1084 results about "Estimation result" patented technology

The invention relates to the technical field of bolt tightening, and provides an intelligent bolt tightening control method and system based on axial force measurement. Comprising the steps of collecting bolt identification and process parameter data, collecting temperature measurement data, and obtaining sound velocity compensation parameters and elastic modulus compensation parameters. And zero-load preloading data are collected and processed to generate zero-load baseline data, and a self-calibration model is obtained. Axial force data and ultrasonic echo data are collected, and an axial force fusion estimation result is obtained. And comparing an axial force fusion estimation result with target axial force data. And when in the target interval, fine twisting control is carried out based on model predictive control and a micro-stepping strategy, and the controllable damping unit is driven to carry out energy absorption and stopping. And acquiring an axial force fusion estimation result when the target axial force is reached, processing the data in the holding stage to obtain an axial force rebound check result, and if the axial force rebound check result exceeds the limit, performing secondary twisting control. According to the scheme, refinement and traceability of the bolt tightening process are achieved.

The invention provides a coherent signal arrival direction estimation method and device based on a deep convolutional network, and belongs to the field of array signal processing. The method comprises the following steps: receiving a to-be-detected signal containing a coherent signal by using a uniform linear array antenna to obtain an array receiving data matrix and extract a covariance matrix; forming an input feature vector by right upper triangular elements divided from a diagonal line in the covariance matrix, inputting the input feature vector into a covariance estimation model formed by a deep convolutional network, obtaining an estimation value of the right upper triangular elements under an ideal incoherent condition, and reconstructing the estimation value to obtain a covariance matrix estimation value; and performing characteristic decomposition on the covariance matrix estimation value, and generating a spatial spectrum by using a MUSIC algorithm to obtain an estimation result of the signal arrival direction. According to the method, the noise-containing mixed signal covariance matrix is mapped into the ideal incoherent noise-free signal covariance matrix through a physical constraint supervised learning framework, so that the estimation precision and robustness of the MUSIC algorithm in a coherent scene are improved.

The invention discloses a DOA (Direction of Arrival) estimation method and system based on a deep complex value convolution attention residual network, belongs to the technical field of array signal processing, and solves the technical problems of low precision and poor robustness of the existing DOA estimation method under the severe conditions of low signal-to-noise ratio, limited snapshot number and the like. The method comprises the following steps: acquiring data by using a co-prime array and preprocessing to obtain SCM data as original input information of DOA estimation; constructing a deep complex value convolution attention residual network to directly process covariance matrix information of a complex field, and utilizing an initial two-dimensional complex value convolution layer, a cascaded complex value convolution block attention network and a cross-layer residual connection structure to deeply extract complex value features related to a space angle; the DOA estimation module is responsible for finally mapping the extracted high-dimensional complex value feature vector to a representation space directly related to a DOA estimation task, and the output module converts the internal feature representation output by the DOA estimation module into a DOA estimation result which can be explained by a user.

The invention discloses an interactive multi-model underwater maneuvering target tracking method and system based on a pure azimuth second-order EKF. The method comprises the following steps: establishing a discrete state space model of a pure azimuth target tracking system; performing second-order linearization on the pure azimuth measurement model; calculating mixed input of a sub-filter corresponding to each model in the model set of the interactive multi-model algorithm; estimating a target state through parallel filtering of the azimuth-pure second-order EKF sub-filter corresponding to each model in the model set; updating the probability of each model in the model set; and combining the estimation results of all the sub-filters to obtain a final estimation result of the target state. According to the method, the target tracking precision can be improved, the problem of highly nonlinear measurement of the UUV on a target detection and tracking system through a passive sonar is effectively solved, good real-time performance is achieved, and real-time tracking of the UUV on the motion state of the underwater maneuvering target is achieved through adaptive matching of the real motion of the target through the interactive multi-model algorithm.

The invention discloses a GNSS (Global Navigation Satellite System) / inertial navigation tightly integrated navigation method and system based on robust auto-velocity constraint factor graph optimization, and the method specifically comprises the following steps: extracting pseudo-range and Doppler observation data from original observation information of a GNSS satellite, and obtaining accelerometer and gyroscope data from an INS (Inertial Navigation System); constructing a pseudo-range residual block, a Doppler residual block, an inertial navigation pre-integration residual block and a speed constraint factor, and integrating into a sliding window estimator based on factor graph optimization; according to the size of a set sliding window, adding all residual blocks in the window to form an optimized objective function; performing a first round of factor graph optimization to obtain a preliminary state estimation result, and endowing the observed quantity containing gross error with a relatively low weight; and updating the prior weight matrix, and carrying out second round of optimization to obtain a joint optimal estimation result of a plurality of moment states in the current time window. According to the method, the influence of gross error-containing observation information on weight estimation and state estimation is suppressed, the positioning precision is high, the robustness is strong, and the reliability is high.

The invention discloses a mining equipment positioning system and method based on radar, ultra wide band and inertial navigation, and the system is characterized in that a multi-source sensor unit, a data synchronization and collection module and a preprocessing module are connected in sequence, and a state fusion module is connected with the preprocessing module, a UWB ranging error compensation module, a radar motion estimation module and a pose output module; the upper computer is connected with the pose output module; the method comprises the steps of collecting multi-source data; timestamp alignment and format conversion processing are carried out, and feature and speed constraint information extraction is carried out on the point cloud data; fitting correction is carried out on the ranging data by adopting a weighted least square method, and position information is obtained through an error weighted average algorithm; feature matching is carried out, and in combination with a KISS-ICP point cloud registration method, a speed estimation result is obtained; carrying out data fusion, and obtaining a pose estimation result by adopting an extended Kalman filtering algorithm; and outputting equipment position and attitude information. According to the invention, high-precision real-time estimation of the attitude and position information of the mining equipment can be realized.

The invention provides a floating-point number calculation circuit, a floating-point number calculation method, a storage medium and electronic equipment, and relates to the technical field of computers. In the circuit, a mantissa calculation module is used for executing multiplication of two floating-point number mantissas to obtain a product mantissa when a floating-point number operation starting signal is received; the leading zero detection module is used for responding to the floating-point number operation starting signal and performing leading zero detection on the mantissa of each floating-point number to obtain a leading zero estimation result of the product mantissa; the normalization processing module is used for executing product mantissa displacement control and index adjustment based on the leading zero estimation result to obtain a mantissa operation result and an index operation result; and the result output module is used for outputting a floating-point number product result based on the mantissa operation result and the index operation result. The length of the floating-point number multiplication calculation path can be effectively shortened, and the floating-point number calculation efficiency is improved.

The invention relates to an intelligent measurement and control optimization method and device for dynamic parameter adaptive calibration, equipment and a medium. The method comprises the following steps: executing invariance causal test based on an external environment context to obtain a cross-environment invariant explanatory variable subset, and performing anti-fact simulation on each parameter in the cross-environment invariant explanatory variable subset to generate a causal attribution report; based on an under-excitation parameter subset and a cross-environment invariant explanatory variable subset in the baseline recognizable atlas, performing safe active excitation planning on the parameters to obtain a safe micro-perturbation excitation plan; and based on the residual error, the uncertainty estimation, the delay cross-correlation feature, the hysteresis loop area feature, the causal contribution score and the excitation-response fragment, performing calibration estimation on the target parameter by using a hierarchical estimator, and generating a calibration packet according to a calibration estimation result. By adopting the method, self-adaptive calibration of intelligent measurement and control dynamic parameters can be realized through residual attribution and cross-environment invariance test in combination with safe perturbation excitation.

The invention discloses a power distribution network topology state estimation method, electronic equipment, a medium and a product. The method comprises the steps of obtaining a topological structure and measurement data of a power system; constructing variable nodes and factor nodes according to the topological structure and the measurement data, and constructing a state-topological joint factor graph model according to the variable nodes, the factor nodes and the topological structure; based on the state-topology joint factor graph model, performing state estimation through a belief propagation algorithm to obtain an estimated value of state variable correction; if the estimated value of the state variable correction meets the convergence condition, correcting the topological state of the switch branch according to the active power and reactive power of the head end of the switch branch in the estimated value of the state variable correction; and outputting an estimation result of the topological state of the switch branch until the on-off state of the switch branch obtained according to the state variable is consistent with the original topological state of the switch branch. According to the method, asynchronous real-time updating of the topological state of the power system can be realized.

The invention discloses an Internet of Things equipment real-time early warning method and system based on digital twinning, and relates to the technical field of equipment operation management, and the method comprises the steps: collecting multi-modal original data of Internet of Things equipment, constructing a graph structure, and generating a semantic graph vector; obtaining a digital twinborn model state corresponding to the target equipment, forming an extended state vector by the digital twinborn state, the physical equipment state and the semantic map vector, and inputting the extended state vector into an extended Kalman filter for state fusion to obtain a state estimation result; and performing Monte Carlo simulation according to the state estimation result, generating a plurality of abnormal state samples, calculating a residual mean value between the abnormal samples and the current estimation result, correcting a covariance parameter of the extended Kalman filter, and generating an early warning tag. Fine estimation and risk level early warning of the state of the Internet of Things equipment are realized, and the state fusion precision and the abnormal response timeliness are improved.

The invention discloses a single-view unknown object 6D pose estimation method based on segmentation and new view angle synthesis, and relates to the field of object pose estimation. The method comprises the following steps: designing a segmentation module and a new view angle synthesis module of a pose estimation network, and obtaining a segmentation result of a target object by using the segmentation module; generating a target object multi-view-angle embedding template by using a new view angle synthesis module; matching the embedding representation zquery'of the segmentation result with a multi-view embedding template of the target object to find out the embedding representation most similar to zquery ', and determining a preliminary rotation attitude estimation result R of the target object; and estimating the relative translation vector t of the target object, and combining the R to obtain the relative pose (R, t) of the target object. According to the method, robust 6D pose estimation of the unknown object in a complex visual scene can be realized only by taking a single reference image as input, and the pose estimation precision and robustness of the unknown object in the complex scene are improved while the real-time requirement is met.

The present invention is a parameter estimation method for compartment model based on physics-informed neural networks. Starting from a physical model, the method extracts information from an AIF and a small amount of measurement data to obtain kinetic parameters, thereby greatly improving the scanning efficiency of a measuring instrument, and reducing occurrence of inaccurate estimation results due to patient movement. In addition, the present invention has the robustness to AIF noise and measurement data noise, and can flexibly arrange the time of data acquisition, reduce an error of inaccurate estimation caused by long time 10 acquisition and the patient movement, and improve the efficiency of data acquisition of the instrument. Experimental results show that the present invention is more stable and has less errors. Meanwhile, the present invention does not require the setup of training datasets, and is superior to an end-to-end supervised reconstruction method U-net network with fewer samples.

The invention discloses an offshore rocket erection state anomaly detection method based on sparse representation and adaptive filtering, and the method comprises the following steps: obtaining state data in a rocket erection process through multiple sensors, constructing a time sequence, setting a sliding window with change capability in each time period, and extracting sparse features for state prediction. A feedback adjustment signal is formed by calculating the difference between a predicted value and an observed value, and parameters in the state estimation process and the external disturbance modeling process are adjusted respectively. A double-path structure is adopted to independently estimate the state and disturbance, and a final estimation result is obtained through a fusion strategy. The system continuously updates parameters according to error conditions, continuous self-adaptive adjustment and anomaly recognition are achieved, and the recognition precision and the response capability of the anomaly trend are improved. The method is suitable for the recognition processing of the abnormal state in the erection process of the rocket in the marine environment, and has high dynamic adaptive capacity and time sequence anomaly detection capacity.

The invention discloses a lightweight human body posture estimation method based on PLES-YOLO, and belongs to the field of computer vision, and the method comprises the following steps: carrying out the feature extraction of an input image through employing an improved down-sampling module, and obtaining an initial feature map; based on the initial feature map, a multi-scale feature fusion module is adopted to carry out progressive feature enhancement, and multi-scale fusion features are obtained; based on the multi-scale fusion features, performing feature optimization by adopting a spatial pyramid pooling module combined with an attention mechanism to obtain weighted multi-scale features; and on the basis of the weighted multi-scale features, a lightweight shared convolution detection head is adopted to predict human body key point coordinates, and a final attitude estimation result is output.

The invention belongs to the crossing field of artificial intelligence and mechanical engineering, particularly relates to a micro-motor fault prediction and health management system, and aims to solve the problems that early faults of a micro-motor are difficult to recognize, degradation modeling is inaccurate and maintenance lags. The system collects multi-source data through high-density sensing, combines denoising reconstruction, composite feature extraction and time-varying weighted fusion to generate health indexes, identifies health stages by using a segmented hidden Markov model, iteratively updates residual life prediction based on a Wiener process, outputs an estimation result with a confidence interval, and links a hierarchical maintenance strategy. And continuous optimization of the model is realized through federal learning. The system improves the fault early warning accuracy and prediction reliability, and reduces the operation and maintenance cost.

The invention discloses a forest carbon sink accurate monitoring system and method based on multi-source data fusion, and relates to the technical field of carbon sink monitoring. In order to solve the problems of low precision and poor timeliness of the traditional monitoring means, the adopted scheme comprises a multi-source data acquisition module used for acquiring satellite remote sensing, unmanned aerial vehicle aerial photography, ground monitoring sensor and meteorological data; the data pre-processing and fusion module is used for pre-processing the collected data and extracting and combining related features through a two-layer fusion method to serve as input data of a carbon sink estimation model; the model construction and optimization module is used for constructing and optimizing a carbon sink estimation model; the result visualization and analysis module is used for displaying a carbon sink estimation result, researching influence factors of forest carbon sinks and predicting a future change trend; and the integration and management module is used for integrating hardware equipment to construct a monitoring platform, developing a monitoring system based on cloud computing and the Internet of Things, and establishing and perfecting a management system. The method is used for monitoring the forest carbon sink.

The invention discloses a sparse regularization direction of arrival estimation method based on a risk minimization principle, and belongs to the technical field of array signal processing and underwater acoustic signal processing. The method comprises the steps of receiving array signals and establishing an observation model; constructing a sparse representation and over-complete dictionary; establishing and initializing a regularization optimization model; carrying out adaptive weight updating and risk-driven parameter selection; after regularization parameters are determined, a fast iterative shrinkage threshold algorithm FISTA is adopted to carry out optimization solution, and dictionary refinement is carried out on the detected direction after each iteration convergence so as to reduce off-grid errors; and after a small amount of outer layer iteration is repeated, outputting a final DOA estimation result and corresponding power. According to the method, self-adaptive selection of regularization parameters and noise levels can be realized, and the problem of precision degradation under complex conditions of low signal-to-noise ratio, limited snapshot number, signal source correlation, power imbalance and the like is effectively solved without manual parameter adjustment, so that the robustness and practicability of estimation are remarkably improved.

The invention discloses a multi-modal remote sensing image registration method based on domain self-adaption and an optimizer set algorithm, and relates to the field of image processing, and the method comprises the steps: respectively extracting the feature vectors of a reference image and an image to be registered, carrying out the feature matching of the feature vectors according to the class label containing conditions of the feature vectors, and obtaining a registration result; obtaining a feature matching vector based on a matching result; estimating transformation parameters corresponding to the affine transformation model by using a random sampling consistency algorithm, and optimizing the affine transformation model according to an estimation result and similarity measurement; and performing spatial transformation on a to-be-registered remote sensing image by using the optimized affine transformation model to realize spatial alignment between the reference image and the to-be-registered image. According to the method, firstly, the feature points of the multi-modal remote sensing image are extracted, the difference of feature vectors is reduced by using a domain adaptive method, the precision and robustness of feature matching are improved, transformation parameters are optimized through an optimizer set algorithm, and search is prevented from falling into local optimum.

The invention provides a battery state-of-charge estimation method and system based on a physical gating neural network, and the method comprises the steps: obtaining battery operation data, and inputting the data to a data drive estimation module to obtain a preliminary SOC estimation value; meanwhile, the physical correction module performs integration on the battery operation data to obtain a physical estimation value, and generates a correction estimation value under physical constraint; calculating confidence through a confidence gating module to perform weighted fusion on the initial estimation value and the corrected estimation value, and outputting a final state of charge estimation result; and determining an optimal hyper-parameter through K-fold cross validation and completing network training to obtain a trained physical gating neural network, and predicting a state-of-charge estimation value of the battery based on the trained physical gating neural network. According to the method, high-precision and low-error SOC estimation can be realized under a complex multi-cycle charging and discharging working condition, and the stability and the reliability of an estimation result are remarkably improved.

The invention provides a water quality intelligent monitoring method and system, and the method comprises the steps: obtaining the historical water quality data of a water transmission and distribution pipe network, and obtaining parameters in a water quality dynamic system model through a least square method based on the historical data; a parameter estimation result is applied in a laboratory simulation environment, if the difference between model output and measured data does not exceed a preset threshold value, parameters are deployed in an actual water supply system, and the model is dynamically calibrated according to real-time operation data of a water transmission and distribution pipe network; and acquiring real-time water quality data through an Internet of Things sensor, inputting the real-time water quality data into the water quality dynamic system model to predict future water quality indexes, and adjusting water treatment process parameters in advance according to a prediction result. Through the method and the corresponding system, a water quality supervisor can be helped to make an adjustment decision in advance before the water quality is deteriorated, and more intelligent monitoring is realized.

The invention discloses a distribution network hot-line work robot pose estimation system and method, and belongs to the technical field of robot visual perception. The system comprises an input preprocessing module which is used for carrying out noise reduction and enhancement processing on an RGB-D image; the shared feature extraction module is used for extracting multi-scale universal features based on a lightweight convolution architecture; the 6D pose estimation module is used for processing the image based on the neural implicit field to obtain a pose estimation result; and the joint optimization module is used for realizing detection and pose estimation shared feature extraction through cooperation of a multi-task loss function and a pose estimation result. The system adopts adaptive median filtering and homomorphic filtering to eliminate noise and uneven illumination, and bilateral filtering optimizes a depth map; a bottleneck structure and cavity convolution are introduced into feature extraction, and a rank enhancement linear attention module is embedded; according to the pose estimation, a geometric field and an appearance field are modeled through a neural implicit field, and pose hypotheses are generated and optimized. According to the method, the problems of low pose estimation precision and poor real-time performance in the distribution network live working environment are solved, and the working safety and efficiency of the robot are improved.

The invention discloses an underwater target positioning method based on data-model dual drive. The method specifically comprises the following steps: data acquisition: acquiring initial data by using a receiver; performing sight distance reasoning: constructing and training a sight distance information reasoning neural network based on data driving, preprocessing the collected initial data, inputting the preprocessed data into the neural network, and outputting arrival time estimation of a sight distance path; joint estimation: adopting a model driving method, calculating the arrival time difference of sound signals received by a receiver by using the obtained sight distance arrival time, designing underwater target position and signal propagation speed joint estimation based on the arrival time difference, and calculating initial estimation values of the target position and the propagation speed by using a weighted least square method; and robust iteration: based on a maximum likelihood estimation function, performing robust iteration optimization on the obtained preliminary estimation value to obtain a stable and convergent final joint estimation result. The target positioning method is convenient to popularize and apply in different underwater environments, and has relatively high adaptability and expansibility.

The invention discloses a narrow environment high-precision laser inertial navigation and SLAM (Simultaneous Localization and Mapping) method based on adaptive parameters. According to the method, firstly, radar and IMU data are collected and preprocessed to form a unified input data stream; obtaining a pose prediction result by using the data stream, constructing an observation model, and obtaining a laser observation residual error; calculating a characteristic value proportion in the matching process of the point cloud and the sub-map, and judging a geometric degradation scene; when degradation is detected, adaptively adjusting a voxel filtering radius and a loopback detection threshold value; fusing the pose prediction result and the laser observation residual error by adopting a Kalman filtering model to obtain an optimal pose estimation result, and carrying out loopback detection and back-end map optimization under a dynamic threshold value to correct accumulated drift and update a voxel map; and finally outputting a continuous pose track and a three-dimensional voxel map. According to the method, the robustness and the positioning precision of the laser inertial navigation fusion SLAM in narrow scenes such as pipe galleries, tunnels and chemical plants are effectively improved.

The present invention discloses a visual inertial odometry method and system based on transformation error state, and relates to the technical field of visual inertial odometry optimization. The technical points of the present invention include: estimating posture information using a transformation extended Kalman filter, including: establishing a system continuous-time motion model and measurement equation based on real-time acquired visual and IMU data; establishing a linearized error state system based on the system continuous-time motion model and measurement equation; designing a linear time-varying transformation, and using the linear time-varying transformation to transform the linearized error state system into an error state system in which the system state is independent of the system's unobservable subspace; performing state estimation based on the transformed linearized error state system to obtain estimated posture information. The present invention proposes a transformation-based method to solve the inconsistency problem in VINS, alleviates the observability mismatch problem, and ensures that the visual inertial odometry using the transformation extended Kalman filter has consistent estimation results.

The invention provides a radar-based human skeleton estimation method, system and product, and the method comprises the steps: carrying out the target detection of collected radar echo data, and generating a target four-dimensional point cloud containing distance information, speed information and angle information based on a target detection result; dividing the target four-dimensional point cloud into a plurality of human body topology point cloud blocks based on the velocity direction similarity of each point in the target four-dimensional point cloud; and inputting the human body topology point cloud block into a human body skeleton estimation network for human body skeleton estimation to obtain a human body skeleton estimation result. According to the human skeleton estimation method provided by the invention, human topology priori can be constructed from sparse millimeter wave radar point clouds, point cloud block features sensed by a structure are extracted, the modeling capability of a model for the spatial relationship of key parts of a human body is enhanced, diversified and natural daily human behaviors in a non-inductive monitoring scene can be adapted, and the human skeleton estimation accuracy is improved. The human body skeleton estimation with higher generalization ability is realized, and the accuracy and robustness of human body posture prediction can be effectively improved.

The invention provides a vector array sparse Bayesian learning direction of arrival estimation method. The method comprises the following steps: constructing a far-field vector sparse signal model; and establishing a noise covariance model under the vector sound field. And estimating a signal power hyper-parameter through a vector array sparse Bayesian learning process. And through a maximum likelihood estimation technology, noise power hyper-parameter estimation is realized. And finally, carrying out peak searching on the converged signal power hyper-parameter to obtain a direction of arrival estimation result. The method has the advantages that the vector array signal processing performance advantage is obtained, higher signal processing gain is obtained, and meanwhile the method has the capability of restraining the azimuth ambiguity problem; and by using the difference between the noise covariance matrix and the signal covariance matrix under the vector noise, the estimation precision of hyper-parameters such as the signal power and the noise power is remarkably improved. Compared with a traditional vector array DOA estimation method, the vector array DOA estimation method has a lower spectrum background and a sharper spatial spectrum peak, and the resolution and DOA estimation precision are remarkably superior to those of other methods.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a three-dimensional modeling system and a robot, and the system comprises a pose covariance estimation module and a covariance display module. The pose covariance estimation module is used for carrying out pose map optimization by adopting a preset algorithm based on a target optimization function and the received point cloud data, calculating poses at all moments and a sea plug matrix corresponding to each pose, and determining a covariance matrix of a node according to the inverse of the sea plug matrix of the node corresponding to the poses; and the covariance display module is used for obtaining, rendering and displaying a pose covariance estimation result and a point cloud covariance estimation result according to the covariance matrix corresponding to the pose and the received point cloud data. The problems that an existing three-dimensional modeling system is low in modeling efficiency and low in precision can be effectively solved.

The invention discloses a power grid power flow real-time calculation and state estimation method and system based on a PMU, and belongs to the technical field of electric measurement and electric fault positioning. The method comprises the following steps: synchronously measuring electrical variables, including a voltage phasor and a current phasor, of a power grid node through PMU equipment to obtain synchronous phasor data, and verifying time and topology consistency; the power grid observability grade is judged based on PMU configuration node distribution corresponding to the verified electric measurement data; directly generating a real-time state estimation result through linear calculation based on PMU electric measurement data if the whole domain is observable; and if a part is observable, fusing PMU and SCADA data, carrying out nonlinear state estimation through a weighted least square method, distributing a high weight for the PMU electrical measurement data, and dynamically adjusting the SCADA weight to suppress bad data. According to the method, through observability adaptive judgment, the precision and efficiency of state estimation are improved, a data basis is provided for power grid fault positioning, and the method is suitable for practical application scenes of various PMU coverage degrees.

The invention discloses a dual-path non-cooperative spacecraft pose estimation method based on uncertainty perception, and belongs to the technical field of spacecraft pose estimation. According to the method, a dual-path prediction framework based on a shared backbone network is constructed and comprises a geometric reasoning path and a global context sensing path. In the training stage, prediction results of the two paths are aligned through a geometric consistency loss function, and joint optimization is carried out by combining multi-task losses such as key point regression, classification, rotation and translation regression and the like. In the inference stage, the pose estimation result is adaptively fused according to the predicted variance output by each path, when the variance is low, the output of the global context sensing path is directly adopted, otherwise, the key point projections of the two paths are combined, and the final pose is recovered. The method has higher estimation precision, robustness and interpretability in complex space environments such as shielding, illumination variation and large-scale variation, and provides reliable visual navigation support for on-orbit autonomous tasks.

The invention relates to a bridge crane game model-free optimal control method based on event triggering, and the method comprises the steps: collecting a state vector of a bridge crane in real time, and building a nonlinear multi-player system model of the bridge crane; estimating an unknown dynamic function and an input gain matrix on line in the input identifier neural network; according to an event triggering mechanism, whether triggering is conducted or not is judged based on the error between the current state vector and the state vector of the recently-triggered sampling; if so, triggering a dynamic updating instruction; under a non-zero sum game framework, obtaining an optimal value function gradient based on an estimation result and a current state vector through a self-adaptive evaluator network, and generating an event triggering optimal control law of each player based on a sampled state vector; and the event triggering optimal control law is processed by a zero-order retainer and then is output as a physical driving signal to control the operation of the bridge crane. Compared with the prior art, the method has the advantages of high applicability, high disturbance resistance, high accuracy and the like.