3454 results about "Positioning technology" patented technology

The invention discloses a vehicle-mounted Beidou positioning deviation self-calibration system fused with inertial navigation information, and relates to the technical field of navigation and positioning. The method is used for solving the problems of positioning deviation accumulation and signal failure in a complex environment. The system realizes space-time alignment of inertial navigation and Beidou observation data through timestamp interpolation and coordinate system conversion, and constructs a multi-source synchronous data stream. A difference value between an inertial navigation calculation speed and a Beidou Doppler speed is analyzed based on a vehicle incomplete constraint model, and a weight factor is generated in combination with a confidence threshold to suppress abnormal observation. A geometric matching error is calculated through a high-precision lane map curvature and a vehicle steering angle, and candidate tracks are screened to generate transverse calibration parameters. The noise covariance is dynamically adjusted through a tight coupling filtering algorithm, the mode is switched to a lane constraint calibration mode when a Beidou signal is interrupted, inertial navigation errors are compensated through error boundary constraint, and stable output is achieved. Multi-source data deep fusion and adaptive robust calibration are realized, and system robustness and positioning continuity are improved.

The invention discloses a trajectory optimization method based on a multi-source heterogeneous positioning data fusion algorithm, and relates to the technical field of intelligent navigation and high-precision positioning, multi-modal data are acquired through a multi-modal sensor array, positioning redundancy of scenes such as tunnels and indoor scenes is enhanced, a weight distribution strategy is dynamically adjusted through an Actor-Critic network architecture, and the positioning accuracy is improved. The state space input comprises an environment semantic tag, a historical error sequence and a real-time noise variance, the output action space is continuous weight distribution of each data source, a multi-target reward function optimization strategy is combined, scene adaptability is realized, a local SLAM map, inertial navigation error parameters and a weight distribution strategy are shared in real time based on a V2X protocol, and the real-time performance of the system is improved. According to the method, a single device accumulative error is compensated by using adjacent vehicle data, a terminal locally trains an error compensation model, parameters are uploaded to a cloud end through differential privacy encryption, the cloud end adopts a FedAvg algorithm to aggregate a global model and issue the global model, the error difference between devices is inhibited, and dynamic road network updating and scene differentiation model distribution are supported at the same time.

The invention relates to the technical field of navigation and positioning, in particular to an intelligent positioning and navigation early warning system and method for ships and boats. The method comprises the following steps: collecting and carrying out sea condition sensing and positioning signal evaluation according to a multi-source sensor synchronous data set to obtain a sensor reliability score table; performing target motion situation analysis according to the sensor reliability score table to obtain a fusion situation vector diagram; performing target motion state judgment according to the fused situation vector diagram to obtain a target motion state judgment result; performing multi-target collision risk assessment according to the target motion state judgment result to obtain a risk degree assessment matrix; performing navigation maneuvering performance constraint according to the risk assessment matrix to obtain a route planning maneuvering constraint rule set; and performing route trajectory search according to the route planning manipulation constraint rule set to obtain a navigation trajectory scheme. According to the invention, through the positioning navigation early warning technology, the positioning precision, the navigation safety and the early warning system practicability of the ship in a complex environment are obviously improved.

The invention relates to the technical field of intelligent maintenance, and discloses a fault diagnosis positioning technology, which comprises a data acquisition and alarm module for acquiring equipment operation state data and generating a dynamic alarm signal; the integrated analysis processing module is used for analyzing, processing and identifying the abnormal state of the system; the multi-source fault positioning module is used for analyzing the identified abnormal state, extracting fault features through an optimization algorithm and positioning parts which have faults and possibly have faults in combination with a deep learning model; and the three-dimensional fault visualization module is used for marking the position and the reason of the fault component in the model. Through cooperative work of the data acquisition and alarm module, the integrated analysis processing module, the multi-source fault positioning module and the three-dimensional fault visualization module, automation of the whole process of fault diagnosis is realized, and the diagnosis efficiency is greatly improved. Compared with the existing problems of dependence on manual checking and simple alarm and difficulty in accurate positioning, the method effectively solves the defects of low positioning efficiency and slow response of a traditional method.

The invention relates to the technical field of visual positioning, and provides a visual positioning method and system for precise connector assembly. Performing multi-mode HDR fusion and distortion correction on the original connector image to obtain a multi-layer fusion image matrix; and performing feature region coarse positioning in combination with a composite convolutional neural network to obtain a connector coordinate set, performing geometric feature fine positioning according to the connector coordinate set and the multilayer fusion image matrix to obtain a key point coordinate set, and performing hand-eye calibration and dynamic compensation on the key point coordinate set to generate a pose instruction. And performing vision-force control hybrid assembly processing on the pose instruction according to the sensor feedback data to obtain assembly completion state data. Through multi-modal image fusion, deep learning coarse positioning, precise geometric registration and dynamic cooperation of vision and force control, the speed, precision and stability of precise connector assembly are improved.

The invention discloses a multi-modal information fusion odometer construction method for star catalogue positioning, and relates to the technical field of star catalogue patroller positioning. The method comprises the following steps: carrying out space joint calibration on a monocular camera, a laser radar and an inertial measurement unit, reconstructing a laser radar point cloud by using a timestamp of a camera image, and realizing time synchronization of the camera image and the laser radar point cloud; establishing an IMU pre-integration error model; and performing motion compensation distortion removal on the laser point cloud by using an IMU pre-integration result, and extracting geometric features of the distorted laser point cloud based on a neighbor region smoothness calculation method of a fixed measurement distance. By researching a multimodal information fusion odometer method, the accumulative error of motion measurement is reduced, the positioning precision and stability are improved, technical support is provided for design and development of a star catalogue navigation system, and the problems that a single-modal star catalogue positioning method is weak in environment adaptive capacity and poor in algorithm generalization are solved.

The invention discloses a mechanical arm natural language instruction control system and method based on a large language model, and belongs to the field of intelligent manufacturing. Aiming at the limitation that traditional mechanical arm control depends on pre-programming and a static rule library, a dynamic mapping mode from a natural language instruction to an atomic action sequence is designed, an atomic skill library including detection, grabbing, moving, placement and other operations is constructed, and semantic analysis and a multi-mode cooperation technology are combined, so that the atomic action sequence is obtained. And support is provided for man-machine cooperation of a flexible assembly task. The method specifically comprises the steps that a DeepSeek-Distil-Llam-8B large model and a LoRA fine tuning technology are adopted, and a natural language instruction is converted into an executable atomic action sequence; based on a transfer learning optimized YOLOv8 target detection technology and a binocular vision positioning technology, a sensing module adaptive to an assembly scene is constructed and is fused with a mechanical arm motion planning module, and positioning grabbing of parts and tools is achieved. And an interactive interface is built by combining a voice-to-text large model and a Gradio front-end framework, so that the convenience of man-machine interaction is improved. By optimizing large model reasoning and motion planning cooperation efficiency, response delay from instructions to execution is reduced, and an efficient and extensible solution is provided for man-machine cooperation in intelligent manufacturing.

The invention discloses a multi-target pedestrian re-identification system based on multiple modes and a vector database, relates to the technical field of network communication and positioning, and solves the problem of cross-target and cross-mode trajectory association in a complex multi-camera scene. The multi-target pedestrian re-recognition system comprises a monocular tracking module, a multimode extraction module, a trajectory generation module, a multi-objective matching module and a global retrieval module, through organic combination of multi-modal features and a multi-modal multi-path recall strategy, the accuracy and applicability of cross-modal pedestrian re-identification are significantly improved. Through track-level feature generation and storage design, the modeling capability of dynamic features of a target in a complex scene is enhanced; through collaborative design of a space-time constraint mechanism and multi-modal features, logic consistency and global optimality of target person trajectory association are ensured.

The invention discloses a ship-shore cooperative tracking and positioning method based on multi-modal sensor fusion, and belongs to the technical field of target positioning. The method comprises the steps that multi-sensor layout and sensor fusion calibration are carried out on a target ship set and a shore end respectively, multi-view image sequence data and three-dimensional point cloud data are obtained, and the target ship set comprises a plurality of target ships; performing data fusion based on the multi-view image sequence data and the three-dimensional point cloud data to obtain fusion data of the target ship set, establishing an adaptive motion state model and an adaptive observation model based on the fusion data, and performing state prediction, state updating, data association and tracking management on the target ship set by adopting an unscented Kalman filtering algorithm; and establishing a space-time diagram model based on the Kalman filtering fusion observation factor and the Kalman filtering state prediction factor, and performing pose optimization on the target ship set in combination with the GPS factor. According to the method, the accuracy of ship and ship-shore cooperative positioning is improved.

The invention relates to the technical field of Beidou positioning, and discloses a Beidou positioning inspection method and system. The method comprises the following steps: acquiring geographic information and equipment state parameters of an inspection area, and generating a structured task queue according to dimensions such as equipment types and fault levels; constructing a Beidou positioning dynamic model containing a position axis, a time axis and an environment factor axis; determining an initial inspection constraint condition according to the equipment type and the fault level, and performing path planning simulation to generate an initial inspection route; detecting a route anomaly, and adjusting an abnormal node by using a hybrid optimization algorithm to generate a stable route; model parameters are updated in combination with real-time environment data, and a final inspection instruction set containing information such as equipment moving time sequence is generated. The system comprises a data processing module, a model construction module, a constraint determination module, a path planning module, an anomaly detection module, an optimization adjustment module and the like. The inspection efficiency and precision are improved, the stability and reliability are enhanced, and the inspection requirements of complex environments can be effectively met.

The invention relates to the technical field of positioning, in particular to a device based on vision and 2D laser fusion positioning, and the device comprises a horizontal distance measurement module which is horizontally installed on a robot body and is used for collecting contour distance measurement data of a motion plane; the vertical vision module is installed on the robot body in a top view mode and used for collecting visual feature data of a vertical space; the calculation processing module is configured to construct a laser contour map and a visual feature map which are aligned in a coordinate system based on the contour distance measurement data and the visual feature data, determine an initial pose of the robot according to a given position or an optimal position of self-global matching of the laser contour map and the visual feature map, generate a predicted pose in combination with a motion measurement value, and send the predicted pose to the robot. And iteratively executing dual-mode observation filtering updating of laser contour matching optimization and visual reprojection matching optimization by taking the predicted pose as a center. According to the invention, through deep integration of a heterogeneous sensor architecture and a computing platform, an industrial scene-oriented all-weather positioning solution is constructed.

The invention discloses an adaptive environment weighted data fusion navigation and positioning method for a manned submersible in a polar region, which belongs to the technical field of ocean navigation and positioning, is used for navigation and positioning of the submersible in the polar region, and comprises the following steps: acquiring data of IMU (Inertial Measurement Unit), acoustics, optics, environment sensors and Doppler sensors, and carrying out de-noising, time synchronization and coordinate conversion; modeling the polar region environment variable, calculating the adaptive weight, and dynamically adjusting the contribution degree of the sensor by adopting an exponential decay function; introducing an environment factor to correct the weight, and performing nonlinear state estimation by adopting an EKF (Extended Kalman Filter); performing error modeling by using historical data, and optimizing a weight adjustment strategy in combination with machine learning; and filtering optimization is performed on the fusion result, and multi-scale correction is performed in combination with prior environment information. Compared with the prior art, the influence of a single sensor error on a final positioning result is reduced, the positioning precision of the submersible in a polar region complex environment is improved, and the stable navigation capability is kept under the condition that sensor data suddenly changes or is lost.

The invention relates to a defect positioning method based on fusion of weld defect features and trajectory tracking data, and belongs to the technical field of weld defect detection and positioning. The method comprises the following steps: capturing welding seam track dynamic data and defect feature data, constructing a dynamic coordinate system based on a welding seam initial feature point, and establishing double-data-set reference mapping; performing multi-physics field interference decoupling correction on the trajectory data, and performing cross-modal feature purification and core feature consistency verification on the defect data; converting the preprocessed data into a feature form adaptive to fusion, and constructing a welding process-defect formation mechanism association network to regulate and control fusion weight; and finally, reconstructing a three-dimensional dynamic contour of the welding seam, calling dynamic positioning logic to position the defect, and outputting a result carrying the process-defect causal confidence coefficient. The positioning precision is improved through multi-dimensional data fusion and mechanism association, and technical support is provided for welding quality management and control.

The invention relates to the technical field of welding seam positioning, and discloses a structured light and line laser fused welding seam thickness grading three-dimensional positioning method and system, and the key point of the technical scheme is that the method comprises the following steps: S1, shooting a workpiece through a structured light camera, and obtaining a workpiece image; s2, based on the workpiece image, through a channel space attention mechanism and a Center Net convolutional neural network, welding seam position coarse identification is carried out, and the welding seam position is determined; s3, according to the identified position of the welding seam, scanning the position of the welding seam through a line laser camera to obtain three-dimensional point cloud data of the welding seam; and S4, based on the three-dimensional point cloud data, through a Marr-Hildreth edge detection algorithm, identifying to obtain three-dimensional coordinates of the welding seam, and rapidly and accurately positioning the three-dimensional data of the welding seam of the workpiece.

The invention relates to the technical field of TDOA (time difference of arrival) positioning, and provides an ultrasonic three-dimensional positioning method based on beam enhancement and TDOA, which comprises the following steps: extracting initial space parameters based on beam scanning of an array receiving signal, carrying out iterative optimization on beam weight to adjust a direct wave component and suppress reflection interference, constructing a closed-loop optimization mechanism, and carrying out beam enhancement and TDOA positioning. Calculating an enhanced signal and an array element reliability measurement index through frequency domain segmentation and energy normalization; forming an optimization array based on the array element reliability measurement index, extracting a time delay parameter from the optimization array, constructing a distance measurement equation set, and calculating an initial estimation value of a target position by adopting a weighted optimization algorithm; and constructing a residual error model, constructing an optimized weight matrix by combining an array element reliability measurement index and a signal quality index, iteratively calculating a target coordinate until a convergence condition is met, and outputting a final three-dimensional coordinate. According to the method, the comprehensive performance of ultrasonic three-dimensional positioning in the aspects of anti-interference performance, stability and positioning precision in a complex environment is improved, and error source control is realized.

The invention discloses a PPP-RTK time delay processing method and device in conversion from SSR to OSR, and relates to the technical field of global navigation satellite system positioning, and the method comprises the steps: converting orbit, clock error, ionosphere and troposphere delay SSR parameters provided by a server into OSR virtual observation values through geometric distance calculation and projection function mapping; based on a constant velocity model and a static random process, predicting a correction amount of a future moment, and correcting an OSR virtual observation value through state transition matrix compensation time delay; combining server side parameters and user side observation data, recursively generating an error covariance matrix, dynamically adjusting a state prediction weight, and obtaining covariance information through adaptive filtering optimization positioning calculation; and realizing ambiguity rapid fixation and centimeter-level positioning by using the corrected OSR observation value and covariance information. According to the method, real-time centimeter-level positioning in a high-delay scene is realized through a dynamic correction value prediction method and a recursive covariance estimation method.

The invention discloses a vehicle navigation positioning method and system when a navigation signal is lost, and relates to the technical field of vehicle autonomous localization, and the method comprises the steps: carrying out the tight coupling matching based on a geomagnetic feature map, obtaining the geomagnetic sequence similarity through a sliding window dynamic time warping algorithm, and generating a fusion positioning track; extracting a road network topology connection relation from the high-precision map, constructing a road connection graph Laplacian matrix with a weight, and obtaining a topology constraint correction coefficient through a graph convolutional network in combination with the fusion positioning track; and constructing a joint optimization function based on the topological constraint correction coefficient, and solving an optimal positioning coordinate by adopting an asymmetric Gaussian-Newton iterative algorithm. According to the method, the defects of topology modeling stiffness and non-linear optimization instability of a traditional method are overcome, so that high-precision and high-robustness vehicle continuous positioning is realized in a GNSS signal loss scene.

The invention discloses a centimeter-level positioning and control system for a low-altitude unmanned aerial vehicle, relates to the technical field of high-precision positioning, and solves the problems that firstly, centimeter-level positioning precision is difficult to realize; secondly, a safe flight path is difficult to generate and adjust in a complex environment; thirdly, it is difficult to optimize the global optimal path by comprehensively considering multiple factors such as energy consumption, obstacles and geo-fences; and finally, the technical problem that it is difficult to generate a control instruction and execute the control instruction under centimeter-level positioning in combination with the obstacle avoidance sensitivity, the hovering stability coefficient and real-time environment perception is solved. According to the invention, centimeter-level real-time positioning is realized through multi-source sensor fusion and Kalman filtering; constructing a geofence and a three-dimensional obstacle avoidance path, and combining obstacle detection to prevent boundary-crossing collision; path planning is optimized by utilizing machine learning and risk assessment, so that the unmanned aerial vehicle dynamically adapts to a complex environment; and optimizing a low-altitude flight control strategy through positioning error compensation and control strategy optimization.

The invention provides a high-precision positioning method and system based on multi-source information fusion. The high-precision positioning method comprises the following steps: acquiring the omm positioning information of the pose of an unmanned aerial vehicle in real time based on a visual inertial navigation unit; a global feature extraction algorithm is combined with a similarity algorithm, and a target satellite image matched with the airborne image of the unmanned aerial vehicle is selected from a satellite image library; using a local feature matching algorithm to combine with the geographic coordinates of the target satellite image to obtain GPS predicted positioning coordinates; fusing the odom positioning information and the GPS predicted positioning coordinates by using a global fusion technology to obtain preliminarily fused odom positioning information; according to the method, secondary fusion is carried out on the basis of GPS positioning data obtained by a real GPS sensor in combination with the preliminarily fused odom positioning information to obtain final odom positioning information, and through a double fusion strategy, the advantages of a multi-source positioning technology are fully integrated, and the positioning accuracy is improved.

The invention discloses an immersed tunnel leakage voiceprint recognition system based on deep learning. The system comprises an acoustic signal acquisition module, a signal preprocessing module, a voiceprint feature extraction module, a deep learning recognition module and a leakage positioning and early warning module. According to the invention, the leakage water flow sound and the structural damage elastic wave signal of the immersed tunnel are comprehensively considered, the multi-dimensional voiceprint feature extraction and deep learning model are fused, and the adaptive signal noise reduction and sound source positioning technology is combined, so that the high-precision monitoring and real-time early warning of the leakage of the immersed tunnel are realized; the accuracy and real-time performance of immersed tunnel leakage nondestructive monitoring are improved, and powerful support is provided for follow-up risk prediction and early repair.

The invention discloses an unmanned aerial vehicle intelligent precise positioning system and method based on multi-source information fusion, and belongs to the technical field of unmanned aerial vehicle navigation positioning, and the system comprises a multi-sensor data collection module which is used for obtaining an original physical signal required by positioning; the data preprocessing and synchronizing module is used for processing original physical signals and ensuring the consistency of multi-source data; the feature extraction and semantic recognition module is used for extracting environment features and industrial equipment semantic information from original data and providing geometric and semantic constraints for positioning; the multi-source information fusion positioning module is used for fusing multi-source data and constructing a factor graph containing sensor error terms and semantic constraint factors; the positioning calculation and state estimation module is used for calculating the positioning pose of the unmanned aerial vehicle based on the fused data; according to the unmanned aerial vehicle intelligent accurate positioning system and method based on multi-source information fusion provided by the invention, by fusing multi-source information, the positioning problem in a complex industrial environment is solved, and accurate identification and positioning of target equipment are realized.

The invention relates to the technical field of fault positioning, and particularly discloses a digital distribution network fault traveling wave range finder and method, and the range finder employs fault traveling wave collection devices disposed at all key nodes of a distribution network to collect fault traveling wave signals in real time, and obtains stable line mode traveling wave signals through signal filtering and line mode component extraction. Then introducing a signal processing algorithm based on deep learning to carry out time domain feature analysis on the line mode traveling wave signal of each key node so as to detect the wave head position of the signal, further determining a power distribution network section where a fault point is located by comparing the signal arrival time of each key node, and determining the fault location of the power distribution network based on a double-end traveling wave distance measurement algorithm. And calculating the distance between the fault point and the nodes at the two ends according to the signal arrival time difference of the nodes at the two ends of the section and the signal propagation speed. Thus, through deep time domain analysis of the fault traveling wave signal, the wave head position of the signal can be accurately identified, and the reliability of distribution network fault traveling wave distance measurement is improved.

The invention relates to the technical field of computer systems and human-computer interaction, in particular to a text travel element universe virtual role collaboration system and method based on large-space interaction. The method specifically comprises the following steps: tracking a user spatial position in real time through a multi-source positioning technology, and carrying out identity authentication and equipment legality verification by fusing multi-modal features; constructing a virtual-real fused semantic point cloud structural body, binding role interaction nodes, dynamically generating a task collaboration graph by using a graph neural network, and driving multiple roles to collaboratively execute subtasks; calling a graph attention network and a GPT module to deduce personalized plot branches in combination with user multi-modal input and a historical track, and rendering an immersive interactive scene in real time; spatial preferences and content interests are mined by adopting DBSCAN clustering and depth feature coding, a dynamic portrait is constructed, and a task path and content presentation are optimized through a weighted recommendation algorithm. According to the method, cloud rendering, edge calculation and cross-module cooperative control are integrated, and the interactivity and immersion experience of the text travel element universe are remarkably improved.

The invention discloses a high-low voltage switch cabinet feeder fault positioning method and system based on transient traveling waves, and belongs to the technical field of power system fault detection and positioning, and the method comprises the steps: synchronously collecting electric and acoustic multi-mode signals, and generating a weighted transient synchronization feature matrix; performing time-frequency transformation and feedback optimization on the matrix, and outputting a multi-scale time-frequency feature set; analyzing the feature set by using an integrated learning network, and outputting a layered preliminary positioning result; convergence to an accurate fault section is carried out through iteration calibration; and finally, fusing multi-model calculation and outputting a comprehensive fault positioning report. According to the method, a technical path of combining multi-modal signal fusion and a physical model is adopted, convergence from fuzzy region division to an accurate position can be realized in stages, and the accuracy, the speed and the anti-interference capability of switch cabinet feeder line fault positioning are remarkably improved.

The invention discloses a sparse view angle-oriented three-dimensional Gaussian visual positioning method, which belongs to the technical field of positioning, is used for visual positioning, and comprises the following steps of: extracting global image features and local image features of a sparse image, and constructing a feature database; performing nearest neighbor search on the feature database and the RGB image to obtain a sparseness index, calculating a camera pose of a pseudo view angle, rendering a color image and a depth image under the pseudo view angle by using a training main model, extracting global features and local features and storing the global features and the local features into the feature database, constructing a 2D-3D corresponding relation in combination with a target color image, and performing dynamic inner point screening and pose solving. And obtaining a positioning result. According to the method, visual angle deficiency of a key area is intelligently completed, the balance and coverage of feature expression in a three-dimensional scene are effectively improved, and dynamic updating and mismatching elimination of a 2D-3D matching relation are realized, so that the positioning precision and the system stability are remarkably improved, and the method has higher robustness and actual availability under a sparse training condition.

The invention relates to the technical field of positioning, in particular to an unmanned aerial vehicle cluster positioning method and device based on Kalman filtering in a satellite denial environment, which can be applied to unmanned aerial vehicle cluster positioning in the satellite denial environment, and can utilize a plurality of ground base stations to position a plurality of aircrafts first. Therefore, the distance change between the adjacent aircrafts is verified by using the observation data of the inertial sensors of the adjacent aircrafts during clustering, and when the verification is abnormal, the abnormal unmanned aerial vehicle is rapidly determined by using the calculated abnormal distance. And then calling Kalman filtering to carry out positioning correction on the section of the abnormal positioning data of the abnormal unmanned aerial vehicle. According to the method, the Kalman filtering is executed by screening the abnormal points, so that the aircraft cluster can be accurately positioned by using the base station with relatively low computing power, and the method has relatively high applicability.

The invention discloses a high-precision AI positioning method and system based on spatial multi-modal data fusion, and belongs to the technical field of fusion positioning, and the method specifically comprises the steps: collecting satellite positioning signals, inertial measurement data, visual images, laser radar point clouds, high-precision maps and other spatial multi-modal data; analyzing visual image illumination distribution, laser point cloud atmospheric particle density distribution and a satellite signal multipath reflection path to generate an environment physical attribute parameter set; inputting the original data of each modal into a correction function bound with environmental parameters, and outputting a standardized feature vector subjected to error compensation; and finally, inputting the recurrent neural network with space-time memory, and outputting the six-degree-of-freedom pose of the carrier by dynamically adjusting each modal feature fusion weight coefficient. According to the invention, through deep fusion of multi-modal data and environmental physical attributes, the problems of poor environmental adaptability and insufficient dynamic adjustment in the prior art are solved, and the precision and robustness of positioning in a complex scene are improved.

The invention relates to the technical field of high-precision positioning, in particular to an outdoor immersive text and travel hybrid positioning and dynamic rendering optimization method, and the method comprises the steps: obtaining a positioning signal of a mobile terminal in a target service area through a hybrid positioning model; the positioning signals comprise an ultra-wideband ranging signal, a visual inertial odometer signal, a real-time carrier phase difference signal and an inertial navigation signal; processing the positioning signal through a multi-layer signal processing filtering model to obtain coordinate data, and generating a space coordinate; constructing a user movement situation prediction model based on the space coordinates, generating a navigation prediction track and navigation confidence, and generating a preloading strategy; acquiring geographical environment parameters of the target service area, and integrating the space coordinates, the geographical environment parameters, the preloading strategy and the virtual object through a multi-dimensional virtual-real fusion model to generate an augmented reality space scene; and analyzing the user movement track data and the augmented reality space scene through a multi-modal driving model to generate interactive content.

The invention discloses an automatic conference summary generation method based on multi-source heterogeneous information fusion. The method comprises the following steps: extracting facial features of a speaker by using a face recognition technology and recognizing a face identity of the speaker; acquiring an audio signal through a microphone array, and recognizing a voiceprint identity of a speaker by using a voiceprint recognition technology; in combination with video and audio information, through a multi-source heterogeneous information fusion technology, alignment of video and audio data is carried out in time; through a sound source positioning technology, a voiceprint identity and a face identity are matched and aligned in space, and the identity of a speaker is accurately positioned and recognized; the spokesman and the speaking content are calibrated and separated, and it is ensured that the identity of the spokesman is accurately matched with the speaking content; and generating a spokesman abstract and a conference summary according to the calibrated speech content by using natural language processing and a deep learning model. The method is suitable for various conference scenes, the speaking content of different spokesmen can be recognized, the spokesman abstract is generated, and the efficiency and accuracy of conference summary generation are improved.

The invention discloses an unmanned ship displacement attitude high-precision fusion method based on adaptive Kalman filtering, and belongs to the technical field of ship sensing positioning, and the method comprises the steps: building a discrete time state space model of a navigation data state through employing a discrete integration mode based on the navigation data state; obtaining a system state equation by means of a rotation matrix and a median theorem; obtaining an observation transfer matrix through coordinate system information, a geodesic line length between two points and an azimuth angle, and obtaining a state observation equation by combining noise data; based on the system state equation and the state observation equation, using an adaptive Kalman filtering algorithm to obtain displacement attitude information of the unmanned ship; detecting the prediction data of the unmanned ship by using a sliding window method; and when the predicted data is detected to be abnormal, substituting the predicted data into Kalman filtering for correction. According to the invention, through state modeling, fusion algorithm, anomaly detection and anomaly correction, the precision, reliability and robustness of obtaining the displacement attitude information of the unmanned ship are improved.