3208results about How to "High positioning accuracy" patented technology

The invention provides an automatic walking and positioning method and system for a signal positioning and measuring robot in a complex environment, and relates to the field of technical positioning and measuring robots, and the method comprises the steps: obtaining an indoor three-dimensional map and multi-mode perception data, carrying out the semantic segmentation through a graph neural network, and achieving the environment matching through the combination of a point cloud registration technology; and generating robot motion track planning data. Multi-dimensional signal features are collected through a phased-array antenna system, and a signal feature distribution diagram is constructed in combination with a robot pose and an environment electromagnetic propagation model. A partitioning strategy and a multi-constraint optimization algorithm are adopted to calculate three-dimensional coordinates of a signal source, and finally, the signal source is accurately positioned through a depth calibration network.

The invention relates to a feature enhancement-based multi-modal image-text tampering detection and positioning method, which comprises the following steps of: obtaining a tampered image-text pair, respectively preprocessing an input image and an input text, and constructing a data set; constructing a multi-modal image-text tampering model, wherein the multi-modal image-text tampering model comprises a text encoder, an image encoder, an image frequency domain enhancement module, a text enhancement module, a multi-cross attention fusion module and a multi-task tampering detection and positioning head; the image frequency domain enhancement module is used for selecting an optimal frequency band and sending the optimal frequency band out of the module; performing feature fusion on the frequency domain feature output by the image frequency domain enhancement module and the image domain feature output by the image encoder to obtain an image enhancement feature; fusing the aligned image enhancement features and text enhancement features by using a multi-cross attention fusion module; and training a multi-modal image-text tampering model by using the data set, and detecting and positioning multi-modal image-text tampering. According to the invention, the feature extraction capability of the network is enhanced, and the tampering detection and positioning capability is improved.

The invention provides a multi-source information fused virtualized network fault rapid positioning method, and relates to the technical field of electric digital data processing, and the method comprises the steps: classifying and sorting historical fault events, constructing a historical dynamic evaluation model, correcting a calibration index, capturing equipment performance changes and abnormities, and enhancing the fault recognition capability. Similar faults are accurately classified and preferentially processed according to severity; in the real-time monitoring stage, real-time multi-source and abnormal data are combined, a real-time dynamic evaluation model and a correction calibration index are calculated, a ratio is applied to calculate a fine adjustment strategy, the real-time performance and accuracy of fault positioning are remarkably improved, and network dynamic changes are quickly responded. And carrying out difference analysis on the adjusted real-time model and the historical model, calibrating a preliminary fault positioning result, and improving the positioning precision and reliability.

The invention discloses an ROV positioning navigation system based on vision inertial navigation fusion and pose correction, which comprises an inertial measurement value and image preprocessing module loaded on an ROV, the inertial measurement value and image preprocessing module is connected with a vision and inertial navigation data fusion module through a wire, and the vision and inertial navigation data fusion module is connected with a pose correction module. The invention also discloses a positioning navigation method of the ROV positioning navigation system based on visual inertial navigation fusion and pose correction. According to the method, the inertial data provided by the IMU and the environment characteristic information captured by the visual sensor are fused, high-precision estimation of the position and attitude of the ROV can be realized, the sensor data are optimally fused by using the MSCKF algorithm, the influence of the error of a single sensor on the positioning result is reduced, and the positioning precision and reliability of underwater operation are remarkably improved.

The invention relates to a multi-article identification and distinguishing method, system and device and a medium. The method comprises the steps of obtaining multi-view image data of articles in a target area; performing object detection and instance segmentation on the multi-view image data by using a deep learning algorithm to obtain preprocessed image data; acquiring a first spatial position of the object in the target area by using a depth sensor, and performing spatial correction by combining the preprocessed image data to obtain depth information of the object in the target area; in combination with the preprocessed image data and depth information, performing secondary positioning on articles in the target area through a multi-modal data fusion algorithm, optimizing the distinguishing between the articles in the target area by using spatial correlation, confirming the identity information of each article in the target area, and generating an identification result; and matching the identification result with the article state in a preset database to generate a matching result, and feeding back the matching result to the user side. The method has the effect of improving the recognition precision of the borrowing machine.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle self-adaptive navigation system which comprises an inertial navigation system, a visible light sensor, a data fusion module, a database and a navigation control module. The inertial navigation system provides attitude, speed and acceleration data; the visible light sensor collects an image and obtains environment feature points through deep learning algorithm processing to realize visual positioning; and the data fusion module fuses two kinds of sensor data by using a dynamic weighted Kalman filtering algorithm, and dynamically adjusts the weight according to the environment to optimize the positioning information. The map library and the feature library store environment data to assist in positioning and dynamically update; the navigation control module generates an instruction according to the fused positioning information, and automatically adjusts a flight strategy by considering various factors based on an adaptive control algorithm. The system improves the positioning precision and environmental adaptability, enhances the flight safety and stability, is high in intelligence and autonomy degree, has flexibility and expandability, and can be widely applied to the field of unmanned aerial vehicles.

The invention discloses a fault detection method and system for a mine unmanned vehicle, and relates to the field of positioning navigation, and the method comprises the steps: collecting the data of each sensor; according to the laser radar data, a slam point cloud map is constructed through a cartographic algorithm; according to the IMU data and the vehicle speed data, performing fusion through a Kalman filtering algorithm to obtain odometer data; the preprocessed laser radar data, IMU data, vehicle speed data, camera data and odometer data are converted to a slam point cloud map coordinate system through coordinate conversion, fusion is carried out through a Kalman filtering algorithm, and state estimation of the unmanned vehicle is obtained; taking the state estimation of the unmanned vehicle as a predicted value, comparing the predicted value with laser radar data, IMU data, vehicle speed data and camera data, and calculating an observation residual error; and performing unmanned vehicle fault detection according to the observation residual error. Aiming at the low positioning precision of the mine unmanned vehicle in a complex environment in the prior art, the positioning precision is improved through multi-sensor fusion, fault-tolerant switching and the like.

The invention discloses a multi-mode adaptive fusion navigation positioning method and system in a satellite navigation denial environment, the system adopts GNSS / IMU / visual sensors, and the method comprises the following steps: receiving data of the three sensors and completing modeling; initializing the three sensors; judging the strength of the GNSS signal by utilizing chi-square test; optimization variables are set, a residual matrix is constructed, and nonlinear optimization is carried out; setting a GNSS trust factor when the GNSS signal is weak or denial, and calculating a weight; and weighting and fusing sensor data, outputting optimal state estimation of the system, and realizing continuous navigation and positioning. According to the method, a self-adaptive weight adjustment mechanism is established, GNSS trust factors are introduced, and the weights of multi-sensor data are adjusted in a self-adaptive manner, so that the positioning difficulty under the conditions that the GNSS is subjected to deception interference, suppression interference, building blocking and the like can be overcome, and continuous navigation positioning of an unmanned system (such as a robot, an unmanned vehicle, an unmanned aerial vehicle, an unmanned ship, an unmanned underwater vehicle and the like) is realized.

The invention discloses an environment-adaptive high-precision Beidou visual SLAM (Simultaneous Localization and Mapping) cooperative localization optimization method. The method comprises the following steps: step 1, acquiring environment characteristic data, visual SLAM data and Beidou system data; 2, constructing an environment self-adaptive model, wherein the environment self-adaptive model adjusts the weight distribution of the visual SLAM and the Beidou system according to the environment characteristic data; and step 3, performing data fusion on the visual SLAM data and the Beidou system data according to respective weight distribution to form a unified positioning result. Through the environment adaptive model, the Kalman filtering algorithm is improved, and the mechanism is adjusted in real time, so that the cooperative positioning precision and robustness of the visual SLAM and Beidou system are effectively improved.

The invention relates to the technical field of synchronous positioning and map construction, and discloses a multi-sensor fusion SLAM method and system based on data quality comprehensive evaluation, and the method comprises the steps: carrying out the multi-dimensional quality evaluation of laser radar point cloud data and camera image data, and enabling the evaluation indexes to comprise the scene degradation degree, the view field integrity and the intensity information entropy of a point cloud, according to the point cloud data and the definition, self similarity and natural quality of the image, calculating the comprehensive weight of each index by adopting a subjective and objective weight fusion method, and dynamically selecting a laser radar pose, a visual sensor pose or an IMU integral pose increment as an initial value according to the comprehensive quality score of the point cloud data and the image data; and matching optimization of the point cloud and the map and global pose map optimization are carried out. According to the method, the sensor data quality is evaluated in real time, and the fusion strategy is dynamically adjusted, so that the positioning precision and robustness of the SLAM system in a complex environment are remarkably improved, and meanwhile, the calculation complexity is reduced.

The invention discloses a dynamic environment dense point cloud SLAM (Simultaneous Localization and Mapping) method and system based on YOLOv11 and ORB-SLAM 3. According to the method, real-time target detection and image segmentation technologies of a YOLOv11 model are integrated into an ORB-SLAM 3 framework, so that high-precision and robust visual SLAM in a dynamic environment is realized. A traditional convolution layer is replaced by using a balanced convolution method GSConv layer in a YOLOv11 model, and a traditional C2f layer is replaced by using a new feature fusion module VoVGSCS layer, so that a Neck structure of the YOLOv11 is improved, and a lightweight network model is realized. Experimental data prove that the pose estimation precision of the method in a dynamic environment is remarkably superior to that of an existing visual SLAM algorithm.

The invention relates to a UWB-based man-machine real-time positioning method in a long-distance tunnel hole. The method comprises the following steps: S1, collecting original ranging information of integrated signal characteristics; s2, correcting an original distance based on signal quality; s3, carrying out NLOS signal identification based on SVM (Support Vector Machine); s4, NLOS ranging error elimination based on an error distribution fitting model; s5, preliminarily resolving a tag position stake number; s6, label position correction based on Kalman filtering; and S7, determining a man-machine position based on label matching. According to the invention, the UWB positioning system is used for positioning and tracking personnel machinery in a hole, so that real-time transmission and analysis of man-machine position pile number information in the hole are realized, and management personnel can timely and accurately master man-machine operation positions; according to the man-machine real-time positioning method, the influence of the complex construction environment in the tunnel hole is reduced, and the man-machine positioning precision and stability in the tunnel hole are effectively improved.

The invention relates to the technical field of radio orientation, in particular to a measuring method capable of tracking and recording motion trail information of a vibrating rod, which comprises the following steps: arranging at least four UWB (Ultra Wideband) base stations, fixing UWB transmitters at the joint of the vibrating end of the vibrating rod and a rubber tube, and fixing IMU (Inertial Measurement Unit) sensors at the vibrating end of the vibrating rod; according to the data output by the UWB transmitter and the IMU sensor, the position and posture of the vibrating rod in the detected target mold are calculated through a positioning algorithm, and the motion trail of the vibrating rod is further calculated; and according to the vibrating time, the vibrating area and the vibrating area overlapping degree of the vibrating rod, the vibrating state of the concrete is analyzed. The UWB technology and the IMU technology are fused, so that high-precision positioning of the vibrating rod can still be kept under the ground where wireless signals such as GPS signals cannot be transmitted, and the vibrating quality can be conveniently detected and evaluated.

The invention relates to the field of environment monitoring, and discloses a mobile VOCs rapid detection and positioning system, which comprises a data acquisition module, a data fusion module, a concentration gradient analysis module, a Bayesian filtering module and a particle filtering optimization module, the data acquisition module is used for acquiring VOCs concentration data and multi-source positioning data; the data fusion module outputs high-precision position information for the positioning data; the concentration gradient analysis module is used for calculating a concentration gradient based on the VOCs concentration data and meteorological information and simulating concentration field distribution; the Bayesian filtering module dynamically updates the probability distribution of the pollution source position in combination with the concentration field distribution and the fusion position information; and the particle filtering optimization module optimizes the Bayesian filtering result. The system is suitable for various complex environments, can realize rapid detection of VOCs and high-precision positioning of pollution sources, and has good real-time performance, precision and environmental adaptability.

The invention discloses a perception fusion method of an unmanned driving system, and relates to the field of unmanned driving, and the method comprises the steps: obtaining a laser point cloud, image data and millimeter wave radar data; a Zhang's calibration method is adopted to obtain internal parameters of a camera, and a first pose transformation relation of a camera coordinate system relative to a laser radar coordinate system is solved; a second pose transformation relation of the millimeter wave radar relative to the laser radar is obtained by using the millimeter wave reflector; a Point RCNN method is adopted to obtain first target information; obtaining target information II by adopting a YOLOv8 method; obtaining target information III by adopting a clustering algorithm; and carrying out coordinate unification on the target information by utilizing a pose transformation relation, fusing the multi-sensor target information under a unified coordinate system by utilizing an unscented Kalman filtering algorithm to obtain target state estimation, and generating an unmanned driving control instruction. The target sensing precision is improved aiming at the low target sensing precision of multiple sensors under the unmanned driving scene of the well industry and mining in the prior art.

The invention relates to the technical field of crane control, in particular to an intelligent tower crane anti-collision accurate hoisting system based on multi-sensor fusion, which comprises a data acquisition module, a data processing module, a safety early warning module, a control execution module, a data processing module, a data processing module and a control module, an alarm module electrically connected with the safety early warning module and the data processing module and used for receiving the alarm signal sent by the safety early warning module; a hoisting control instruction is generated based on the alarm signal and the real-time three-dimensional position coordinates of the hoisted object sent by the data processing module; according to the method, the lifting control instruction is executed, the lifting arm angle, the rotation angle and the lifting height of the tower crane are adjusted, accurate lifting and anti-collision control are achieved, and the safety, accuracy and efficiency of the tower crane are comprehensively improved through organic combination of advanced technologies such as multi-sensor fusion, an intelligent algorithm and self-adaptive control.

The invention provides a solid-state laser radar and IMU (Inertial Measurement Unit) robust tight coupling system under the assistance of point cloud intensity, which comprises the following steps of: firstly, pre-processing a solid-state laser radar point cloud by the system, eliminating point cloud defect points, correcting a point cloud intensity value and performing down-sampling; and then geometric feature extraction and intensity feature extraction are carried out on the preprocessed point cloud, and intensity calibration of the laser radar point cloud is carried out. And then compensating the motion of the point cloud by using the high-frequency prior information predicted by the IMU. For geometric and intensity feature point clouds, a classical registration method and a fast plane fitting method are respectively adopted to calculate residual errors, and an intensity feature assisted point cloud residual error measurement model is constructed. And a robust strategy based on an IGGIII robust weight function is designed by using a residual error estimation value in a filtering algorithm. According to the invention, on the premise of keeping relatively low calculation cost, the positioning and mapping capabilities and robustness of the small-view-angle laser radar tight coupling system in a complex environment are improved.

The invention discloses an overhead line system cable state real-time monitoring method and sensing system based on distributed optical fiber sensing. The Mach-Zehnder interference and phi-OTDR fusion type distributed optical fiber sensing system comprises a first light source, a second light source, a Mach-Zehnder interferometer, a phi-OTDR and a data acquisition and processing module. Raman amplifiers are arranged at the two ends of the Mach-Zehnder interferometer, so that the sensing distance is prolonged; the optical fiber sensing system extracts an interference optical signal and a backward Rayleigh scattering optical signal corresponding to a fault event after phase modulation when the fault event occurs; then processing the extracted backward Rayleigh scattering light signal to obtain intensity change information of a fault event, and demodulating the position of the fault event; and sending the two-dimensional optical signal into a pre-trained Swin Transform network model to extract a time domain feature, a frequency domain feature and a spatial domain feature, and identifying the type of a fault event through a classifier.

The invention discloses an IMU (Inertial Measurement Unit) and RGB-D (Red, Green and Blue-Depth) fusion-driven real-time three-dimensional modeling method and system for buildings and structures, belongs to the field of three-dimensional reconstruction, and solves the problem that a fixed sampling rate is adopted in the prior art. The method comprises the following steps: acquiring data of an RGB-D camera in real time based on an outdoor scene, wherein the data comprises IMU data acquired by an IMU sensor, a depth image acquired by the camera, a color image and a camera pose; after the sampling rate of the RGB-D camera is dynamically adjusted by a self-adaptive sampling rate adjustment algorithm based on IMU data, the RGB-D camera scans the building structure to obtain data of the RGB-D camera; recognizing and rejecting the dynamic feature points based on the result and a dynamic feature perception pose resolving optimization algorithm to obtain a static feature pose of the RGB-D camera; progressive point cloud replacement is carried out on point clouds corresponding to the depth image and the color image based on static feature poses and point cloud comprehensive precision evaluation of the RGB-D camera, and outdoor scene progressive three-dimensional modeling of real textures is carried out in combination with structural feature constraints. The method is used for real-time three-dimensional modeling of buildings and structures.

The invention provides a sewer internal defect detection method and system based on YOLOv11 improvement, and the method achieves the dynamic weight fusion of multi-level features through a self-adaptive feature fusion module, and improves the detection capability of a model for small targets and complex backgrounds. The selectivity of channel characteristics is improved through an SENet attention mechanism, and background interference is reduced; and introducing an MPDIOU loss function to improve bounding box regression loss. The performance of the improved model in detection tasks of sewer defects such as cracks, obstacles, corrosion, deformation and the like is remarkably superior to that of original YOLOv11, and particularly key indexes such as mAP50 (average precision), Box precision (P), recall rate (R) and the like are remarkably improved. According to the invention, efficient and accurate detection of multiple types of defects in the sewer can be realized, and a better solution is provided for maintenance and management of the sewer.

The invention provides a light guide plate surface defect detection method based on a GCC-YOLOv8 network model. The method comprises the following steps: acquiring an image of a light guide plate to be detected; the image is input into a GCC-YOLOv8 network model for defect detection; outputting surface defect information of the light guide plate based on a detection result; wherein the GCC-YOLOv8 network model is constructed based on a YOLOv8 network model, and comprises a backbone network unit, a neck network unit and a detection head network unit with three parallel detection branches, which are arranged in sequence; two GKA-C2f1 modules are arranged at the tail end of the backbone network unit; the neck network unit comprises an FPN network unit and a PAN network unit; the FPN network unit comprises two up-sampling modules, two C2f2 modules, two Concat modules, an SPPF module and a CPCA module; the PAN network unit comprises two CBS modules, two Concat modules, two C2f2 modules, three CPCA modules, a CFC module and an SFC module. The light guide plate surface defect detection method has good light guide plate surface defect detection performance and robustness, and also has good detection speed.

The invention relates to the technical field of satellite navigation and positioning, and discloses a Beidou precise point positioning method based on double-frequency combination, which comprises the following steps of: firstly, acquiring an original observation file and various correction files of a double-frequency signal from an IGS official website; a clean observation value is obtained through the steps of data validity checking and data preprocessing; carrying out correction and correction to obtain an error correction value; establishing a Beidou dual-frequency ionospheric elimination combination model, and performing dual-frequency DCB correction on the combination observation value to obtain a corrected dual-frequency combination observation value; constructing a double-frequency ionosphere elimination observation equation, and performing coordinate solution by using Kalman filtering to obtain a positioning result of double-frequency combination; analyzing the positioning result of the double-frequency combination, counting the error value of the positioning result, and evaluating the positioning precision and the convergence time; according to the invention, when DCB correction is considered, a dual-frequency ionosphere-eliminating PPP positioning model is established by using a BDS signal, so that the positioning precision can be remarkably improved, and the convergence time can be shortened.

The invention discloses a puncture robot tumor ablation method and system based on magnetic resonance. The method comprises the following steps: scanning a focus through medical imaging examination to obtain a preoperative magnetic resonance image; reconstructing to obtain a three-dimensional model of the focus and surrounding tissues, and planning to obtain an optimal puncture path; updating the position relation between the needle point of the puncture needle and the focus center in real time, feeding back deviation information of the needle point position, and guiding the puncture needle to a tumor ablation area in real time; two-way ablation is adopted, real-time temperature monitoring and whole blood circulation dynamic monitoring are conducted, and ablation power and time are correspondingly adjusted; the ablation effect is consolidated by adopting a postoperative wound management and closing technology; the ablation effect is evaluated through magnetic resonance imaging examination and postoperative biopsy. According to the puncture robot tumor ablation method based on magnetic resonance, tumor ablation can be achieved, temperature and blood flow monitoring is effectively carried out, and the ablation effect and the residual focus condition are reflected in real time through a comprehensive postoperative evaluation method.

The invention relates to a medical image target detection method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining a to-be-detected medical image and a medical image target detection model, according to the medical image target detection model, an expandable residual module is added in a backbone network, a multi-branch attention mechanism module is added to a head in a neck network, and at least part of conv modules in the backbone network and the neck network are replaced by YOLOv8 models of deep down-sampling modules. The medical image target detection model is obtained through joint regression loss function training; and inputting the to-be-detected medical image into the medical image target detection model to obtain a medical image target detection result. According to the medical image target detection method provided by the invention, the accuracy of medical image target detection is improved.

The invention relates to a double-end traveling wave fault location method based on weighted Minkowski distance and cubic spline interpolation. The method comprises the following steps: performing smooth fitting expansion processing on fault waveform data W through cubic spline interpolation; training a traveling wave head clipping model, and automatically positioning and clipping a wave head region Wcs; respectively recording the Wcs after the traveling wave processing at the two ends as Wa and Wb, translating one waveform, and calculating a weighted Minkowski distance dist; when dist is minimum, the translation amount is the time difference delta t when the traveling wave reaches the two ends for the first time. Through cubic spline interpolation, under the condition of low sampling rate, smooth fitting expansion is carried out on the signal waveform, the actual error range is obviously reduced, and the positioning precision is improved; according to the method, the weight of the distance near the fault moment is increased, the effect of highlighting the fault point is achieved, for the noisy waveform, noise reduction and other operations which can change the original waveform can be not carried out, the time difference delta t of the wave heads of the two fault traveling waves can be calculated, and the measurement error existing in the practice of the prior art is reduced.

The invention provides an object positioning method and device based on wireless devices, and the method comprises the steps: obtaining a plurality of pieces of initial channel state information of a plurality of wireless devices in response to an indoor positioning instruction, and enabling the initial channel state information to represent device data which are collected by the wireless devices and are related to a target object; for each piece of initial channel state information, performing deviation elimination processing on the initial channel state information based on a preset deviation elimination formula to obtain target channel state information; according to the multiple pieces of target channel state information, multiple data combination matrixes are generated, and each data combination matrix comprises multiple matrix row and column values; for each data combination matrix, processing a plurality of matrix row and column values and position coding data of the wireless device corresponding to each matrix row and column value by using a target encoder, and generating a coordinate data set of the target object; and performing weight fusion processing on the plurality of coordinate data sets to obtain target positioning information of the target object.

The invention provides a mine personnel positioning method and system based on wireless communication, and relates to the technical field of mine safety, and the method comprises the steps of data collection, model construction, structure prediction, base station position and number definition, UWB base station installation, base station parameter setting, personnel positioning and positioning display. The system comprises a data acquisition module, a model construction module, a structure prediction module, a base station position and number definition module, a UWB base station installation module, a base station parameter setting module, a personnel positioning module and a positioning display module. According to the method, the installation positions and the number of the UWB base stations are defined, the UWB base stations are installed and base station parameters are set according to the coal mine channel prediction number and the terrain prediction structure, personnel positioning and displaying are carried out through the UWB base stations, the signal interference influence of the coal mine channel number and the dynamic change of the terrain structure on radio waves in the channel propagation process is overcome, and the working efficiency is improved. The purpose of improving the positioning accuracy of the underground personnel is achieved.

The invention discloses a scale-effect-free mine dynamic disaster fine monitoring and dynamic early warning method, which comprises the following steps of: arranging a multi-point micro-seismic monitoring network on a coal mine working face, and continuously acquiring and preprocessing micro-seismic signals; an innovative analysis positioning algorithm is adopted to accurately analyze the accurate position and time information of the micro-seismic source; in combination with signal features and positioning data, a four-dimensional joint kernel density estimation and confidence interval theory is innovatively applied, a rock burst risk assessment model is constructed, and a danger level classification standard is determined; and the system calculates risk indexes in real time and dynamically issues early warning. According to the invention, the precision, timeliness and reliability of monitoring and early warning of the dynamic disasters in the mining area are obviously improved, and key technical support is provided for safe production of a coal mine.

A novel hinge welding and positioning tool comprises a copper rod, a brass plate installed on the copper rod and a red copper plate arranged on the brass plate, an execution command comprises a jacking air cylinder, a cylindrical rod fixed to the jacking air cylinder and an air cylinder top plate of the cylindrical rod, and the cylindrical rod and the air cylinder top plate are vertically arranged; during use, a to-be-welded plate and a hinge positioning hole are combined and then placed on the plane of the red copper plate, the jacking air cylinder is controlled by an air channel of the throttling valve to execute actions, jacking is conducted from the bottom face of the red copper plate, and therefore the space position concentricity of the hinge and the plate is limited and fixed. Plate welding can be carried out through a welding robot or other manual welding machines and the like, after welding is completed, the jacking air cylinder returns to the zero position, after the cylindrical rod is loosened, the material taking action is manually completed, the jacking air cylinder can stretch out and draw back or jack up according to the actual situation, a welding tool does not need to be replaced due to product replacement, plate welding can be carried out according to various situations, and the welding efficiency is improved. And the technical effect of efficient and rapid plate welding operation is achieved.

The invention belongs to the technical field of underwater positioning, and discloses an underwater single-beacon Gaussian Newton positioning method and system under a linear sound profile condition. According to the method, underwater acoustic signal propagation time observation noise is modeled into Gaussian distribution, a maximum likelihood estimation problem is constructed, and then the maximum likelihood estimation problem is converted into a nonlinear least square estimation problem; and solving the gradient of the observed quantity to the position coordinates of the AUV based on a chain rule, iteratively solving a nonlinear least square estimation problem based on a Gauss-Newton method, and obtaining the position coordinates of the AUV after iterative convergence. Compared with other single beacon positioning methods which do not consider sound propagation refraction, the method of the invention can obtain a more accurate position calculation result under a linear sound profile condition, and enhances single beacon positioning, especially the practical application capability of the method in deep sea areas.