15results about How to "Fast rebuild" patented technology

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 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 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 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 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 application discloses a kind of based on big language model and agent collaborative face hearing nerve double-constrained ROI automatic fiber tracking method, the method comprises the following steps: step 1, the intelligent cleaning and standardization of multimodal medical image data;Step 2, double-constrained ROI generation based on anatomical semantics;Step 3, three-dimensional space reconstruction and Logitech rule packaging;Step 4, dynamic fiber tracking and face hearing nerve reconstruction.The application solves the problems of complicated data preprocessing, strong subjectivity and low efficiency of manual layer-by-layer drawing of ROI in traditional reconstruction process, and the fragmentation of multi-software tool chain, realizes the rapid, accurate and standardized reconstruction of face hearing nerve three-dimensional structure.

The application discloses a physical information multi-fidelity neural network three-dimensional cascade flow field reconstruction method based on Fourier characteristics, and relates to the field of aero-engine turbine machinery aerodynamic design. The method comprises the following steps: generating high / low fidelity data sets with boundary consistent and gradient perception sampling; constructing a multi-fidelity neural network containing Fourier characteristic embedding, multi-fidelity collaborative network and RANS equation residual constraint; adopting a two-stage training strategy, first optimizing data fitting loss to learn the flow field mapping relationship, and then jointly improving the conservation with physical loss; finally, realizing fast, high-precision and physically consistent reconstruction of three-dimensional cascade space coordinates to key physical quantities such as velocity, static pressure and temperature. The method significantly reduces the dependence on high-fidelity data, improves the prediction accuracy in high gradient areas, and is suitable for rapid iterative design of aero-engine cascades.

The invention discloses a robot and a robot zero point calibration method, and relates to the technical field of robotics.The robot comprises a first arm and a second arm which are rotationally connected, the first arm is provided with an interference part, the second arm is provided with a collision part, the interference part is used for colliding with the collision part so that the robot can obtain a collision reference position signal, and the collision reference position signal is transmitted to the robot. And the peripheral surface of the interference piece is provided with at least one collision plane and is used for being in linear contact with the collision piece. According to the technical scheme, the structural complexity and collision delay are reduced, and the signal precision is improved.

The invention belongs to the technical field of ecological restoration, and particularly relates to a novel application of morchella esculenta and a restoration method for enriching the biodiversity of soil in a burned area based on wild-imitating cultivation of the morchella esculenta, and the application of the morchella esculenta in enriching the biodiversity of the soil in the burned area. Morchella esculenta is planted under the burned area forest by adopting a wild-imitating cultivation technology, the soil biological activity is activated through the metabolic activity of morchella esculenta, and the soil biocenosis is rebuilt. The restoration method is universal, efficient and high in sustainability, and ecological and economic win-win effects are achieved; through scientific configuration of strains and a cultivation mode, synergistic recovery of physical and chemical properties of soil and biocenosis is realized, allogenic substances such as chemical modifiers, pesticides and chemical fertilizers are not used in the whole process, and the risk of secondary pollution is avoided; the soil biodiversity improvement effect is obvious, and the recovery efficiency is high; the cost is low, and large-scale popularization is easy.

The application discloses a kind of double-layer medium optical characteristic parameter fast detection method based on convolutional neural network, it is related to near-infrared optical detection technical field, for the problem of low precision of existing optical parameter inversion, the present application utilizes the rich time-varying signal provided by time-domain model, by fixing the position of detector, changes light source position to obtain the radiation signal at different distances between the two, to establish and train CNN model, finally, the optical parameters of biological tissue are inverted by the CNN model.Compared with the traditional algorithm, the optical parameter reconstruction of the application can be completed within milliseconds, i.e., the optical parameter inversion accuracy is improved, and the calculation efficiency is greatly improved, the optical parameters of double-layer medium can be quickly reconstructed.

The application discloses a method for reconstructing ultra-high resolution pathological whole sections, comprising the following steps: obtaining an effective tissue area image of pathological tissue fragments by using a fusion mask; obtaining an effective splicing edge of the effective tissue area image by using an edge classification model and an image enhancement strategy; constructing a multi-dimensional matching score system to calculate the geometric matching degree of the splicing edge; screening out an optimal Top-N splicing scheme in combination with a splicing quality evaluation of a deep neural network; realizing iterative stitching of the effective tissue area image based on a genetic algorithm, global topology optimization, sub-pixel level fine tuning and a multi-dimensional matching score system for evaluating splicing quality; and finally outputting an optimal splicing scheme; and reconstructing a pathological tissue whole section image based on the optimal splicing scheme; the application has efficient and accurate automatic splicing and iterative optimization capabilities, and realizes rapid reconstruction of ultra-high resolution pathological whole sections.