantum communication technologies, including quantum key distribution (QKD).

The invention provides a radar and vision fusion method and device based on binary Bayesian filtering, and relates to the technical field of multi-sensor target fusion. The method comprises the following steps: preprocessing visual acquisition data and radar acquisition data to obtain a visual detection result and a radar plot detection result; filtering invalid radar plots in combination with vehicle motion information, and determining a radar effective plot target; combining a visual detection result with a radar effective trace point target, and determining a first matching probability of each combination in the current period based on a radar and visual target attribute difference weight ratio; carrying out binary Bayesian filtering by taking the first matching probability as a prior probability to obtain a multi-cycle second matching probability; and finally, performing matching tracking on the target combination determined by the second matching probability, and fusing vision and radar detection results. Therefore, the problem of poor visual and radar correlation matching effect is effectively solved, and the accuracy and stability of multi-sensor target fusion are improved.

The application discloses a kind of indoor multi-scene semantic map construction method based on semantic relation graph, comprising: based on laser radar using grid map algorithm constructs 2D grid map;Using binocular camera obtains scene image, obtains depth map by stereo matching and identifies object in environment using trained target detection model, obtains the class and coordinate of landmark, image data adopts bayesian filtering technology to combine prior information and carries out scene classification, obtains the semantic label of current scene, using the semantic label obtained using the semantic map of occupancy grid method;Based on target identification dataset constructs prior relation graph, fuses space relation knowledge graph and prior relation knowledge graph to construct graph layer, obtains multi-scene semantic map.The semantic relation map of the present application based on deep learning and knowledge graph construction stores a large amount of prior knowledge, gives map comprehensive and accurate environmental information, can realize quick search in multi-scene rescue and daily service.

The invention relates to the technical field of power transmission line monitoring, and discloses a power transmission line sag observation and wind load real-time monitoring system, which comprises a sensor data acquisition module, a synchronization module, a data processing module, a monitoring and early warning module and a remote control module, the method comprises the following steps: monitoring sag and wind load changes of a power transmission line in real time through multiple sensors, ensuring time synchronization of data by using a synchronization module, constructing a mathematical model between the wind load and the sag changes, and carrying out real-time analysis, identification and prediction of potential risks based on the model. Through multi-sensor data fusion and a variational Bayesian filtering algorithm, the monitoring precision of the sag and wind load change of the power transmission line is improved, potential safety risks can be recognized in real time, safety control measures such as current limiting, load transfer or power failure are taken in time through an automatic early warning mechanism, and the safety of the power transmission line is improved. Therefore, the operation safety and stability of the power transmission line are effectively improved.

The application discloses a kind of unmanned aerial vehicle cluster target joint tracking method, first, according to the three interactive rules of unmanned aerial vehicle cluster separation, aggregation and adjustment, the cooperative movement of unmanned aerial vehicle cluster is effectively described using stochastic differential equation;Second, based on the definition of new distance measure of target motion characteristics, the division of cluster in field of view is realized using DBSCAN clustering algorithm based on the measure;Finally, in the framework of Bayesian filtering, DBSCAN clustering algorithm is combined with JPDA algorithm, and the cluster division and target joint state estimation in clutter environment are realized simultaneously;In simulation experiment, the behavior of cluster is simulated and state estimation is carried out, and the results show that the algorithm can effectively describe the interactive motion process of unmanned aerial vehicle cluster target, and the tracking algorithm can effectively track the joint tracking of cluster target, which proves that the method has the characteristics of good tracking and classification effect.

The invention discloses a multi-modal perception-based intelligent error correction and feedback system for sports actions, which relates to the technical field of sports training assistance and comprises a data interface module, a storage module, a capability estimation module, a capability updating module and a decision and instruction transmission module, the ability estimation module calls a latent variable observation model in the storage module to calculate a real-time ability observation vector, the ability updating module updates an individual ability model through online Bayesian filtering to obtain an individualized ability variable, and the decision making module generates an individualized action tolerance domain based on the variable and individual attribute data; an error correction instruction is generated when the comparison action data exceed an allowable domain, and the instruction transmission module sends the error correction instruction to a receiving end to prompt a user to adjust; the method has the advantages that the method can dynamically adapt to individual differences and real-time physiological state changes, the misjudgment phenomenon is reduced, and more accurate error correction feedback is provided.

The invention relates to the technical field of numerical control machining, and discloses a reinforcement learning-based numerical control machining process parameter adaptive optimization method and system, an accurate sensing system of a machining state is established through a multi-source sensor acquisition and Bayesian filtering fusion technology, and a high-quality information basis is provided for intelligent decision making. Meanwhile, the measurement uncertainty is quantified to support risk assessment. Secondly, the meta-learning technology enables the strategy network to have generalization ability of cross-material, cross-cutter and cross-process, adaptive adjustment can be completed only by a small amount of trial cutting when a new task is started, the product remodeling period is remarkably shortened, the flexible response ability of a production line is improved, and flexible balance among efficiency, quality and cost can be achieved according to production requirements; the upper layer strategy plans cutter full life cycle management, the lower layer strategy executes real-time parameter adjustment, and collaborative optimization of multiple time scales is achieved.

The application discloses a space target classification method based on physical behavior characteristics, comprising the following steps: constructing a typical target model characteristic training data set according to in-orbit measurement space target historical data; collecting physical characteristic parameters of the space target to obtain space target characteristic sample information; performing principal component analysis on the space target characteristic sample information to establish a characteristic vector sequence; pre-judging the space target type through the physical characteristic vector sequence to construct a space target classification model; training the classification model by using the training data set; calculating the space target type likelihood probability by using the behavior characteristic vector sequence and outputting to a space target type sequence; performing Bayesian filtering on the space target type sequence and outputting the filtered space target type as the space target type. The application realizes effective and accurate classification of the space target type, can continuously update the identification accuracy according to the in-orbit identification condition and has strong practicability.

The invention discloses an H-shaped steel rolling uncertainty self-adaptive dynamic optimization method. The method comprises the following steps: constructing probability digital twin bodies containing structured uncertainty characterization in an H-shaped steel rolling process; a domain randomization method based on confrontation disturbance is adopted, and a reinforcement learning robust control strategy is trained in the probabilistic digital twinborn body; in the actual rolling process of the H-shaped steel, real-time sensing data are obtained, digital twinborn parameters are dynamically updated through recursive Bayesian filtering, and a pre-training strategy is adjusted online through a self-adaptive strategy optimization framework; and judging whether the real-time sensing data reaches a termination condition or not, if not, returning, and if yes, outputting an optimization result. According to the method, an adversarial domain randomization training strategy is adopted, so that the reinforcement learning strategy actively learns to cope with the worst working condition disturbance, and the robustness of the control strategy is improved. In the face of a real and uncertain rolling environment, the problems that in a traditional method, the occurrence rate of folding cracks is high and the yield is low due to model mismatching are solved.

The application provides a shearer relative position positioning system and method based on discrete Bayesian filtering, and belongs to the technical field of shearer positioning; the application solves the problems of frame loss and frame jump existing in the positioning result of the infrared sensing technology used in the current shearer positioning; the application comprises a shearer, an inertial navigation positioning device, an infrared signal transmitter and an infrared signal receiver; the inertial navigation positioning device sends the processed real-time angular velocity and real-time acceleration data of the shearer to a crossheading master computer; an electro-hydraulic controller on a different hydraulic support performs algorithm processing on the received real-time shearer infrared position information; after the crossheading master computer receives the latest shearer position information, the crossheading master computer combines the inertial navigation positioning change between the last shearer position information and the latest shearer position information, adopts a discrete Bayesian filtering algorithm, and calculates the shearer relative position fusion positioning information; the application is applied to shearer position calculation.

This application discloses an arc fault detection method, apparatus, computer-readable storage medium, and system. The method includes constructing a stochastic dynamic system model of the arc fault in the circuit to be detected, the stochastic dynamic system model including the state equation and observation equation of the arc fault; acquiring sampled data of the circuit to be detected in the current observation period, extracting features from the sampled data to obtain a multidimensional observation vector of the current observation period that matches the observation equation; based on the stochastic dynamic system model and the multidimensional observation vector of the current observation period, using a Bayesian filter as a framework, performing state estimation through a continuous-discrete extended Kalman filter to obtain the posterior state vector of the current observation period; and determining whether a fault arc exists in the current observation period through a preset fault decision scheme. This application provides a novel implementation path for high-precision, high-reliability, and low-cost AC series, parallel, and grounding arc fault detection.

The invention discloses a dynamic incremental three-dimensional reconstruction method for a long-distance approaching section of an asteroid, and belongs to the field of deep space exploration. The method comprises the following steps: establishing an ellipsoid model of an asteroid, performing contour extraction on a two-dimensional image of the asteroid obtained by a detector, and initializing the ellipsoid model by using a contour back projection result; performing layered voxelization on the ellipsoid model, performing gridding processing on a three-dimensional space where the ellipsoid is located, performing uniform sampling on an inner layer and an outer layer, and performing voxelization processing on the ellipsoid according to an ellipsoid mask; respectively representing the occupancy state of each voxel by using probabilities, establishing an observation model of the voxels, and carrying out frame-by-frame Bayesian filtering on the voxel probabilities by using a plurality of frames of contour images to obtain the voxel occupancy state of multi-view image verification; voxels with the probability larger than a threshold value are reserved, the voxels are spliced, and a three-dimensional reconstruction result of the target asteroid is obtained; and dynamically updating the three-dimensional model by using the newly added image to realize real-time three-dimensional reconstruction of the long-distance approaching section of the asteroid.