4363 results about "Positioning technology" patented technology

The invention discloses a small unmanned aerial vehicle inspection data management method, relates to the technical field of unmanned aerial vehicle inspection, and is used for solving the problem that unmanned aerial vehicle inspection data management is not clear. According to the invention, through a high-precision positioning technology and timestamp synchronization, multi-modal data collected by an unmanned aerial vehicle is associated with an equipment position, space-time consistency of inspection data is realized, multi-modal feature fusion and dimension reduction processing are carried out, unified feature representation is generated, the precision and efficiency of data analysis are improved, historical data and real-time collected data are combined, and the accuracy and efficiency of data analysis are improved. Equipment states are monitored in real time, fault modes are identified and classified, risks are evaluated, fault development trends are predicted through a time sequence prediction model, maintenance suggestions are generated, the timeliness of fault diagnosis is improved, based on fault detection results, routing inspection task priorities and path planning are dynamically optimized, an optimal routing inspection path is generated, and a flight path is corrected; the intelligence and the precision of the inspection task are improved, and the efficiency and the safety of the unmanned aerial vehicle inspection system are remarkably improved.

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 discloses a logistics park vehicle path planning method and system, and the method comprises the steps: obtaining the real-time position and task state information of a vehicle in a park, dynamically constructing a vehicle path planning network model, dividing a road into nodes and edges, and forming a directed weighted graph; according to the directed weighted graph, an improved multi-lane algorithm is adopted, the path length, the congestion degree and the vehicle characteristic factors are comprehensively considered, and the optimal path of each vehicle is calculated; for the optimal path, introducing a virtual lane and a time dimension, pre-judging occupation conditions of multiple vehicles on the virtual lane for each time slice, and constructing a conflict constraint matrix; obtaining park high-precision map data, extracting road network topology, lane lines and traffic sign marking elements, performing lane-level refinement on the optimal path, and generating a driving track; and through a multi-sensor fusion positioning technology, obtaining high-precision position and attitude information of the vehicle, matching the high-precision position and attitude information with the driving track, and calculating a track tracking error.

The present invention provides an intelligent robot for power inspection and an inspection method thereof, comprising a quadruped robot, wherein a laser radar, a voiceprint sensor and an infrared sensor are provided at the front end of the quadruped robot; a pan-tilt base is provided at the middle of the quadruped robot, and a camera is provided on the pan-tilt base; a six-degree-of-freedom mechanical arm is also provided at the tail of the quadruped robot, and a mechanical claw is provided at the end of the mechanical arm. The robot flexibly plans tasks based on the layout of the substation, sets inspection cycles for different equipment according to their importance, and automatically generates accurate task plans, thereby avoiding the tediousness and omissions of manual planning and improving planning. The advanced path planning algorithm enables the robot to quickly plan the optimal path, integrates multiple positioning technologies to ensure accurate navigation, and effectively reduces travel time. Multiple inspection modes are carried out in parallel, non-stop inspections are dynamic screening, routine inspections are comprehensive inspections, and non-preset inspections are flexibly targeted at specific points, which greatly improves the coverage efficiency of the inspection and avoids detection blind spots.

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 provides an aircraft and satellite cluster collaborative navigation positioning accurate control method and system, and relates to the technical field of aviation, and the method comprises the steps: an aircraft receives a multi-system navigation signal from a satellite cluster, carries out the fusion processing of multi-source navigation information through employing a self-adaptive Kalman filtering algorithm, and carries out the navigation positioning of a satellite cluster based on the fused multi-source navigation information. Calculating the accurate position of the aircraft by adopting a differential positioning technology; and transmitting the optimal control instruction to a flight control system of the aircraft based on the accurate position of the aircraft in combination with a preset flight plan, calculating a correction instruction by using a satellite-ground cooperative trajectory correction algorithm when trajectory deviation is detected, and transmitting the correction instruction to the aircraft through a high-frequency broadband communication system, and closed-loop accurate control of the track of the aircraft is realized.

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 provides an unmanned aerial vehicle infrared target positioning system fusing multi-model Kalman filtering and deep learning, and relates to the technical field of unmanned aerial vehicle navigation and positioning. The main body system comprises a data acquisition module, a multi-source data fusion module, a feature extraction enhancement module, a dynamic model optimization module, a target state estimation module, a positioning calculation module and a flight decision planning module, and the data acquisition module is provided with a high-resolution infrared sensor and accurately captures target infrared radiation information; a high-definition vision camera and a high-precision laser radar are integrated at the same time, environment data are collected in an omnibearing mode, and the data collection module can collect the environment data according to the environment complexity and the real-time change of the target motion state by adopting a self-adaptive sampling strategy based on fuzzy logic, particle swarm optimization and a self-adaptive threshold value. And the sampling frequency and the resolution of the sensor are dynamically and intelligently adjusted.

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 relates to the technical field of fault positioning, in particular to a power wire fault positioning system based on the Internet of Things, which comprises a data collection module, a fault analysis module and a data visualization module, firstly, a data collection module obtains wire data collected by a sensor on a wire in real time; secondly, the fault analysis module is used for carrying out data processing on the wire data to obtain fault data; according to the conductor data and the fault data, constructing a power conductor digital twinborn model; analyzing the fault features and the environmental parameters through a power conductor digital twinborn model to obtain an output result; analyzing the output result through a topology detection algorithm and signal sensitivity adjustment to obtain an optimized output result; obtaining a space-time propagation process formula of the fault signal through signal propagation simulation and space-time analysis according to the output result; evaluating the accuracy of an output result according to a space-time propagation process formula of the fault signal; and finally, the data visualization module generates a visualization chart and an analysis report.

The invention discloses a single-epoch GNSS-oriented PAR ambiguity precision factor determination method, and relates to the technical field of satellite navigation and positioning. Comprising the following steps: dividing pseudo-range and carrier wave observed quantities in a GNSS double-difference mathematical model into two groups, and constructing a function relationship between a floating point solution # imgabs1 # and a floating point solution # imgabs2 # variance-covariance matrix of an ambiguity vector # imgabs0 #; constructing a single-frequency single-epoch single-system P-ADOP expression and establishing a shrinkage factor k de-inversion transformation analytic expression, and simultaneously obtaining a single-frequency single-epoch single-system GNSS non-inverse transformation P-ADOP approximation formula; and performing analogy derivation on the single-frequency single-epoch single-system non-inverse transformation P-ADOP approximate formula to obtain a single-frequency multi-system or multi-frequency single-epoch GNSS non-inverse transformation P-ADOP general approximate formula. The method has important theoretical value and practical guiding significance for GNSS optimal ambiguity subset rapid selection and positioning and partial ambiguity fixing success rate influence mechanism analysis.

The invention relates to the technical field of smart bracelet positioning, and discloses a smart bracelet position tracking and navigation method, device and equipment and a storage medium, and the method comprises the steps: collecting multi-source positioning data through a built-in multi-sensor system of a smart bracelet, and carrying out the preprocessing, thereby obtaining the preprocessed multi-source data; performing feature extraction and space-time embedding processing to obtain an embedded multi-source feature sequence; inputting the embedded multi-source feature sequence into a multi-source information fusion model for processing to obtain an initial position estimation result and position uncertainty; constructing an adaptive pose map based on the initial position estimation result and the position uncertainty, and optimizing the adaptive pose map to obtain optimized position information; and a multi-level environment map is generated according to the optimized position information, and dynamic path planning is performed to obtain a navigation path and a navigation instruction, so that high-precision and low-power-consumption position tracking and navigation functions are realized, and smooth user experience is provided at the same time.

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 relates to the technical field of ocean salvage ship anchors, and discloses an ocean salvage ship anchor method which comprises the following steps: delimiting a ship anchor preliminary search range according to historical records and environmental data of a ship anchor falling area; acquiring the accurate position of the ship anchor by using a multi-mode composite positioning technology, wherein the multi-mode composite positioning technology comprises a positioning process based on sonar detection, magnetic sensing and underwater image recognition; through a multi-ship collaborative optimization scheduling system, a salvage task is allocated according to ship functions and real-time positions, a salvage ship and a salvage tool are controlled according to an optimized path, and ship anchor salvage operation is executed; and adjusting the salvage path and the operation parameters based on the real-time environment data until salvage is completed. A multi-mode composite positioning technology is adopted, and through combined application of sonar detection, magnetic sensing and underwater image recognition, the technical effect of quickly and accurately positioning the ship anchor in a complex underwater environment is achieved.

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 provides a fan blade tip visual positioning method based on multi-parameter correction, and relates to the technical field of visual localization, and the method comprises the steps: firstly controlling an unmanned aerial vehicle to reach a top view point through the position information of a fan, carrying out the target detection and feature extraction of the fan through an algorithm, and according to the position relation between a hub and a cabin, carrying out the target detection and feature extraction; a redundancy calculation mechanism for acquiring yaw information of the fan is designed; secondly, the unmanned aerial vehicle conducts pose correction based on the top view by means of visual feedback, and therefore calculation is conducted and the unmanned aerial vehicle goes to a fan front view point; then, primary positioning of blade tips is completed through a front view, and three correction parameters of a blade phase angle, a wind wheel elevation angle and a blade taper angle are measured and calculated; and finally, constructing a fan simplified model and combining the corrected parameters to realize accurate positioning. According to the invention, precision guarantee can be provided for fan inspection route planning.

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 provides a single-station earthquake positioning system and method combining multi-source features and collaborative loss, and relates to the technical field of earthquake positioning, and the single-station earthquake positioning system comprises a data preprocessing module, a model training module, and a single seismic source positioning model. Inputting three-component waveform data into a data preprocessing module by querying seismic directory information, inputting the preprocessed seismic waveform data into a single seismic source positioning model, training each network module to obtain a single-station seismic positioning system, and outputting a time sequence feature tensor, a time frequency feature tensor and a station information feature tensor; the trained seismic positioning system is used for predicting the seismic source depth and the epicentral distance, seismic waveform data collected by the station are input into the data preprocessing module to obtain standardized waveform data, the standardized waveform data are input into the seismic source positioning module, and prediction results of the seismic source depth and the epicentral distance of the station are output.

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.