48results about How to "Accurate reconstruction" patented technology

A kind of time domain ground penetrating radar intelligent forward method based on physical parameter guidance, comprising the following steps: step one: build underground medium model and generate supervision data;Step two: the ground penetrating radar GPR forward simulation is expressed as the mapping problem of " physical parameter field to radar profile ";Step three: build PhyTANet network based on encoder decoder;Step four: the PhyTANet network obtained by step three is trained, fine-tuned;From the above steps, finally, after training fine-tuning ends, the dielectric constant and conductivity distribution of any given underground medium are input, and the corresponding prediction result is obtained.The purpose of the present application is to solve the technical problems of the existing ground penetrating radar forward technology, such as insufficient accuracy, insufficient modeling of key physical parameter coupling relationship and limited time domain feature expression in complex heterogeneous medium, and the present application is proposed.

This invention discloses a hip joint prosthesis for bone defects, comprising a crescent-shaped acetabular rim filler assembly, a bone-metal defect block, an acetabular wall defect support block, a cylindrical support block, an angle pad, a locking screw, a locking nut, an assembly screw, an acetabular sealing screw, and an acetabular support block. The crescent-shaped acetabular rim filler assembly includes two sets of crescent-shaped acetabular rim fillers, which are assembled by engaging pins through pin holes. The outer circumference of the bone-metal defect block of the acetabular support block is connected to the crescent-shaped acetabular rim filler assembly via bone cement. This hip joint prosthesis for bone defects effectively fills irregular defect areas of the acetabular rim. The various screw holes and Kirschner wire holes on its surface provide multi-dimensional fixation options. The overall structure, through the synergistic action of multiple components, achieves precise reconstruction while ensuring biomechanical performance, significantly reducing the risk of postoperative prosthesis loosening and wear.

The invention discloses a face gear on-machine measurement comprehensive error compensation method based on sequential decoupling. Specifically, a sequential decoupling compensation strategy is provided for solving the problem that the measurement precision is limited due to mutual coupling of a measuring head pre-stroke error and a workpiece installation coordinate system error in the prior art. The method comprises the following steps: firstly, establishing a measuring head pre-stroke error model through standard ball calibration, and performing anisotropic compensation on a tooth surface measuring point by utilizing Delou inner triangulation and a weighted interpolation method; secondly, based on the compensated data, the outer cylindrical surface and the tooth top plane of the face gear are accurately fitted through an iterative optimization algorithm, an accurate measurement coordinate system is established, and the initial phase of the C axis is optimized; and finally, reconstructing an actual tooth surface by adopting a two-stage NURBS curved surface fitting method, and calculating a normal deviation. According to the method, an error propagation chain is effectively cut off through a strict execution sequence, the overall precision and reliability of on-machine measurement are remarkably improved, and a key technical support is provided for face gear closed-loop manufacturing.

This invention provides a physical information neural network modeling method for the hysteresis characteristics of piezoelectric fast-acting servo systems, belonging to the technical field of hysteresis characteristic modeling for piezoelectric fast-acting servo systems. The method includes the following steps: based on the input voltage of the piezoelectric fast-acting servo system at multiple sampling times up to the current time, an expert feature vector for the current time is constructed through an expert feature layer based on a memory path; static and dynamic representations are extracted through static and dynamic branches respectively; the static and dynamic representations are fused through a feature representation layer to obtain a fused representation for the current time; and the fused representation for the current time is mapped to a scalar displacement prediction of the piezoelectric fast-acting servo system through a regression head. The advantage of this invention is that it improves the accuracy and robustness of modeling the hysteresis characteristics of piezoelectric actuators.

This invention discloses a broadband oscillation disturbance source localization method based on compressed sensing and graph convolutional neural networks (GCNs), comprising two stages: offline training and online localization. In the offline training stage, electrical quantities under subsynchronous / supersynchronous broadband oscillation modes are obtained using measurement data from actual systems or simulation examples, constructing a broadband oscillation offline sample library. Compressed sensing is used to encode and compress the electrical quantities in the offline sample library, obtaining a training set and a test set. The constructed GCN localization model is trained using the training set until the test set reaches the target localization accuracy, resulting in a broadband oscillation localization model. In the online localization stage, a substation collects electrical quantity data and compresses and encodes it using compressed sensing technology; the substation data is uploaded to the master station; at the master station, the feature matrix and system adjacency matrix are input into the trained GCN localization model, and the oscillation source location is output. This invention's method is applicable to broadband oscillation disturbance source localization under various operating conditions.

This invention relates to the field of machine vision technology and discloses a robust extraction method for the center line of structured light in fillet welds. It aims to solve the problem of missegmentation and loss of corner features caused by traditional algorithms relying on geometric connectivity segmentation under strong arc light and high reflectivity interference. The invention first obtains a light stripe backbone mask through multi-scale morphological filtering and clustering screening; secondly, it constructs a scattered data pool by performing global coarse extraction within the mask; subsequently, it uses a sequential random sampling consensus algorithm to adaptively extract the equations of the upper and lower main lines and calculate the theoretical intersection point through a "fit-stripping" iterative mechanism; finally, it extends along the main line towards the intersection point and performs independent pixel grayscale thresholding and sub-pixel fine extraction within a local window of the original image. This invention completely eliminates the risk of missegmentation, accurately repairing large-scale reflective fractures while perfectly preserving and reconstructing the sharp corner features of the fillet weld.

This invention relates to the field of optical detection technology and discloses a method for measuring the three-dimensional topography of an object surface based on optical interferometry. The method includes: acquiring a mixed temporal-domain interferometric signal sequence during axial scanning; extracting seed pixels and constructing a spatial topological connectivity feature matrix; introducing a fractional Fourier transform to construct a joint decoupling objective function containing a frequency-domain nonlinear phase distortion evaluation index and a temporal-domain physical invariant evaluation index; performing multi-level optimization for the seed pixels to lock the globally optimal rotation angle; performing array adaptive decoupling in conjunction with the spatial topological connectivity feature matrix to generate the optimal rotation angle matrix; and performing signal decoupling and reconstruction on the mixed signal to extract the absolute surface height of each pixel to generate three-dimensional topography data. This invention solves the phase decoupling problem caused by thin-film interferometric aliasing through multi-dimensional joint decoupling and spatial prior constraints, improving measurement accuracy while reducing the computational overhead of large pixel arrays.

The application discloses a kind of active implantable medical device in-vivo radio frequency response modeling method based on equivalent lumped element model, by constructing and matching the electromagnetic characteristics of the actual conductive path of AIMD and surrounding tissue, equivalent lumped element model (LEM), the key factors such as the multiscale electrical characteristics of the geometry size of implantable medical device and its in homogeneous, non-uniform even real anatomic tissue environment are parameterized characterization, so as to quickly and accurately reconstruct the radio frequency transfer function of implant.The method of the application effectively breaks through the bottleneck of the existing Tier 3 evaluation method, meets the technical requirements of the latest standard based on Tier 4 evaluation method for modeling the radio frequency transfer function of implant in the environment closely related to clinical tissue, and provides a new technical path and implementation means for efficient and accurate evaluation of the radio frequency heating risk of implantable medical device in the magnetic resonance environment.

The application discloses a motor controller sampling delay error compensation method, a motor controller and a motor driving system. The method comprises the following steps: obtaining operation parameters comprising motor physical model parameters, real-time state parameters, pulse width modulation parameters and a preset sampling delay time; calculating a threshold time based on the pulse width modulation parameters; determining a target calculation model according to the size relationship between the sampling delay time and the threshold time; dividing the sampling delay time into one or more time segments according to voltage vector switching points, and calculating the real-time current change slope of each segment; multiplying each segment by the corresponding slope and accumulating the current error amplitude, thereby correcting the current sampling value. Thus, the real current value lost due to hardware delay is accurately calculated, so as to solve the current sampling delay error problem.

The application discloses a kind of physical constraint under aero-magnetic data deep learning forward and inversion method and system. Including: constructing the forward network based on deep operator network DeepONet, the network adopts logarithm-linear dual space mechanism, to log resistivity profile and flight height as input, predicts log quadratic field;Inversion network is constructed, with the input of observed transient electromagnetic response and flight height, and the output of resistivity profile;When training, the forward network is pre-trained to convergence under data-driven loss and Maxwell equation physical constraint loss;Inversion network is trained under the joint supervision of its model space loss and physical data space loss, wherein the physical data space loss is obtained by comparing the re-synthesized response with the original observation after inputting the resistivity profile predicted by the inversion network into the frozen forward network.The application deeply integrates physical laws into data-driven models, and realizes the integrated efficient solution of aero-magnetic forward and inversion.

This invention discloses an online detection method and device for the trajectory of sphere polishing, relating to the field of precision machining online detection. The method includes: S1, acquiring a continuous image sequence during the sphere polishing process at time i using a high-speed camera; S2, preprocessing and feature recognition of the continuous image sequence; S3, a rotation angular velocity measurement module calculating the rotation angle and rotation angular velocity to obtain theoretical values; and a three-dimensional trajectory reconstruction module radially projecting discrete trajectory points onto a sphere surface determined based on the first point, to achieve visualized three-dimensional trajectory reconstruction under geometric constraints, thus completing the online detection of the sphere polishing trajectory. This invention combines a transparent grinding disc structure, a multi-view observation layout, and a dedicated image processing algorithm to achieve real-time, high-precision, and high-efficiency online detection of the sphere's rotational motion parameters and trajectory during the polishing process.

The invention discloses an SAA-DoA high-precision direction finding method based on LSTM time sequence prediction, and the method comprises the following steps: sequentially switching a plurality of antenna units through a sequential sampling mode of a switch antenna array, and obtaining the signal sampling data of a target direction; performing phase processing on the signal sampling data, and extracting an instantaneous frequency time sequence representing carrier frequency drift; inputting the instantaneous frequency time sequence into a pre-trained long short-term memory network model, learning a dynamic change rule of carrier frequency drift through the model, and predicting an instantaneous frequency value of an unsampled time slot; based on the predicted instantaneous frequency value, reconstructing a complete parallel sampling data matrix through phase integral operation; based on the reconstructed parallel sampling data matrix, an array signal processing algorithm is adopted to calculate the arrival angle of the signal; according to the method, the precision of DoA estimation under the SAA architecture and the robustness in a real wireless environment are remarkably improved.

This disclosure provides a method, apparatus, electronic device, and storage medium for lung segment reconstruction. It calls a preset algorithm to reconstruct lung segments from lung imaging data, generating initial lung segment reconstruction data. The reconstructed data is then validated to identify deviated lung segments with reconstruction deviations at their junctions. Based on the reconstruction instructions for these deviated lung segments, a lung segment assembly is reconstructed. Based on the data collected from the lung segment assembly, the junctions of each lung segment within the assembly are reconstructed and repaired to obtain the final lung segment reconstruction data. Compared to related technologies, this disclosure, by calling a preset algorithm for lung segment reconstruction, can utilize advanced algorithmic technology to initially outline the lung segment structure. Then, by validating the reconstructed data, it can accurately identify deviated lung segments with reconstruction deviations at their junctions. For these deviated lung segments, a lung segment assembly is reconstructed according to their specific reconstruction instructions, ensuring accurate reconstruction when dealing with problematic areas.

The invention discloses an automatic test system and method based on a relay protection device, and relates to the technical field of relay protection device analysis. In the relay protection test process, electrical time sequence data and device state signals in the test stage are collected in real time, data are processed, and a scene data unit capable of being independently stored and analyzed is generated; constructing a test path transfer directed graph; predicting a subsequent stage candidate set according to the test path transfer directed graph; forming a to-be-executed candidate list according to the predicted subsequent stage candidate set in combination with the current available resource set; analyzing a resource conflict relationship among the test items, and generating an optimized test sequence; when it is detected that the test process is abnormally interrupted, a continuous test execution instruction is generated by conducting mode matching on the context and a historical successful continuous test case, the test is recovered from the breakpoint, and meanwhile the interruption and recovery mode is stored in a historical continuous test case library. And the success rate and the safety of continuous testing are improved.

The invention discloses a hyperspectral anomaly detection method based on spatial spectrum attention enhancement and a blind spot U-Net network, the overall architecture of the network is in a U-Net form, and interference of an abnormal target on subsequent feature extraction is eliminated through mask convolution and down-sampling operation. Firstly, in order to effectively capture global information, a spectrum-space self-attention module is embedded in a network; particularly, for a spatial self-attention module, in order to prevent blind spot information from interfering subsequent feature extraction, a mask is added to an attention map to meet blind spot requirements. In addition, in order to perceive the multi-class attributes of the background, an attention module based on a sequence dictionary is designed in the potential layer space. The multi-class attributes of the background are perceived through an additional dictionary, so that the background expression is effectively enhanced, and the anomaly detection precision is improved. And after the network converges, obtaining an anomaly detection result through a reconstruction error. According to the method, target features can be enhanced for interference of small targets, complex backgrounds and the like, so that a relatively good detection effect is achieved.

This invention belongs to the field of blade trajectory monitoring technology, specifically involving a method for dynamic monitoring of the three-dimensional trajectory of wind turbine blades by fusing imagery and laser point cloud data. The steps include: fixing a camera and scanner with a rigid connection device for use as a dynamic monitoring system for the three-dimensional trajectory of the wind turbine blade; calibrating the camera; calibrating the relative position and attitude between the camera and scanner; acquiring image data and laser point cloud data during normal operation of the wind turbine; performing motion compensation correction on the laser point cloud data; calculating the three-dimensional coordinates corresponding to the monitoring point; obtaining a time-series image-side coordinate sequence of the monitoring point; and calculating the three-dimensional coordinate sequence corresponding to the time-series image-side coordinate sequence of the monitoring point, i.e., the three-dimensional motion trajectory of the monitoring point on the wind turbine blade during operation. This invention uses imagery and laser point cloud technology to monitor the three-dimensional trajectory of wind turbine blades, requiring no auxiliary markers, eliminating complex calibration, offering flexible deployment and high accuracy, and efficiently supporting blade safety assessment and maintenance decisions.

The application discloses a five-dimensional seismic data interpolation method and system based on coordinates, constructs a point-by-point seismic data interpolation model based on a NeRF theory, modifies an MLP framework by combining an additional convolution layer on the basis of the point-by-point interpolation model, constructs a profile-by-profile seismic data interpolation model, introduces kernel norm regularization to construct an objective function, reads original five-dimensional seismic data D obs and inputs the five-dimensional seismic data with missing traces into the network to realize reconstruction of the five-dimensional seismic data. The application eliminates the need for additional labeled data, utilizes unique characteristics of seismic data, enables a convolution network decoder to output data profile by profile, can significantly improve data processing efficiency by 40 times, and increases kernel norm regularization in the objective function, so that the noise resistance and robustness of the model can be effectively improved.

The present application relates to finite time reduced dimension observer design method and chaotic communication system, belong to information security and chaotic communication field. In view of the defects of traditional asymptotic observer slow, time is uncontrollable, sliding mode method exists chattering and message signal pollution output is difficult to construct observer, the present application establishes the chaotic drive system containing message signal embedding, constructs the equivalent system without unknown signal, designs finite time reduced dimension observer based on observer matching condition and strong observable condition. Compared with the traditional asymptotic convergence observer method, the present application can realize the complete synchronization of chaotic system within the user preset time, adopts continuous control law without chattering, and the message signal can be accurately recovered through algebraic formula. The synchronization time of the present application is controllable, the convergence speed is fast, the recovery precision is high, and the robustness is strong, which is suitable for various chaotic secure communication scenes, effectively improves the communication real-time, security and engineering practicability.

The present application relates to a method and system for electromagnetic tomography reconstruction based on graph convolutional neural network, the method comprising: carrying out grid division on the to-be-measured region of the target image, obtaining a plurality of grid units and constructing a topological graph; constructing an electromagnetic characteristic unit based on the nodes and edges in the topological graph, calculating the conductivity distribution and the corresponding current density distribution of the electromagnetic characteristic unit, and obtaining the node features in the topological graph; constructing the node features into a node feature matrix and inputting the node feature matrix into a pre-trained graph convolutional neural network model to output a feature map; counting the distribution of feature values in the feature map, combining the concentration degree and the change range of the electromagnetic characteristics in different regions, obtaining the mixing state of the multiphase flow in the stirring process, and completing the electromagnetic tomography reconstruction of the target image. The present application effectively mines the complex relationship of non-euclidean data in the to-be-measured region, and provides strong support for state monitoring, flow behavior analysis and process optimization in the mixing and stirring process of multiphase flow.

The invention discloses a hybrid five-level bidirectional active bridge containing an auxiliary branch and a control method thereof, and relates to the technical field of power electronics, and the hybrid five-level bidirectional active bridge comprises the steps: introducing an auxiliary bridge arm and a coupling auxiliary transformer into a primary side circuit, and enabling the primary side of a high-frequency transformer to generate five step levels by controlling the state of the bridge arm. According to the control, real-time voltage gain is calculated, a target switch time sequence mode is automatically selected according to the relation between the real-time voltage gain and a preset reference threshold value, and an optimal phase shift combination is calculated by calling a GOC optimization algorithm with the purpose of minimizing inductive current stress. According to the method, a plurality of voltage matching points are physically constructed through the coupling auxiliary transformer, and more control freedom degrees are provided for current stress optimization; and a GOC optimization equation is utilized to ensure that the device operates at a current stress minimum point under all working conditions. Through collaborative optimization of software and hardware, current stress of all working conditions is reduced, a ZVS region in a full load range is widened, and efficiency and stability of a wide voltage working range are improved.

The application discloses a table structure recognition method and device, a storage medium and an electronic device. The method comprises the following steps: obtaining a target picture containing a target table object; using a predetermined target key point prediction model to predict key points of the target picture by using a key point heat map prediction method, to obtain coordinate values of each key point and type information of each key point; using a predetermined target key point matching model to match each key point based on the coordinate values of each key point and the type information of each key point, to obtain a plurality of key point sets; and performing table reconstruction based on the coordinate values in each key point set, to obtain a recognition result of the target table object. In the application, the key point prediction model and the key point matching model are used, so that a plurality of key point sets corresponding to each cell can be accurately obtained, and the target table object can be accurately reconstructed based on the plurality of key point sets.

This invention discloses a digital prediction and reverse causal chain diagnosis method for hydrogen refueling station fires, belonging to the field of fire prediction and diagnosis technology. Based on the environmental parameters and leakage source parameters of the hydrogen refueling station, a multivariate coupled operating condition is designed to simulate a hydrogen refueling station fire and construct a database. Based on the Transformer model, a learnable location encoding mechanism is designed to dynamically fuse spatiotemporal coordinate features, training a fire development prediction model. By fusing temperature sensor data through a spatiotemporally coupled Transformer model, a diffusion generation architecture with residual constraints is constructed, training a fire temperature visualization prediction model. Based on the temperature sensor data and environmental parameters of the hydrogen refueling station, combined with the fire development prediction model and the fire temperature visualization prediction model, digital prediction of hydrogen refueling station fires is achieved. A reverse causal chain diagnosis method based on fault trees and multi-source data is established to achieve second-level root cause diagnosis. This invention is used for more accurate prediction of hydrogen refueling station fires and reverse causal chain diagnosis.

This reconstruction structure, based on a transmissive phase discontinuity surface, generates a circularly polarized beam, belonging to the field of beam reconstruction. Traditional optical rotation response and beam direction control techniques typically rely on bulky and complex devices to achieve these functions, which not only limits system integration but also reduces signal processing efficiency. The proposed structure comprises an array of n×n integrated layers, of which n types exist. The first, second, and third metal structural layers have elliptical annular cross-sectional shapes. The rotation angles of the first, second, and third metal structural layers on each type of integrated layer are not identical; these angles are the angles between the major axis of the elliptical annulus and the negative direction. The left-hand or right-hand circularly polarized wave phases of the n integrated layers differ by m° sequentially, and the arrangement range of the left-hand or right-hand circularly polarized wave phases of the n integrated layers is from 0 to 2π. This structure is used to generate a circularly polarized beam.

This invention discloses a method for dynamically detecting the morphology of a cavity based on interlayer fracture vibrations, belonging to the field of cavity construction and monitoring technology for salt cavern gas storage. The method involves drilling and trenching to locate the cavity center and mudstone interlayer, deploying monitoring points and burying distributed monitoring optical fibers in the outer region of the cavity wall to capture the vibration wave signals of the interlayer fracture, recording the time difference and fluctuation intensity differences; analyzing the signal data of the propagation path to establish a three-dimensional coordinate system; and establishing a system of equations based on the time difference between the primary fracture vibration and the secondary vibration from the rock impact to calculate the three-dimensional coordinates of the fracture point and characteristic points of the cavity wall; then reconstructing the real-time contour of the cavity based on the layered symmetrical fracture characteristics and signal energy characteristics, and verifying it with other signals from the fracture layer and correcting the accuracy of the monitoring equipment. This method does not require interruption of the cavity construction operation, has high spatiotemporal resolution, and can accurately invert the cavity morphology, providing a basis for dynamic adjustment of the flow velocity and direction in water-soluble cavity construction, effectively reducing the risk of cavity collapse and excessive lateral dissolution.

The invention provides an underwater surveying and mapping system and method based on a rotary scanning type three-dimensional imaging sonar. The surveying and mapping system comprises a water surface moving platform (unmanned ship), an underwater scanning unit (three-dimensional imaging sonar), a pose and coordinate measuring unit (RTK / IMU), a data processing and converting unit and a remote display and control unit. The surveying and mapping method specifically comprises the following steps: controlling the unmanned ship to move to the center of each sub-region; a sonar is started to plot the sub-regions to obtain underwater three-dimensional point clouds of the underwater terrain and the underwater creatures; based on the position information and the attitude information of the underwater surveying and mapping system, the coordinates of the underwater three-dimensional point cloud are converted into an east-north-sky rectangular coordinate system, and the final coordinates of the underwater three-dimensional point cloud are obtained; and on the basis of the underwater three-dimensional point clouds of the sub-regions, a complete underwater three-dimensional point cloud of the region to be measured is generated through fusion. The method is suitable for underwater topographic surveying and mapping and dynamic target monitoring of rivers, lakes and offshore waters, the measurement efficiency is high, data processing is convenient, and the method does not depend on platform tracks and measuring lines.

The invention discloses an internal demoulding polyurethane composite material as well as a preparation method and application thereof, and belongs to the technical field of polyurethane materials. The composite material is prepared by mixing and curing a first component (modified polyurethane containing side chain carboxyl and end group double bonds) and a second component (cyclic polyurethane). The first component is prepared through ring opening esterification of itaconic anhydride, introduced side chain carboxyl effectively reconstructs a hydrogen bond network to enhance the mechanical strength, and end group double bonds promote demolding components to efficiently migrate to a mold interface; the unique end-group-free topological structure of the second component effectively limits excessive slippage of a macromolecular chain segment, and the demolding component is permanently and stably anchored at an interface. The composite material effectively solves the problems that a traditional composite material is difficult to demould and the mechanical property is difficult to consider at the same time, realizes an excellent and lasting internal demoulding effect, greatly improves the tensile strength and toughness of the material, and is especially suitable for preparation of composite materials such as PHC cover plates of automobile trunks and the like.

The application is suitable for the field of geophysical technology, and provides a magnetotelluric multi-parameter inversion method for mineral exploration, comprising: constructing a synthetic training set integrating current-type distortion interference; constructing a joint inversion model; based on the synthetic training set, using a joint supervision training method of multi-parameter collaborative solution, and an error back propagation optimization method to train the joint inversion model, so that the joint inversion model synchronously outputs a resistivity model and a current-type distortion tensor. The inversion method can effectively suppress current-type distortion interference and accurately reconstruct the resistivity structure, has excellent robustness and generalization performance, can improve the resolution and reliability of MT deep electrical structure inversion imaging in complex areas, and provides key technical support for efficient and accurate exploration of deep mineral resources.

The invention provides a range radar device based on ultra-wideband resolvable chaotic synchronization, which relates to the technical field of radar range finding and comprises a first ultra-wideband resolvable chaotic signal generation circuit unit, a second digital excitation circuit unit, a third ultra-wideband resolvable chaotic signal synchronization circuit unit and a fourth wideband related receiving circuit unit. According to the invention, the problem of radar real-time ranging with high resolution and high measurement signal-to-noise ratio is solved, high-resolution real-time correlation ranging is realized through the generation circuit and the receiving circuit of the ultra-wideband analyzable chaotic signal, correlation receiving is completed only by simulating a matched filter, and high-sampling-rate data pressure and large correlation calculation amount are avoided. In addition, a receiving end can analyze synchronization of chaotic signals through driving of a digital excitation circuit, and chaotic echo signals are accurately reconstructed to improve the signal-to-noise ratio of distance measurement.

This invention discloses a method, apparatus, and system for tilt attitude calibration of a vertical hydrophone array based on the ray-mapping method. The method includes: sequentially performing a pre-search estimation of the receiving-transmitting distance of each hydrophone in order from shallow to deep depth, minimizing the error between the theoretically calculated arrival time difference between the direct wave and the reflected wave from the sea surface and the actual extracted time difference, thereby obtaining the optimal distance estimate for each hydrophone in the pre-search stage; recalculating the optimal direct wave arrival time for each hydrophone using the optimal distance estimate obtained from the pre-search; and again, sequentially fine-tuning and optimizing the receiving-transmitting distance of each hydrophone in order from shallow to deep depth, iteratively searching for the distance estimate that most closely matches the theoretical direct wave arrival time calculated based on the pre-search estimate with the actual measured value. This invention provides a marine vertical receiving array attitude calibration scheme that is independent of external assistance, highly accurate, robust, and computationally efficient.

The application discloses a kind of unmanned aerial vehicle bridge appearance inspection method and system based on multispectral, comprising: ground control point reflective sticker and bridge deck linear reflective sticker are set in bridge;Laser point cloud data is collected using basic route, and bridge three-dimensional model is constructed;Based on bridge three-dimensional model, plan fine route, select orthographic image from the bridge image shot, detect two kinds of reflective sticker marks, output reflective sticker center pixel coordinates and corresponding reflective sticker orthographic image;Read the metadata of reflective sticker orthographic image, obtain the three-dimensional coordinates and pixel coordinates of ground control point;The three-dimensional coordinates of reflective sticker center are obtained by solving;Bridge photo is detected using bridge surface disease identification model, and disease photo is screened, and disease boundary box center coordinates are used as disease position;The same disease in multiple photos is extracted, and the three-dimensional coordinates of disease boundary box center point are calculated;Based on disease three-dimensional coordinate set and the change condition of bridge deck reflective sticker position, plan key area reinspection route.