58results about How to "Fast rebuild" patented technology

The invention provides a method for reconstructing a three-dimensional dynamic scene. The method comprises the following steps: acquiring a multi-view synchronous video of the dynamic scene; calculating matching points between video images of different viewing angles, and estimating internal and external parameters of each camera; obtaining a Gaussian sputtering point set {p0} according to the sparse point cloud constructed according to the depth of each matching point; performing static training on the first-frame images of all videos by using {p0} to obtain a Gaussian sputtering point set {p}; and for other frames of images, dividing {p} into a static point set {S} and a dynamic point set {D}, performing dynamic training on {D}, forming a dynamic Gaussian sputtering point set # imgabs0 # by {p}, {S} and finally obtained {D}, and performing rendering on # imgabs1 # by combining internal and external parameters of a camera according to a rendering pipeline of Gaussian sputtering to obtain rendered images of a new view angle at different moments. According to the invention, the dynamic three-dimensional scene can be recovered from the multi-view synchronous video, training can be completed within minute-level time, and real-time rendering can be realized after training.

The invention belongs to the technical field of medical image three-dimensional reconstruction, and discloses an abdominal cavity reconstruction and focus positioning method based on a binocular endoscope, which comprises the following steps of: calibrating and correcting internal reference of the binocular endoscope; inputting the trained depth estimation network to obtain a disparity map and processing the disparity map into a picture according to frames; the luminosity error is minimized through a coarse-to-fine pyramid optimization method, and a transformation matrix of the endoscope pose between two continuous frames is calculated through nonlinear iterative optimization; light beam adjustment optimization is carried out on the motion trail of the endoscope, a depth map is superposed, outliers are removed, and three-dimensional reconstruction of the abdominal cavity tissue is achieved; recognizing a focus, extracting three-dimensional information according to the disparity map, and visually guiding the operation arm to move to a focus point in combination with a calibrated hand-eye matrix so as to realize positioning. According to the method, rapid and dense three-dimensional reconstruction of the peritoneal environment before the operation and accurate focus positioning based on visual guidance during the operation are realized, the accuracy of motion track estimation of the endoscope is improved, and autonomous development of the surgical robot is expected to be promoted.

The invention discloses anti-swelling hydrogel with high toughness and rapid self-recovery and application of the anti-swelling hydrogel as a strain sensor. The preparation method comprises the following steps: dissolving a polymerizable monomer containing carboxyl, an acrylate monomer containing phenyl or phenoxy, multivalent ion salt and a photoinitiator in dimethyl sulfoxide (DMSO) to prepare a uniform precursor solution; carrying out ultraviolet polymerization on the precursor solution in an inert atmosphere to obtain organic pregel; and soaking the organic pre-gel in a large amount of water, and removing the solvent in the gel through solvent exchange to obtain the final hydrogel. The hydrogel has excellent mechanical performance, sensing performance and anti-swelling performance, and is used as a strain sensor to transmit information based on Morse passwords in an underwater environment.

The invention provides a face model reconstruction method and device, equipment and a medium. The method comprises the following steps: acquiring multi-frame original point clouds of a user face; determining an original point cloud of a reference frame and an original point cloud of a non-reference frame in the multiple frames of original point clouds; according to the multiple frames of original point clouds, reconstructing a first face model matched with the expression state corresponding to the original point clouds of the reference frame; and reconstructing a second face model matched with the expression state corresponding to the original point cloud of each non-reference frame by aligning the first face model to the original point cloud of each non-reference frame. By using the method, the reconstruction efficiency of the dynamic face model can be improved.

The invention discloses a geometric error parameterization rapid modeling method for a multi-configuration five-axis machine tool. The method comprises the following steps: constructing a general transfer vector table with parameters of a five-axis machine tool of a target type; the parameter set of the five-axis machine tool is input into a general transfer vector table, the general transfer vector table generates transfer vectors of all geometric error items of the five-axis machine tool, and therefore a geometric error model is constructed; and inputting the geometric error item and the five-axis coordinate into the model, and outputting the position deviation and the attitude deviation of the cutter by the model so as to compensate the geometric error of the cutter of the five-axis machine tool. According to the method, the constructed transfer vector table can be reused, and the constructed geometric error model can be suitable for most of conventional configuration machine tools; according to the method, parameterized rapid modeling of geometric errors of the five-axis machine tool can be realized, experience requirements of research and operators can be reduced, and convenience is provided for precision design and error compensation research of various conventional five-axis machine tools and transverse comparison research between various machine tool configurations and layouts.

The invention provides a multi-view sheep point cloud data processing method and device. The multi-view sheep point cloud data processing method comprises the following steps: acquiring multi-view point cloud data of goats with different postures in a goat captivity channel; registering the multi-view point cloud data to obtain registration data, and filtering the registration data to obtain filtering data; segmenting the filtering data according to different parts of the goat to obtain a plurality of part point cloud clusters; and extracting corresponding part features from each part point cloud cluster, and performing point cloud completion on the plurality of part point cloud clusters according to the part features, the target search radius, the target sampling radius and the target sampling step length to obtain target point cloud data. According to the method provided by the invention, the goat missing point cloud can be quickly reconstructed with high precision through the distributed point cloud completion method, and the quality of goat point cloud data and the accuracy of point cloud reconstruction are improved.

The invention discloses a decentralized application-oriented smart contract program generation method, device and equipment and a storage medium, and belongs to the field of block chain smart contracts, and the method comprises the steps of persisting an executable code and state data of a fragmented smart contract in block data; in response to a calling instruction for the fragmented smart contract, extracting the executable code and the state data persisted in the block data to execute the fragmented smart contract, and updating the state data persisted in the block data according to execution of the fragmented smart contract; and in response to a reconstruction instruction for the fragment smart contract in execution, extracting persistent executable codes and state data from the block data, and reconstructing the fragment smart contract in execution through the extracted executable codes and state data. The problems of complex cross-fragment calling, low execution speed and difficult fault recovery of the smart contract are solved, and the performance and stability of the block chain system in decentralized application processing are improved.

The invention relates to a three-dimensional radiation field reconstruction method and device based on a deep neural network. Obtaining a data set of three-dimensional radiation field reconstruction according to the obtained feature signal and the target signal; determining neural network hyper-parameters of three-dimensional radiation field reconstruction according to the characteristic signal, the target signal, and shielding arrangement characteristics and geometric complexity in the radiation space; training a deep neural network according to the data set and the neural network hyper-parameters to obtain a three-dimensional radiation field reconstruction neural network model for three-dimensional radiation field reconstruction; performing a radiation field reconstruction test of three-dimensional radiation field reconstruction on the three-dimensional radiation field reconstruction neural network model by using a test sample; and after the radiation field reconstruction test is passed, reconstructing the three-dimensional radiation field according to the three-dimensional radiation field reconstruction neural network model, and reconstructing the three-dimensional radiation field reconstructed by the three-dimensional radiation field. According to the invention, the reconstruction of the three-dimensional radiation field can be rapidly carried out through the characteristic signal and the target signal; the three-dimensional radiation field reconstruction neural network model has stability and generalization ability.

The invention discloses a flow field data order reduction processing method and system for atmospheric pollution prediction, and belongs to the technical field of atmospheric pollution prediction. An existing air pollution data processing scheme needs to process a large amount of data, the calculation time is generally long, the memory consumption is high, and the analogue simulation cost is high. According to the flow field data order reduction processing method for atmospheric pollution prediction, large-scale original flow field data is decomposed into discrete blocks, namely different sub-matrixes, and each sub-matrix can be subjected to decomposition calculation, so that large-scale simulation data can be processed under limited hardware resources, and the calculation efficiency is improved. And the decomposition calculation speed is increased. And meanwhile, singular value solution is performed on each sub-matrix to obtain a flow characteristic primary function, so that original flow field data can be accurately described by using a small number of primary functions, the purpose of data order reduction is realized, the requirements on computing resources and equipment are further reduced, and the analogue simulation cost is reduced.

The invention discloses a road surface condition rapid detection method based on two-way stereoscopic vision, and relates to the field of rapid detection, and the method comprises the following modules: a three-dimensional model optimization and texture mapping module, which is used for carrying out smoothing processing and detail enhancement operation on a three-dimensional model by adopting a model optimization algorithm, and meanwhile, carrying out texture mapping on the three-dimensional model; according to the method, the three-dimensional model optimization and texture mapping module adopts a model optimization algorithm and a texture mapping technology, the visual effect and authenticity of the three-dimensional model are improved, the model is enabled to be more vivid by performing smoothing processing and detail enhancement operation on the model, and the image quality is improved. Meanwhile, the image information of the road surface is attached to the surface of the model through the texture mapping technology, the visual expressive force of the model is enhanced, the visual quality of the three-dimensional model is improved, and a user can know the actual condition of the road surface more visually.

The invention discloses a one-transmitting and multi-receiving wavefront trajectory common tangent point double-layer medium ultrasonic imaging method based on tool center coordinates and a related device, and belongs to the technical field of ultrasonic nondestructive testing. The method comprises the following steps: establishing a workpiece coordinate system by taking a certain point on a workpiece as a coordinate origin, clamping a linear phased array probe by using a multi-axis scanning system, and inverting the position and deflection angle of each array element relative to the coordinate origin on a scanning position according to a tool center coordinate returned by each scanning position; establishing a plurality of common tangent points between a wavefront track and the curved surface workpiece by utilizing multiple groups of one-transmitting and multi-receiving ultrasonic echo signals in the full-matrix data acquired at the scanning position, and further constructing a workpiece surface contour line through interpolation processing; a workpiece surface contour line measured by a wavefront trajectory common tangent point method, an array element position under a tool center coordinate and a one-transmitting-multiple-receiving mode signal are utilized, an ultrasonic image of a curved surface workpiece is constructed through a double-layer medium delay superposition algorithm, and ultrasonic imaging of the complex curved surface workpiece under a water immersion automatic phased array detection working condition is achieved.

The invention discloses a slag crust supplementing method and device, electronic equipment, a medium and a program product, the slag crust supplementing method is applied to a blast furnace, and the blast furnace comprises a furnace body, a cooling layer and a slag crust layer; the slag crust supplementing method comprises the following steps: acquiring temperature data in a blast furnace; determining a slag crust missing area of the cooling layer based on the temperature data; and the slag crust raw materials are supplemented to the slag crust missing area, so that a slag crust layer is formed in the slag crust missing area. According to the slag crust supplementing method, the temperature data in the blast furnace are obtained, the slag crust missing area of the cooling layer is determined based on the temperature data, the slag crust raw materials are actively supplemented to the slag crust missing area, the slag crust layer is formed in the slag crust missing area, and the mode that in the prior art, the slag crust layer is naturally formed passively depending on molten slag in the furnace is replaced; the protection layer can be rebuilt actively and quickly, so that the dry burning time of the cooling layer in an unprotected state is remarkably shortened, and the cooling layer is effectively prevented from being damaged due to overheating.

The application discloses a real-time leakage positioning monitoring system and method based on electrical resistance tomography, relates to the technical field of process tomography and industrial safety monitoring, and solves the technical problem that it is difficult to intelligently identify, three-dimensionally position and quantitatively evaluate leakage events; the system comprises a sensor array, a multi-channel data acquisition and excitation module, a real-time data processing and image reconstruction module, a leakage intelligent identification and positioning module, a man-machine interaction and data management module; the core lies in that a multi-layer nested annular array and a sparse measurement strategy are adopted to improve data acquisition efficiency, a physical information neural network is introduced to reconstruct an image, a measurement topological coding matrix and a physical residual correction module are used to reconstruct the image, and finally, a time-space dual-domain leakage probability field model is constructed, a virtual sensor network and an improved D-S evidence reasoning framework are combined to intelligently identify and position a leakage diffusion mode.

The invention relates to the technical field of scanning devices, in particular to a quick response X-ray scanning device based on a micro-electro-mechanical chip, which comprises a detection table, a photoelectric sensor is arranged in the detection table, a plurality of positioning blocks are arranged on the detection table, and a table top of the detection table is used for bearing an object to be imaged; the X-ray source is arranged above the detection table and can emit X-rays to the direction of the detection table, and the X-ray source is connected with a movable support capable of driving the X-ray source to move; the detector is connected with the detection table and can receive the X-rays penetrating through the object to be imaged and convert the X-rays into digital signals; the detection table fixes the position of an object to be imaged through the positioning block, the image acquisition module acquires an image of the detection table, and the control module adjusts the position of the X-ray source according to the position of the object to be imaged on the image so as to completely scan the object to be imaged. By accurately positioning the position of the object to be imaged and standardizing the image of the object to be imaged, the accuracy and reliability of the whole scanning process are improved, and the scanning efficiency and the imaging quality are improved.

The application discloses a kind of bio-organic fertilizer and preparation method thereof, belong to organic fertilizer technical field.The bio-organic fertilizer includes by weight parts: plant source organic matter matrix 62-75 parts, carbonized rice husk 10-20 parts, compound functional bacterial agent 0.5-3 parts, mineral conditioning component 0.5-2 parts.Plant source organic matter matrix is composed of rice bran, corn meal, soybean meal, soybean oil;Compound functional bacterial agent is compounded by indigenous functional bacterial flora and artificial intensive bacterial flora according to mass ratio 1:(1-2);Preparation whole process uses normal temperature powder process, avoids high temperature inactivation bacterial flora.The whole plant source formula of the application eliminates heavy metal and antibiotic pollution of livestock and poultry manure source, and the porous carrier of carbonized rice husk cooperates with normal temperature process to greatly improve the survival rate of bacterial flora, and the double bacterial system is adapted to local soil, and is colonized for a long time, which can realize multiple functions such as organic matter supplement, acid-base two-way adjustment, granular structure repair, soil-borne disease prevention and control.

The present disclosure provides a visual imaging and recognition device, which can be applied to the field of image processing technology. The device comprises: an imaging sensor for acquiring two-dimensional image information and three-dimensional depth information of a target object; a signal shaping and coding module for signal shaping and coding the two-dimensional image information and the three-dimensional depth information to obtain target two-dimensional image information and target three-dimensional depth information; a processing module for processing and recognizing the target two-dimensional image information and the target three-dimensional depth information to generate a recognition result; an output module for signal synchronous buffering and outputting the recognition result; and a programmable microcontroller for providing synchronous control signals to the imaging sensor, the signal shaping and coding module, the processing module and the output module to control the synchronous and coordinated operation of the device. The device can naturally imitate the working mechanism of the human brain visual system and has the advantages of high performance and low power consumption.

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 invention provides a thermal deformation optimization method for a carbon fiber parabolic reflector of a honeycomb sandwich structure. The thermal deformation optimization method comprises the following implementation steps: constructing a reflector model; determining a constraint interval of structural parameters of the reflector model; calculating the root-mean-square of thermal deformation of the working surface of the reflector model under the high-temperature and low-temperature working conditions; and constructing a reflector optimization model and solving the reflector optimization model based on an ant lion optimization algorithm. According to the parameterized model constructed by the method, the time for re-modeling in the optimization process is shortened, and the optimization efficiency is improved; the reflector optimization model is solved based on the ant lion optimization algorithm to obtain an optimal solution, the defect that in the prior art, the optimization precision of an optimization result obtained by changing a single structure parameter for multiple times of calculation and then taking heat deformation to achieve the minimum root mean square is low is overcome, and the optimization precision is improved; and determining the constraint interval of the structural parameters of the parameterized model of the reflector, thereby avoiding the defects of overlarge optimization variable range and low optimization precision in the prior art, and improving the optimization efficiency and precision.

The invention relates to a three-dimensional phenotype analysis robot and an analysis method applied to field wheat, relates to the technical field of agricultural auxiliary breeding, and realizes refined analysis of phenotype data of a single plant of wheat in a field environment, the three-dimensional phenotype analysis robot comprises a walking trolley and a three-dimensional phenotype analysis device, the walking trolley realizes walking and steering, and the three-dimensional phenotype analysis device is connected with the walking trolley. The three-dimensional phenotype analysis device can complete isolation of wheat plants, create a dark environment and reduce the influence of external environmental factors, and the data acquisition device rotates by one circle to obtain data and complete transmission; according to the method, sparse point clouds are reconstructed from collected pictures by using improved colmap, dense reconstruction is performed by using an improved mvs algorithm, a complete and smooth three-dimensional point cloud is obtained after background point clouds are removed and noise is reduced, then point clouds of wheat ears, stems, leaves and non-plants are automatically segmented, wheat plant phenotype parameters are extracted, and automatic analysis of wheat phenotypes is realized in the whole process. And the phenotype analysis precision level of the single-plant wheat in the field is improved.

The invention discloses an effective signal extraction method and system based on CVAE, and relates to the field of digital signal processing, and the method comprises the specific steps: constructing a conditional variation auto-encoder which comprises an encoder, a re-parameterization module and a decoder; constructing a training data set based on the observation data set and the corresponding conditional variable set; training the conditional variation auto-encoder by using the training data set, and performing parameter optimization on the conditional variation auto-encoder by using a loss function based on physical consistency constraint; the loss function based on the physical consistency constraint comprises a reconstruction error item, a KL divergence loss item and a physical constraint loss item; acquiring to-be-processed data and corresponding conditional variables; and inputting to-be-processed data and conditional variables into the trained conditional variation auto-encoder to obtain effective signals. According to the method, physical consistency constraint is introduced through the conditional variation auto-encoder, high-frequency noise and pseudo-frequency components are effectively weakened, and a reconstructed signal is closer to a real signal.

The application discloses a kind of based on neural shader's fast reconstruction textured three-dimensional model method, comprising the following steps: step S1, a group of images are photographed at different angles around the object to be reconstructed, and the foreground image, camera pose and internal parameter are obtained by pre-processing image as training data, step S2, establish initial triangular grid, step S3, establish differentiable renderer, it contains rasterization and neural shader two parts vertex and texture of the model of the application are divided into two optimizers, model is successively differentiable rasterization and differentiable neural shader, obtain rendering chart, calculate loss with original chart and back propagation, compared with the ray step of implicit function of other reconstruction methods, the time of rasterization is significantly shorter, since the application of neural shader, the model obtained can be used to paste texture with trained neural shader, so that fast reconstruction textured three-dimensional model is realized.

This invention discloses a calibration method for a mid-level echelle grating spectrometer. It establishes the geometric relationship between theoretical coordinates and pixel coordinates using five-point positioning. An effective region mask is formed through envelope boundary extraction, separating the spectral information regions from the invalid regions on the detector, enabling rapid reconstruction of irregular effective regions. The coordinate relationships obtained from ray tracing and mercury-argon lamp calibration are unified across the entire spectrum. Local inverse distance weighted fusion is performed on each integer coordinate point within the mask to form an integer coordinate-wavelength lookup table, discretizing the continuous wavelength field into a directly queryable database, thus establishing a reusable pixel-level wavelength mapping layer. Gaussian smoothing, top-hat enhancement, and adaptive threshold segmentation are applied to the original target image, and bright stripe pixels are extracted. Finally, these pixels are directly mapped to wavelengths to reconstruct the continuous spectral curve. This invention can quickly recover the entire wavelength relationship, achieving a practical, reusable, and rapid recalibration capability.

The invention relates to a three-dimensional phenotypic analysis robot and an analysis method for field wheat, and relates to the technical field of agricultural auxiliary breeding. The robot realizes the refined analysis of single-plant wheat phenotypic data in a field environment. The robot comprises a walking trolley and a three-dimensional phenotypic analysis device. The walking trolley realizes walking and turning. The three-dimensional phenotypic analysis device can complete the isolation of wheat plants, create a dark environment, and reduce the influence of external environmental factors. The data acquisition device rotates one circle to acquire data and complete the transmission. The collected photos are reconstructed into a sparse point cloud by using an improved colmap, and then densely reconstructed by using an improved mvs algorithm. After removing the background point cloud and reducing noise, a complete and smooth three-dimensional point cloud is obtained. Then, wheat ears, stems, leaves and non-plant point clouds are automatically segmented and wheat plant phenotypic parameters are extracted. The whole process realizes the automatic analysis of wheat phenotypes and improves the accuracy level of single-plant wheat phenotypic analysis in the field.

The application discloses a kind of general detachable sensor's quick electrical capacitance tomography device and imaging method, its rigid support is the combination of two support main bodies detachable;Assembled support can be around the outside of the area needing imaging;Rigid support is set for through the multiple through holes of scale rod;Through hole is all with the center of rigid support as center and extends outward;Scale rod is hollow insulating round rod of length scale being set on surface, and its end head in the inside of rigid support is connected electrode by rotatable connecting device;Electrode is flexible electrode sheet;Shielding signal line of electrode sheet is connected to the capacitance data acquisition instrument circuit of outer end by connecting device, rod cavity of scale rod in turn;When imaging, electrode outer surface is adhered to the surface of the area needing imaging to form the outline of imaging area.The application can realize the measurement of different sizes and different shapes imaging area, and widely applicable, easy to disassemble and assemble, improve measurement accuracy without damaging the object.

The present application provides a kind of microscopic image distortion correction and reconstruction method, system, equipment and storage medium, preset multi-shape calibration slide, including: regular grid slide, concentric circle slide, irregular curve slide and multi-layer depth slide;The image of multi-shape calibration slide under different field of view, different magnification and different focal plane is collected by microscope, and the geometric distortion and blur characteristics of image are quantified by combining edge detection, contour analysis and blur measurement method, and distortion feature vector is generated;Point spread function model is constructed based on distortion feature vector, and point spread function model is optimized using convolutional neural network;The optimized point spread function model is applied to the deconvolution processing and geometric correction of microscopic image, and finally the clear image after reconstruction is obtained.The present application can effectively correct geometric distortion and edge blur, restore the details and clarity of microscopic image.It has wide application prospect in scientific research, clinical diagnosis and industrial detection field.

The application provides a sea wave significant wave height super-resolution reconstruction method and device and electronic equipment. An initial sea wave significant wave height field reconstruction model is trained by using a real low-resolution multivariate sea wave field sequence of continuous T time points before a plurality of time points τ and a real high-resolution sea wave significant wave height field to construct a training data set. The sea wave significant wave height field reconstruction model learns the propagation law of waves along the wave direction according to the significant wave height, average wave period, and sine and cosine components of the wave direction in the sea wave field sequence of continuous T time points, and extracts the physical structure of multiple scales in the wave field sequence. The sea wave significant wave height field is reconstructed according to the learned propagation law and the extracted physical structure under different scales. Compared with the traditional reconstruction method which cannot accurately reflect the wave evolution law and cannot accurately reflect the detailed information of the sea wave field, the resolution of the sea wave significant wave height field constructed by the application can be significantly improved.

The embodiment of the invention discloses a human body reconstruction method and a related device, and the method comprises the steps: inputting the sparse image data of a to-be-detected human body into a target neural network model, and obtaining a three-dimensional human body image which is determined by a target parameter for reconstructing the to-be-detected human body; performing micro-rendering on the three-dimensional human body image to render the three-dimensional human body image into a two-dimensional human body image; and performing joint optimization on the two-dimensional human body image and a sparse image corresponding to the sparse image data to obtain a reconstructed human body model. By adopting the embodiment of the invention, rapid reconstruction and rendering in the electronic equipment can be realized.

The invention discloses a meta universe scene construction and information perception platform based on a human body finite element model. The meta universe scene construction and information perception platform comprises a personalized modeling module, a multi-physics scene construction module and a multi-source data transmission module, the personalized modeling module is used for generating human body finite element models of different signs and comprises a basic model unit, an image acquisition unit, a grid deformation unit and a geometry generation unit; the multi-physics field scene construction module is used for constructing a complex universe scene and comprises a temperature physics field unit, a contact force physics field unit and a cloud computing unit; the multi-source data transmission module is used for transmitting multi-source data obtained through simulation in a multi-physical field scene to a human body and comprises a wearable somatosensory device and a simulation data decoupling unit. According to the invention, the method can reflect the practical multi-element perception conditions of contact force, temperature and the like to a user in the space of the element universe, and achieves the more natural and smooth experience of the element universe.

The present invention relates to the field of computer vision technology, and discloses a human body mesh reconstruction method based on posture transformation, which transforms the posture from an initial posture to a target posture through a neural network. The present invention solves the problems of low accuracy in the prior art.

The invention discloses a method and a device for reconstructing and editing an automatic driving environment based on 3dgs. The method comprises the following steps: acquiring sensor data and perception data of the automatic driving environment; the sensor data and the sensing data are preprocessed; performing reconstruction training of the automatic driving environment based on the preprocessed sensor data and perception data, and performing optimization based on a training result; and based on the trained reconstruction scene, editing objects in the scene to obtain a final automatic driving simulation environment. Based on the sensor data and the sensing data of the automatic driving environment, a real scene can be quickly reconstructed, a highly vivid picture can be rendered, and the problem that an existing simulation method of the automatic driving environment is low in modeling precision is solved.