469 results about "Reconstruction algorithm" patented technology

The invention discloses a cross-platform virtual-real fusion scene construction method and system based on AI space calculation, and relates to the technical field of artificial intelligence and space calculation, and the method comprises the steps: carrying out the multi-scale feature fusion based on a received cross-modal conversion instruction set, and generating an initial image sequence; carrying out implicit field coding on a target object by combining a three-dimensional reconstruction algorithm to obtain an initial parameterized model; performing space-time alignment on the multi-view video stream, loading a digital scene asset package in combination with physical sensing data and a preset spatial index structure, and establishing a bidirectional data channel between a virtual scene and a physical sensor; performing rendering and illumination parameter adjustment on the initial parameterized model to obtain an optimized parameter model; performing differential coding processing on the optimization parameter model to obtain a target virtual-real scene fusion model; and distributing the target virtual-real scene fusion model to a preset terminal. The invention provides a virtual-real fusion construction method for end-to-end collaborative optimization, which is suitable for cross-platform live broadcast or dynamic interaction scenes.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention discloses a personnel information security screening method and system based on big data, and relates to the technical field of image processing, and the method comprises the steps: obtaining to-be-processed face data in a region, screening out unregistered personnel, carrying out the matching and correlation of the images of the same unregistered personnel at different image collection points through calculating the feature similarity, and carrying out the recognition of the unregistered personnel. And connecting time points and spatial positions corresponding to the images of the same unregistered person at different image acquisition points into a spatial-temporal trajectory through a trajectory reconstruction algorithm, and pushing multi-modal feature information of the person to an adjacent preset area for the identified abnormal behavior. According to the invention, through a density clustering algorithm based on time and space constraints, feature association is carried out on personnel images collected by different cameras, a cross-region behavior track of the unregistered personnel is constructed, high-risk behaviors such as wandering are detected by using track analysis and an abnormal behavior identification technology, and early warning information is generated and sent to other regions. And the initiative and the coverage range of safety management are improved.

The invention discloses a lightweight high-speed photographing system and method based on an event camera, and the system comprises a hybrid imaging module which is used for synchronously collecting the image data and event data of a scene, and comprises a visible light camera and an event camera; the hardware synchronization module is electrically connected with the visible light camera and the event camera, and is used for receiving a frame synchronization signal of the visible light camera and sending a trigger signal to the event camera, so that the event camera inserts a time stamp corresponding to a visible light image frame exposure moment in an event stream; the data processing and fusion unit is in communication connection with the hybrid imaging module and is used for receiving the image data and the event data and executing the following steps: performing time alignment on an event stream and a visible light image frame stream based on a time stamp; operating a high-frequency frame insertion reconstruction algorithm, and reconstructing a multi-frame intermediate image by using event stream data between two frames; synthesizing the original visible light image frame and the reconstructed intermediate image into a high-frame-rate video stream; and the power supply and interface module is used for supplying power to the system and outputting a high-frame-rate video stream.

The invention discloses a visual detection test method for damage cracks of a solidified soil test piece, and particularly relates to the technical field of geotechnical engineering material detection. The method comprises the following steps: doping fluorescence labeling particles in a solidified soil test piece, and applying periodic load to induce crack formation after curing and forming; synchronously exciting fluorescent particles through an ultraviolet excitation light source, and collecting a short-wave fluorescence response image sequence; inputting the image sequence into a deep residual neural network model, identifying a nonlinear enhancement region of a fluorescence signal, and deducing a crack initiation position and a propagation path; constructing a three-dimensional dynamic model of crack evolution in combination with an image space reconstruction algorithm and time sequence comparison, extracting indexes such as a crack propagation rate, a bending angle and crack density, performing comparative analysis on the indexes and initial parameters of a material, evaluating a microstructure damage development mechanism, and outputting a visual damage evolution result; the method provided by the invention can realize high-precision, non-destructive and dynamic detection of the fine cracks in the solidified soil, and has the advantages of high identification sensitivity, strong modeling precision and good engineering adaptability.

The invention relates to a multi-modal medical image analysis method and device for cancer diagnosis. The method comprises the following steps: converting a two-dimensional projection sequence of an intraoperative gamma camera into an initial three-dimensional radioactive distribution voxel field through Radon inverse transformation; performing spatial registration by combining a tumor segmentation mask in the preoperative image to obtain a registered image; positioning a hole defect area based on the registration image and the initial three-dimensional radioactive distribution voxel field; generating a multi-modal constraint matrix and a coherence parameter optimization value through anatomical weighted continuous coherence modeling, and then correcting an initial distribution voxel field; fusing the corrected voxel field and the registered image to generate an initial boundary map and confidence; and judging whether to adjust the coherence parameter and regenerate the boundary diagram according to the confidence coefficient. By adopting the method, the problem of projection data missing caused by limitation of the rotation angle of the gamma camera in the operation can be effectively solved, topological structure distortion of a traditional reconstruction algorithm is avoided, and the accuracy and reliability of the cancer infiltration boundary diagram are improved.

The invention relates to the technical field of underwater topographic surveying and mapping, in particular to a sea area water taking channel scanning survey method and system based on multi-beam measurement, and the method comprises the following steps: constructing a rigid support, fixing a depth finder through a three-way cable fastening device, and building a Boolean parameter model; sound velocity, tide level and original echo data are collected; performing space-time registration, coordinate conversion and filtering denoising on the data to generate corrected water depth; and constructing an irregular triangulation network by using hard boundary constraint, generating a digital elevation model, and extracting a siltation area. According to the invention, multi-beam wide sector scanning and multi-sensor attitude correction technologies are fused, a limited water area full-coverage measurement system is constructed, and the problems of terrain blind areas and detail loss are solved. A boundary constraint reconstruction algorithm is utilized to realize closed-loop processing from coordinate reference unification to micro-landform restoration, the accuracy of channel bottom pit capture and edge topography expression is improved, and data support is provided for sedimentation analysis.

This invention provides a method and system for monitoring surrounding rock deformation in underground coal mines based on three-dimensional point clouds, relating to the field of coal mine safety monitoring. The invention includes the following steps: installing a reference target in the monitoring area and collecting point cloud data; preprocessing the point cloud data, performing coarse registration using the reference target and fine registration using the iterative nearest point algorithm, and generating a triangular mesh model using the Poisson surface reconstruction algorithm; parameterizing the triangular mesh model of the monitoring area into a two-dimensional analysis mesh, calculating the three-dimensional displacement vector of each node, and establishing a displacement field matrix; calculating the displacement gradient tensor based on the displacement vector, deriving the Green-Lagrange strain tensor, and obtaining the full-field strain distribution; recording the strain value of each mesh node, and determining whether an instability warning is triggered based on the relationship between the strain value and the warning threshold; this method can achieve high-precision monitoring of surrounding rock deformation in underground coal mines.

The invention provides a coastal erosion rate prediction method, and belongs to the technical field of coastal erosion, and the method comprises the steps: building a three-dimensional seabed grid model, solving a hydrodynamic field through depth average simplification and GPU parallel calculation, and calculating sediment flux distribution based on shear stress discrimination and a high-order windward format. Outputting an erosion mode category and a local erosion strength coefficient by using a coastline erosion feature recognition model comprising a Josephh ring screening layer and a self-organizing mapping projection layer, starting adaptive grid encryption when the local erosion strength exceeds a threshold value, and calling a corresponding parameter set according to an erosion mode to calculate a seabed elevation change and a coastline erosion rate; and a wave energy spectrum reconstruction algorithm is adopted to generate future wave sequence cycle prediction, so that the technical problem that calculation precision and calculation efficiency are difficult to consider in coast erosion rate prediction under a complex wave power condition is solved.

The invention discloses a flow field adaptive calculation method and system based on an acoustic Doppler flow meter. Comprising the following steps: acquiring original frequency spectrum data and synchronous attitude, position and water level information of a Doppler current meter; performing preprocessing and quality marking on the original data; multi-dimensional features are extracted, and a current dominant flow field type is identified through a pre-trained flow field classification model; adaptively selecting a flow velocity profile reconstruction algorithm according to the flow field type, and dynamically adjusting key algorithm parameters according to flow field stability or turbulence intensity parameters in the reconstruction process; a calculation grid is dynamically generated based on a flow velocity field of a reconstruction strategy to perform flow integral calculation, and data, a model and an integral error are comprehensively evaluated to quantify uncertainty of an output flow result; and finally performing flow field abnormity diagnosis, short-term flow trend prediction and equipment health state early warning. According to the method, the problem of insufficient precision of a traditional fixed algorithm in a complex flow field is solved, and the reliability and the intelligent level of flow monitoring are remarkably improved.

The invention relates to the technical field of wireless communication and signal processing, in particular to a short-wave spectrum efficient sampling method and system based on compressed sensing, and the method comprises the steps: collecting full-band power spectrum density data through a low-speed scanning receiver; determining a strong interference frequency point set; generating a binary null observation sequence; loading the binary null observation sequence to an analog front-end frequency mixing circuit, performing analog domain orthogonal frequency mixing and integral processing on the input radio frequency signal, and outputting a voltage sampling value; resolving to obtain a signal sparse coefficient through a compressed sensing reconstruction algorithm; calculating a reconstruction residual error according to the voltage sampling value and the signal sparse coefficient; in response to the condition that the reconstruction residual error is greater than a preset error threshold, triggering a reacquisition process of the power spectral density data; outputting a signal sparse coefficient in response to the condition that the reconstruction residual error is smaller than or equal to a preset error threshold; according to the invention, the problems of limited dynamic range and weak signal submerging are solved, and the receiving sensitivity of weak signals in a strong interference environment is obviously improved.

The invention relates to the technical field of optical fiber communication device detection, and provides an optical fiber connector end face 3D shape detection method and system, and the method comprises the steps: obtaining a multi-step phase shift interference image sequence of an optical fiber connector end face; performing interface segmentation on the multi-step phase shift interference image sequence by adopting a fiber core boundary detection network based on radial gradient perception to obtain interface position data; based on the interface position data and the multi-step phase shift interference image sequence, performing phase unpacking by adopting a dual-coordinate system fusion phase unpacking algorithm to obtain initial phase data; performing global reconstruction on the initial phase data by adopting a regional confidence coefficient weighted reconstruction algorithm to obtain global phase field data; performing spherical parameter fitting on the global phase field data by adopting an IEC (International Electrotechnical Commission) standard constraint hierarchical particle swarm optimization algorithm to obtain spherical geometric parameters; and generating a 3D shape detection result of the end face of the optical fiber connector according to the spherical geometric parameters. According to the invention, the precision and reliability of 3D shape detection of the end face of the optical fiber connector are improved.

A numerical control cutting process digital twinning implementation method based on three-dimensional reconstruction comprises the steps that a digital twinning model and a physical logic twinning model of a numerical control machine tool and a geometric model of a workpiece and a cutter are constructed respectively, and real-time synchronization of a physical machine tool model and a digital twinning machine tool model is achieved by transmitting motion data and machine tool state data in real time; the cutting state of a workpiece and a cutter is calculated in real time through a cutting simulation algorithm, collision detection simulation is carried out, a workpiece model constructed based on a one-way line cluster model is converted into a standard STL triangular mesh model based on a three-dimensional reconstruction algorithm, and the triangular mesh model is mapped into a digital twinning monitoring scene through coordinate system conversion. According to the method, a high-precision digital twinborn model is constructed, real-time data synchronization and cutting process simulation are combined, and rapid conversion between a simulation data model and a display model is realized based on a composite block algorithm.

The invention discloses a mutual inductor error self-compensation method based on dynamic flux linkage reconstruction, and relates to the technical field of mutual inductor error self-compensation. According to the method, a weighted double-domain integral noise suppression flux linkage reconstruction algorithm is used for processing, the fast response of time domain integral and the anti-noise capability of frequency domain integral are fused, and then a dynamic error extension state observer is constructed by taking a flux linkage estimation value as input; and estimating system errors caused by nonlinearity, hysteresis and environmental disturbance of the iron core and dynamic characteristics of the system errors based on extended Kalman filtering recursion. And finally, generating a time-varying compensation amount according to the estimated error and the derivative thereof. According to the method, the contradiction between precision and response speed in traditional flux linkage reconstruction is overcome, the problem that a static error model cannot adapt to dynamic working conditions is solved, high-precision real-time self-compensation of nonlinear time-varying errors of the mutual inductor is realized, and the measurement accuracy and dynamic performance of electric energy metering and protection equipment under complex operation conditions are improved.

The invention relates to the technical field of marine cable monitoring, in particular to a marine cable life prediction and health management method and system based on multi-source data fusion, and the method comprises the steps: constructing an electrochemical impedance spectroscopy and optical fiber humidity sensor array, and synchronously collecting multi-source signals in a coupling environment; performing dynamic compensation on the signal by combining a marine environment and working condition parameters, and eliminating interference to generate a state feature set; constructing a space corrosion-aging distribution diagram by using an electro-chemical coupling field chromatography reconstruction algorithm; real defects are determined through space-time adaptive segmentation and defect dynamics screening; and finally, calculating the corrosion depth and the aging rate by adopting a hyperbolic tangent-power law attenuation combined model to realize risk assessment. According to the invention, the accuracy of defect identification and life prediction of the marine cable in a complex environment is improved.

The invention relates to the technical field of semiconductor packaging detection, in particular to a semiconductor packaging micro welding spot three-dimensional shape intelligent detection system which comprises a sample placing frame, an imaging module, an image processing unit, a detection module and a result display module. The image processing unit constructs a unified curvature field expression by fusing three-dimensional information of three different imaging principles of a spherical mirror model, a light-sectioning microscopic three-dimensional reconstruction algorithm and stripe structured light, and adopts adaptive mesh refinement to process a curvature change violent region; the detection module identifies welding spot defects such as pseudo soldering, bridging and poor connection based on differential invariants and singularity analysis, the system realizes high-precision detection of various semiconductor packaging forms such as BGA, QFN packaging and wafer-level packaging, the measurement precision is improved by 35%-40%, the processing efficiency is improved by about 50%, the defect omission ratio is reduced from 15% to below 3%, and the detection accuracy is greatly improved. And a brand new solution is provided for semiconductor packaging quality control.

The invention discloses a crack strong-robustness time-frequency compressed sensing monitoring method, system, medium and equipment based on inter-blade tip timing, and the method comprises the steps: determining the installation number and installation positions of blade tip timing sensors, and carrying out the circumferential installation of the blade tip timing sensors on a rotor blade case; the blade tip timing sensors are used for obtaining a time sequence when the rotor blades reach the positions of the blade tip timing sensors; based on each time sequence and the rotor blade included angle, obtaining an inter-blade tip coupling vibration speed measured by each blade tip timing sensor; an undersampling vibration speed sparse representation model is constructed based on the inter-blade tip coupling vibration speed, dynamic frequencies of adjacent rotor blades are decoupled and extracted by using a sparse reconstruction algorithm, and non-baseline monitoring is performed on a crack propagation process according to the evolution of a vibration frequency difference between the blades.

The invention provides an interactive three-dimensional holographic display method for a smart farm, and relates to the technical field of smart agriculture and holographic visualization. Comprising the following steps: firstly, performing multi-angle aerial photography in different growth stages of a rice field through an unmanned aerial vehicle to collect image data; processing the data by using a three-dimensional reconstruction algorithm, and constructing a three-dimensional model library of the rice field; on the basis, a voice interaction module is integrated to a visual platform, so that a user can select and switch models and inquire growth information through voice control; further converting the selected three-dimensional model data into structured data suitable for holographic reconstruction, and generating a high-fidelity digital hologram by applying a complex-valued convolutional neural network point cloud gridding algorithm; and finally, performing optical reconstruction on the hologram by driving holographic display equipment. According to the method, the limitation of a traditional two-dimensional image in space and time sequence expression is overcome, dynamic visualization of the rice field growth process is achieved, and the monitoring and decision-making effects are improved.

A multiphase flow dispersed phase identification and completion method includes preprocessing an image to filter out noise and a non-key frequency component; identifying and segmenting the preprocessed image by using a SAM neural network to obtain a segmentation mask; post-processing the segmentation mask to output a precise bubble mask; and reconstructing the shape of a bubble by using a bubble reconstruction algorithm.

The invention discloses an environmental parameter field fine reconstruction method based on preliminary estimation and deep neural network correction, and belongs to the technical field of environmental parameter field reconstruction and deep learning application. The method comprises the following steps: firstly, based on sparse measuring point data, generating a rough field through a field reconstruction algorithm; then, taking the rough field as a main feature, and carrying out multi-channel fusion on the main feature and a space coordinate feature to form an input feature map; and then, inputting the fused feature map into a deep correction neural network, correcting the rough field by using the strong nonlinear mapping capability of the network, and outputting a high-precision and high-resolution fine field. In the model training stage, a composite loss function including data loss and physical constraint loss is adopted for optimization, and key parameters of a field reconstruction algorithm can be further taken as trainable variables to realize joint optimization with neural network parameters. The method effectively solves the problems that a traditional interpolation method is low in precision and weak in detail capturing capability under the sparse data condition, and a pure data driving model lacks physical reasonability, and remarkably improves the accuracy, robustness and generalization capability of environment parameter field reconstruction.

The invention discloses a temperature-pressure-resistant vibration methane carbon isotope detection system for oil and gas logging, which belongs to the technical field of oil and gas logging and comprises a gas sample pretreatment module, an optical sensing module, a driving and temperature control module and a core control and algorithm module. The core control module is realized by an FPGA (Field Programmable Gate Array) and is integrated with a self-adaptive multi-resolution scanning and compressed sensing reconstruction algorithm. The method comprises the following steps: performing current segmented scanning and wavelength modulation on the laser; light path anti-vibration coupling is realized through closed-loop control of the MEMS galvanometer; acquiring sparse sampling spectrum data; reconstructing a high-resolution spectrum by using compressed sensing; and inversing CH4 and CH4 concentrations by adopting Voigt linear fitting, and calculating a delta C value. The precision, the speed and the long-term stability of methane carbon isotope detection in a logging site temperature-pressure vibration coupling disturbance environment are remarkably improved.

The application discloses an infrared long-distance space adjacent target super-resolution method, wherein the method comprises the following steps: S1, analyzing the imaging characteristics of a long-distance target optical detection system and the imaging characteristics of space adjacent multiple targets, modeling point target imaging, and generating a simulation image dataset; S2, initializing the input simulation image based on linear mapping; S3, realizing infrared space adjacent target super-resolution modeling by adopting a sparse reconstruction algorithm, and constructing a deep unfolding network framework; S4, sequentially passing the initialized image through each stage of the deep unfolding network; S5, constructing a GPU training environment, setting a data loader, a model and an evaluator configuration file, training the model, applying the trained model to a test set, generating a high-resolution image, extracting target coordinate information by post-processing and inputting the target coordinate information into the evaluator, obtaining evaluation indexes based on infrared long-distance space adjacent targets, and calculating an average detection rate.

The invention relates to the technical field of image processing, and discloses a three-dimensional reconstruction method and system based on BARF and ZoeDepth, and the method comprises the following steps: S1, collecting a multi-view RGB image of a target scene; s2, taking each acquired RGB image as an input image, performing single image depth estimation through a pre-trained ZoeDepth model, and generating a corresponding depth map; s3, taking the generated depth map as geometric priori, and integrating the depth map into a BARF framework to obtain an optimized neural radiation field model; and S4, dense point clouds are extracted from the optimized neural radiation field, and a grid model is generated through a surface reconstruction algorithm. According to the method, under the condition that the number of input images is seriously limited, strong geometric constraints provided by depth information are fully utilized, a high-precision and high-integrity three-dimensional model is quickly and stably reconstructed, and the method has good engineering practical value.

The invention belongs to the technical field of positioning and navigation, and discloses a precise single-point positioning method and device based on a single Beidou, and the method comprises the steps: building a three-frequency non-difference and non-combination precise single-point positioning observation model based on a Beidou satellite system, estimating the delay of an ionized layer through a non-difference and non-combination method, and extracting the electron content in a line-of-sight direction; carrying out iterative solution on the electron content in the sight line direction by using a multiplication algebraic reconstruction algorithm to obtain the electron density of the ionized layer; taking ionosphere electron density as prior information, and adding ionosphere constraint estimation to obtain a state vector of a receiver; and calculating a signal quality evaluation coefficient of the Beidou satellite system, and according to the signal quality evaluation coefficient, updating the state vector of the receiver by using a hybrid filter to obtain a final positioning position. According to the invention, high-precision regional ionosphere delay modeling and Beidou three-frequency non-difference and non-combination precision single-point positioning under ionosphere constraint are realized, and the positioning precision and robustness of a single Beidou precision single-point positioning system are effectively improved.

Various methods and systems are provided for automatic quality control of image reconstruction. In one example, a method comprises obtaining medical image data, reconstructing the medical image data with a baseline reconstruction algorithm to generate one or more baseline reconstruction images and an enhanced reconstruction algorithm to generate one or more enhanced reconstruction images, detecting and localizing a set of features of interest within the one or more baseline reconstruction images, determining image values for each of the features of interest, comparing image values of the one or more baseline reconstruction images to corresponding image values of the one or more enhanced reconstruction images to determine one or more statistical characteristics, comparing the one or more statistical characteristics to predetermined criteria to determine deviations, and automatically modifying one or more parameters of the enhanced reconstruction algorithm based on the deviations.

The invention discloses an ultra-sparse CBCT (cone beam computed tomography) reconstruction method, system and equipment based on an ordinary differential equation, belongs to CBCT reconstruction in the field of artificial intelligence, and aims to solve the technical problem of low quality of CBCT reconstructed images. The method comprises the following steps: acquiring sample data, preprocessing the data, constructing and training a CBCT-CT nonlinear relation reconstruction model, and performing real-time reconstruction; during preprocessing, converting the three-dimensional image volume data into simulated X-ray projection data, and reconstructing the simulated X-ray projection data by adopting an FDK reconstruction algorithm to obtain an FDK-CBCT image; the CBCT-CT nonlinear relation reconstruction model comprises an encoder, a NODE module and a decoder; in the training process, the CBCT-CT nonlinear relation reconstruction model is trained through the obtained CT sample image and the FDK-CBCT image. In the reconstruction model, through continuous evolution of NODE modeling image features, the model can model a continuous evolution mapping process from a sparse low-quality image to a high-quality CT image during training, so that stripe artifacts and structural distortion do not easily exist in the reconstructed image, and the reconstruction quality is high.

A self-supervised multimodal structured illumination microscopic reconstruction method comprises: exciting a biological sample with structured illumination to obtain J raw fluorescence image sequences, wherein J is an integer greater than or equal to 1, each raw fluorescence image sequence comprises S fluorescence images, S being an integer greater than or equal to 2; generating a training set for each raw fluorescence image sequence among the J raw fluorescence image sequences; training a denoising neural network on the basis of the training set; and performing super-resolution reconstruction on the S fluorescence images in each of the J raw fluorescence image sequences using a standard structured illumination super-resolution reconstruction algorithm to form a super-resolution image, which is input into the denoising neural network to obtain a final super-resolution reconstructed image.

The invention relates to the technical field of underground detection, and discloses a low-frequency electromagnetic field detection positioning method for an underground metal target. According to the invention, a five-frequency-band time division multiplexing detection system is adopted in the frequency band of 1-100 kHz, and a special antenna is matched, so that deep target coverage within 40 meters can be realized, and shallow accurate measurement requirements can be ensured; meanwhile, distributed reference beacon nodes are deployed, parameters such as medium conductivity, dielectric constant and humidity are collected in real time, a wave velocity prediction model is constructed based on support vector regression to dynamically correct the electromagnetic wave propagation velocity, and correction errors are controlled; a time-frequency domain joint sparse reconstruction algorithm is used for separating a close-range target aliasing signal, so that an adjacent metal target can be effectively identified; finally, multi-band emission, signal receiving, medium sensing and a central processing unit are integrated to form a closed-loop cooperative system, an underground target three-dimensional model is generated, position coordinates and size and metal type attribute information are output, the system is suitable for complex scenes such as high-humidity soil and gravel layers, and the comprehensive detection performance is remarkably improved.

The present application provides a kind of marine aquaculture risk prediction method based on marine multi-source observation data, belong to marine aquaculture risk technical field, the present application is reconstructed three-dimensional marine environment field by sparse bayesian field reconstruction algorithm based on the constraint of ocean dynamics, then the risk benchmark distribution of long historical observation aquaculture area is migrated along the manifold coordinate to new aquaculture area by marine risk field manifold alignment migration algorithm, and the physical correction is carried out to bottom shape data and tidal harmonic constant, the risk index uncertainty interval is obtained by non-stationary generalized extreme value distribution combined with bayesian markov chain monte carlo sampling, drive physical constraint artificial intelligence to complete local high-resolution field reconstruction, according to the comparison result of risk index and early warning threshold, corresponding early warning level is triggered, finally form marine aquaculture insurance risk index product, solve the technical problem that new aquaculture area cannot establish reliable risk benchmark distribution due to lack of long-term historical observation data.

The application relates to an online monitoring device and method for microelectrodes, and belongs to the technical field of special machining.The online monitoring device is composed of a coaxial holographic imaging module, an LED backlight imaging module, a motion control module, an image acquisition card, a laser power controller and an LED brightness controller.The LED backlight imaging module is used for positioning the microelectrode image; the coaxial holographic imaging module is used for holographic interference imaging of the microelectrode; based on a holographic reconstruction algorithm, electrode profile information is obtained, and the electrode loss degree is judged.The coaxial holographic imaging module and the LED backlight imaging module switch the positions of the reflecting mirrors through the motion control module to realize control of different imaging modes; the collected images are input into a PC through the image acquisition card, image algorithm analysis is carried out, and the microelectrode loss degree is judged.The application realizes online monitoring of the microelectrode loss.