364 results about "Gaussian function" patented technology

The invention provides a lightweight real-time semantic segmentation method for a three-dimensional Gaussian scene, and belongs to the field of computer vision and three-dimensional scene understanding. The process comprises the following steps: S1, data preparation: acquiring multi-modal information data including point cloud, images and poses, and preprocessing the data; s2, carrying out two-dimensional semantic segmentation, and generating a pixel-level semantic segmentation map and a semantic tag for the image by utilizing PP-LiteSeg; s3, performing three-dimensional scene modeling and semantic projection, fusing multi-modal information to complete three-dimensional Gaussian scene reconstruction, and projecting two-dimensional semantics to a Gaussian function to form preliminary Gaussian function semantic features; and S4, semantic optimization and segmentation output: considering image difference, semantic difference and smoothness, establishing a loss function, optimizing semantic features and outputting a segmentation result. According to the method, efficient, real-time and accurate three-dimensional Gaussian semantic segmentation work can be completed, and the method is suitable for scene understanding tasks of light-weight and resource-limited equipment.

The invention relates to the technical field of mass spectrum analysis, in particular to an analysis method and system for improving the spectrum quality of a mass spectrum. The method comprises the following steps: scanning a sample by using a mass spectrometer, collecting original mass spectrogram data, and preprocessing the original mass spectrogram data; based on the preprocessed original mass spectrum data, identifying potential peak positions through a multi-scale Gaussian fitting technology, and screening out candidate peaks by calculating the signal-to-noise ratio of each peak; fitting each screened candidate peak by using an asymmetric Gaussian function model, and describing the shape of the candidate peak; and constructing a mass offset correction model to correct the mass spectrum data, and performing super-resolution processing on the corrected mass spectrum data by using a convolutional neural network model. According to the design, peak fitting is carried out by adopting a multi-scale Gaussian fitting technology, a signal-to-noise ratio screening mechanism and an asymmetric Gaussian function model.

The invention relates to the technical field of data processing, and discloses a multi-scene-oriented unmanned aerial vehicle operation resource dynamic configuration and scheduling method and a multi-scene-oriented unmanned aerial vehicle operation resource dynamic configuration and scheduling system. The method comprises the steps of generating multi-scene task constraint data through semantic analysis; constructing a dynamic situation field matrix based on a Gaussian function; establishing a continuous motion velocity field containing pressure gradient and viscosity diffusion; the vortex velocity components are fused to form a self-adaptive control velocity field; and constructing a task allocation vector field to generate an operation area configuration scheme and converting the operation area configuration scheme into a flight control instruction. According to the method, the core problems that multi-scene adaptability is insufficient and real-time performance and optimality are difficult to balance in the prior art are solved, and the intelligent scheduling capability and the collaborative operation efficiency of the unmanned aerial vehicle group in a complex and changeable environment are improved through an innovative semantic understanding and physical modeling combined method.

The invention discloses a single-view human body three-dimensional reconstruction method based on Gaussian surface elements, and the method comprises the steps: collecting image data from a plurality of human body data sets, carrying out the preprocessing through employing a targeted preprocessing strategy according to the data characteristics corresponding to each image data, and obtaining a processed human body data set; constructing a human body 3D Gaussian attribute prediction model, wherein the human body 3D Gaussian attribute prediction model comprises a hierarchical feature extraction module, a feature fusion network and a 3D Gaussian function decoder; training the human body 3D Gaussian attribute prediction model based on the processed human body data set to obtain a trained human body 3D Gaussian attribute prediction model; and performing three-dimensional reconstruction on an input human body picture based on the trained human body 3D Gaussian attribute prediction model, and outputting a reconstructed human body three-dimensional image. According to the method, the manufacturing cost of the virtual digital human is greatly reduced, and the efficiency and accuracy of constructing the dynamic human body model are improved.

The invention relates to a power system risk assessment technology, in particular to a rural power distribution network time sequence voltage distribution assessment method and system based on probability modeling, and the method comprises the steps: carrying out the GMM modeling of the historical data of distributed photovoltaic and load power, constructing the correlation between distributed photovoltaic output GMM and load power GMM through a Gaussian Copula function, and obtaining a joint distribution model; adopting MCS sampling to generate distributed photovoltaic and load power samples as power injection of the rural power distribution network; a Newton-Raphson method is used to complete rural power distribution network time sequence probabilistic load flow calculation; gMM modeling is carried out on the power flow voltage, and a comprehensive evaluation index based on analytic integration is used for evaluating voltage distribution characteristics. The method can effectively solve the time sequence limitation problem of rural power distribution network static probabilistic load flow calculation under high-proportion distributed photovoltaic and novel load access, depicts voltage time sequence distribution characteristics, dynamically evaluates the voltage out-of-limit risk, and provides decision support for operation optimization of the rural power distribution network.

The invention relates to an MT-InSAR self-adaptive fusion measurement method and device for long-term surface deformation of a coal mining subsidence area, and the method comprises the steps: obtaining surface deformation monitoring data in a plurality of modes, and carrying out the at least one of time-space alignment, noise removal and error correction of the surface deformation monitoring data, so as to obtain surface deformation data; calculating coherence indexes of the earth surface deformation data; generating a space adjustment coefficient by using a double-peak Gaussian function model constructed based on a coal mining subsidence area deformation rule; constructing an adaptive weighting method based on the coherence index and the spatial adjustment coefficient of the earth surface deformation data to calculate the weight of the multi-source InSAR data; and performing weighted fusion on the surface deformation monitoring data by using the weight of the multi-source InSAR data to obtain a final surface deformation result. Therefore, the problems of insufficient monitoring precision, incomplete coverage, poor adaptability and the like due to the fact that a single InSAR technology is difficult to comprehensively reflect spatio-temporal evolution characteristics of long-term surface deformation of the wide-area coal mining subsidence area are solved.

The invention discloses a noise signal recognition method based on deep learning. The method comprises the steps of original signal collection, signal preprocessing, noise recognition feature extraction, noise signal recognition model establishment and noise signal intelligent recognition. The invention relates to the technical field of digital signal data processing, in particular to a noise signal identification method based on deep learning, and innovatively provides a self-adaptive signal mode decomposition method based on a combined judgment mechanism of energy threshold value judgment and energy range value judgment. Decoupling and reservation of different frequency bandwidth information in an original signal can be effectively completed, and the recognition accuracy of non-stationary communication noise is improved; a frequency band filter bank constructed based on a Gaussian function is introduced, so that the accuracy of communication signal identification is improved; a method for constructing a noise recognition model by fusing graph neural network structure modeling and an original type reference vector matching mechanism is innovatively proposed, and the recognition robustness of the model to non-stationary communication interference signals is improved.

The invention discloses a method for generating a flat-topped beam array, and the method comprises the steps: introducing a multi-Gaussian function and a hyperbolic function, carrying out the superposition of the hyperbolic function, and constructing a weighting function and a kernel function with Gaussian distribution intensity based on the superposed hyperbolic function and multi-Gaussian function; introducing a partially coherent light construction formula, and constructing a cross spectral density function of a multi-cosine multi-Gaussian correlation light field containing a multi-cosine function at a source plane by combining a weighting function and a kernel function; introducing a free space transmission formula, and constructing a light intensity expression of the multi-cosine multi-Gaussian correlation light field at any transmission distance according to a cross spectral density function of the multi-cosine multi-Gaussian correlation light field, namely, an expression of the multi-cosine multi-Gaussian correlation light field; adjusting parameters of the transmission distance and the multi-cosine multi-Gaussian correlation light field to obtain flat-topped beam arrays with different row numbers and column numbers, and realizing an array form with adjustable flat-topped sub-beams; according to the invention, the number of the sub-beams of the flat-topped beam array can be effectively regulated and controlled.

Disclosed in the present invention is a Gaussian mixture shape method applicable to dynamic modeling of the head of a human body. A Gaussian mixture shape is composed of a neutral Gaussian model representing a neutral expression and a plurality of basic expression Gaussian models corresponding to a plurality of basic expressions. Each Gaussian model comprises a group of three-dimensional Gaussian functions having information such as position, shape, and color. A human face and head model of any expression is efficiently generated by linearly mixing the neutral Gaussian model and the basic expression Gaussian models. The method comprises training image acquisition and preprocessing, Gaussian mixture shape training, and human head animation generation. The present invention has higher drawing quality, can retain more high-frequency details of the head of the human body, and improves the sense of reality and fidelity of drawing; and the present invention has higher operating speed which is more than five times higher than that of the current most advanced method. Therefore, the present invention can be widely applied to movies, animation production, online games, teleconferences, etc., and has high practical value.

The invention discloses an SPH fluid rendering method and system based on a Gaussian function, and belongs to the technical field of computer graphics, and the method comprises the steps: receiving SPH simulated fluid particle data, and constructing a particle connection graph based on a particle neighborhood relation; based on the particle connection graph, extracting local geometric features of each particle through a neural network and aggregating the local geometric features into global geometric features; analyzing the global geometric features into Gaussian function parameters to generate a plurality of Gaussian functions for each particle, and superposing all Gaussian functions in space to form an implicit fluid surface; performing screen space rendering on the implicit fluid surface to obtain a final fluid rendering image; and after foam particles generated by SPH simulation are also mapped into a Gaussian function, a fluid rendering image containing a foam visual effect is obtained through screen space rendering. The method can improve the smoothness and reality of the rendering result while guaranteeing the calculation efficiency, and achieves the consideration of the efficiency and quality of fluid rendering.

A set of images of a scene re received. Each image includes temporal data and spatial data relating to the scene. Based on the spatial data of each image, three-dimensional (3D) Gaussian splatting data is generated. The temporal data of each image and the 3D Gaussian splatting data are inputted to a neural network to generate spatial-temporal 3D Gaussian embeddings. Offset data based on the spatial-temporal 3D Gaussian embeddings is generated. The video of the scene is rendered based on the 3D Gaussian splatting data and the offset data, allowing for improved rendering of video of the scene.

The invention discloses a leakage gas cloud three-dimensional reconstruction method, and relates to the technical field of gas cloud image processing. According to the leaked gas cloud three-dimensional reconstruction method, through combination of a multispectral image data cube and a pre-trained Yolov5 model, precise identification and analysis of a gas cloud leakage area are realized, through radiation correction and adaptive filtering processing, absorbance data of the gas cloud are extracted and fitted, concentration distribution of each layer of gas cloud is further obtained, and the three-dimensional reconstruction of the leaked gas cloud is realized. By stacking data of each layer and applying a three-dimensional reconstruction algorithm, an accurate gas cloud three-dimensional structure is generated, high-precision spatial distribution information is provided for gas leakage monitoring, the monitoring and response efficiency is remarkably improved, the limitation of a traditional method in the aspects of precision and adaptability is overcome, and by integrating multi-view and multi-band image data, the real-time performance of gas leakage monitoring is improved. The gas cloud leakage area can be identified more accurately, and the gas cloud concentration distribution is accurately obtained through asymmetric Gaussian function fitting, so that the gas leakage monitoring precision is improved.

The invention discloses a strip steel surface multiple defect detection method based on fuzzy measurement. The surface of the strip steel is shot and subjected to graying treatment, and a surface grayscale image is obtained; calculating a gray level histogram of the surface gray level image, and performing Gaussian function fitting on the gray level histogram to obtain an image background model; performing calculation and construction according to the image background model to obtain a membership function, and performing defect estimation on the surface grayscale image by adopting the membership function to obtain a membership matrix; and carrying out binarization processing on the surface grayscale image according to the membership matrix to obtain a strip steel surface defect area. The method is accurate and practical in detection, can detect various different types of surface defects, particularly has good detection performance for low-contrast images and fine defects, has relatively high application value for defect detection of the strip steel surface, and is relatively small in calculation amount and relatively high in accuracy due to linear operation mainly concentrated on a single pixel. The realization is easy.

The invention belongs to the technical field of medical image reconstruction, and particularly relates to a CT reconstruction system and method based on discrete Gaussian representation. According to the technical scheme, the method comprises the steps that a Gaussian function set is constructed, and a three-dimensional voxel space is directly represented through learnable Gaussian parameters; carrying out parallel aggregation on Gaussian function contribution values to a three-dimensional voxel grid by adopting a discretization volume reconstruction technology; establishing a geometric projection transformation model to reversely project the three-dimensional reconstruction volume to a two-dimensional projection domain, and minimizing the difference of the projection domain through joint optimization of Gaussian parameters; and introducing an adaptive density control mechanism to dynamically adjust Gaussian distribution. Compared with an existing method, the method has the advantages that an end-to-end discretization Gaussian function representation mode is adopted, global optimization of any CT geometric configuration is supported, high-quality rapid reconstruction within 5-15 minutes is achieved while the detail integrity of the anatomical structure is kept, and a high-precision and high-robustness three-dimensional reconstruction solution is provided for low-dose CT scanning and emergency treatment scenes.

The invention discloses a method for regulating and controlling a cosine multi-Gaussian correlation structure light field, and the method comprises the steps: introducing a hyperbolic cosine function and a multi-Gaussian function, and constructing a weighting function and a kernel function based on the hyperbolic cosine function and the multi-Gaussian function; introducing a partially coherent light construction formula, and constructing a cross spectral density function of a cosine multi-Gaussian correlation structure light field at the source plane by combining a weighting function and a kernel function; introducing a free space transmission formula, and constructing a light intensity expression of a cosine multi-Gaussian correlation structure light field at any transmission distance according to a cross spectral density function, namely, an expression of the cosine multi-Gaussian correlation structure light field; adjusting the transmission distance and parameters of the cosine multi-Gaussian correlation structure light field to obtain light intensity distribution regulation and control of the cosine multi-Gaussian correlation structure light field, and further obtaining an array form in which sub-beams are flat-topped beams according to the light intensity distribution regulation and control; according to the invention, controllable transmission of the cosine multi-Gaussian correlation structure light field can be realized, and effective regulation and control of the array form in which the sub-beams are flat-topped beams can be realized.

The invention provides a head posture estimation method and system based on a neural implicit field, and belongs to the technical field of image processing. According to the method, discretization processing is carried out on a head posture label, and a Gaussian function and a Gumbel function are used for joint modeling to generate an asymmetrically distributed label. Sampling points are arranged at fixed intervals in a three-dimensional space, and global features of an input RGB image are extracted through a RepVang network. And establishing a mapping relationship between image features and spatial sampling points by means of a neural implicit field, mapping face information to a point cloud space, and performing binary classification on each sampling point by using a multi-layer perceptron to generate human face point cloud data. Geometric features of the point cloud data are extracted and fused with global features of the RGB image, and the head posture is predicted through combination of a classification module and a regression module. By adopting the method and the system, the precision and the robustness of head posture estimation can be effectively improved, and a reliable solution is provided for practical application.

Disclosed in the present invention is a people flow analysis system based on Internet of Things information acquisition. The system comprises a data acquisition module, a data storage module, a people flow statistics module, an obstruction analysis module, a coupling analysis module and a predictive analysis module. The data acquisition module acquires all people flow data of a laboratory server room by means of an image acquisition apparatus; then, the people flow statistics module separately performs crowd density analysis, wherein an average distance is determined by means of hierarchical and clustering analysis of head labeling points, and further a Gaussian function is used to determine the size of heads, thus solving the problem of uncertainty caused by a nearest neighbor method when crowd densities are low and uneven; the obstruction analysis module performs feature fusion on features of an obstacle in a coverage space and neighboring crowds to obtain a new data set; the predictive analysis module performs overall people flow predictive analysis on the basis of the new data set and people flow data of crowds not adjacent to the obstacle. Therefore, the accuracy of people flow analysis and control efficiency are greatly improved.

The invention relates to the technical field of unmanned aerial vehicle navigation and control, in particular to an improved sparrow searching method for three-dimensional unmanned aerial vehicle path planning. According to the method, a three-dimensional mountain terrain model is constructed through a Gaussian function superposition method, obstacles and no-fly zones are defined by adopting a cylindrical geometric model, and obstacle avoidance constraints, take-off and landing safety constraints and flight height constraints are set; a comprehensive cost function with path length, threat penalty, flight safety height and path smoothness as sub-targets is constructed, an improved sparrow search algorithm is introduced, and a sine and cosine strategy is adopted in discoverer position updating to dynamically balance horizontal exploration and vertical obstacle avoidance. A Levy flight mechanism is introduced in follower position updating so as to enhance the capability of local development and jumping out of local optimum; the method comprises the steps of population initialization, role division, iterative optimization, collision detection and an elitist retention strategy. According to the method, a safe and smooth three-dimensional flight path with the optimal comprehensive cost is output, and the method is suitable for an actual unmanned aerial vehicle navigation task.

The invention discloses a risk assessment and obstacle avoidance method for state uncertainty of a low-altitude aircraft, and relates to the technical field of aircraft control, and the method comprises the steps: constructing surrounding Gaussian distribution for the state uncertainty of the aircraft according to the state of the aircraft and the historical deviation data of the state; generating field intensity distribution of each risk source in the space according to a Gaussian function; constructing a risk probability field according to the surrounding Gaussian distribution and the field intensity distribution corresponding to each risk source; the expected field intensity of the aircraft in the risk probability field is determined, and then the stress of the aircraft is determined; and adjusting the track of the aircraft according to the stress so as to avoid the obstacle. According to the method, a dynamic aircraft risk probability field suitable for a low-altitude aircraft is designed on the basis of an offset model surrounding Gaussian distribution, field intensity distribution of each risk source in space and an aircraft state probability distribution and dynamic risk field model constructed through fusion, decision support is provided for obstacle avoidance of the aircraft, and the risk probability of the aircraft is improved. And the safety of low-altitude flight is effectively improved.

The invention discloses a self-adaptive cooperative control strategy based on a variable speed reaching law and a neural network, belongs to the field of intelligent control, and mainly aims at estimating and compensating uncertainty, external disturbance and actuator saturation existing in a queue in queue control and improving the stability, robustness and control performance of the whole queue. The method comprises the following steps: establishing a queue system model with uncertainty, external disturbance and actuator saturation; establishing a Gaussian error function approximation strategy; establishing a tracking error; establishing a variable speed reaching law; establishing a queue integral sliding mode error; establishing a queue coupling sliding mode error; establishing an adaptive parameter estimation mechanism for external disturbance and Gaussian function approximation errors in the queue; establishing a neural network estimation mechanism for uncertainty in the queue; and establishing a queue integral sliding mode control strategy based on a variable speed reaching law, a Gaussian error function approximation strategy, an adaptive parameter estimation mechanism and a neural network estimation mechanism. The method is used for queue cooperative intelligent control.

The invention relates to a low ionized layer electron density inversion method based on occultation data. The method comprises the following steps: obtaining a secondary data file provided by an occultation item consisting of a plurality of low-orbit satellites, then carrying out TEC data preprocessing, extracting an F-layer component of an ionized layer in a TEC profile according to two-section linear fitting within a certain height interval range based on the preprocessed data, and obtaining an F-layer TEC component of the ionized layer after interpolation; calculating the TEC component of a low ionized layer according to the occultation observation value after interpolation and the TEC component of the F layer of the ionized layer; constructing an electron density inversion linear equation set based on an Abel weighting function according to the height distribution of the TEC component of the low ionized layer, and solving by adopting non-negative least squares to obtain an inverted electron density profile; and performing smooth fitting on the inverted electron density profile by using a Gaussian function to obtain an electron density profile product. By adopting the method, the inversion precision of the electron density of the low ionosphere can be improved.

The invention relates to the technical field of attitude control, in particular to a moving object attitude control method based on an improved LKF. Predicting prior attitude estimation at the next moment; calculating a process error covariance matrix; calculating a measurement attitude; calculating a quaternion measurement error covariance matrix; constructing feature vectors of acceleration interference and geomagnetic interference amplitude levels, and calculating an interference amplitude level value by using a Gaussian function model; updating the process error covariance matrix and the measurement error covariance matrix by using the interference amplitude level value; calculating a priori attitude estimation error covariance matrix by using the updated process error covariance matrix; calculating a Kalman gain matrix by using the priori attitude estimation error covariance matrix and the updated measurement error covariance matrix; calculating attitude posteriori estimation at the next moment; and performing attitude estimation at the next moment. According to the method, the problems of insufficient estimation precision and poor interference suppression capability of the attitude observation error covariance matrix of the existing LKF are solved.

The invention discloses an electric power engineering construction quality evaluation method and device based on three-dimensional Gaussian modeling, equipment and a medium. The method comprises the steps of abstracting a plurality of pieces of equipment in electric power engineering into a physical parameter group, and performing multi-view image fusion based on images of the electric power engineering; modeling is carried out based on a 3DGS three-dimensional scene, parameterized Gaussian distribution is used for representing the geometric morphology of the power equipment, and mapping of physical parameters and the three-dimensional scene is achieved; projecting the 3DGS model into a two-dimensional rendered image based on differentiable rendering, and realizing gradient propagation from pixels of the two-dimensional rendered image to physical parameters; constructing a loss function, and optimizing Gaussian function parameters and physical parameters based on the image of the electric power engineering and the two-dimensional rendering image; and after the optimization is completed, carrying out physical parameter extraction on the 3DGS model corresponding to the electric power engineering so as to realize quality evaluation, graded early warning or visual display. The invention belongs to the field of engineering evaluation. According to the invention, accurate evaluation of the construction quality can be realized.

The invention provides a narrow-linewidth single-frequency laser frequency stabilization system and method based on a Gaussian filter, and the system employs the Gaussian filter to reduce the optical power of single-frequency laser, and employs the frequency selection characteristic of the Gaussian filter, the frequency spectrum of the single-frequency laser is a single narrow peak, and the frequency stability of the single-frequency laser is improved. When laser frequency passes through a designed Gaussian filter (the fluctuation range of a frequency-stabilized laser source is only on the left side of the center frequency of the Gaussian filter), laser signals can be reduced according to a Gaussian function rule, and along with frequency change, attenuation degrees are different and monotonically change, so that output optical power is different, and the output optical power is changed. And the optical power is compared with the optical power of a preset frequency stabilization target (a half-high point on the left side of a Gaussian filter) to obtain the deviation of the laser source relative to the target frequency stabilization frequency, so that the laser frequency is correspondingly adjusted to achieve the frequency stabilization purpose. The high-sensitivity optical power detection is realized, the response speed and the frequency stabilization precision of the system are improved, and the system is simple in structure and low in maintenance cost.

The invention relates to a bilateral filtering remote sensing image denoising method based on an EMD distance, and the method comprises the steps: firstly, carrying out the clustering analysis of an image polluted by noise, extracting a key spectrum cluster and a center thereof, and generating a fuzzy membership matrix; thirdly, calculating the distance between spectral clustering centers, and constructing a spectral clustering difference matrix; for each pixel of the target image and a neighborhood thereof, calculating an Euclidean distance between the pixels, and substituting the Euclidean distance into a Gaussian function to generate a distance weight; then, extracting fuzzy membership degree vectors corresponding to the pixels from the fuzzy membership degree matrix, and calculating an EMD distance by combining the spectral clustering difference matrix as a spectral difference weight between the pixels; and finally, synthesizing the spectral difference weight and the distance weight, calculating an EMD bilateral filtering coefficient of each pixel, and carrying out EMD bilateral filtering on the image according to the EMD bilateral filtering coefficient, so as to effectively remove Gaussian noise and obtain a clear high-resolution satellite image.

The invention relates to the technical field of image data processing, in particular to an image line extraction method based on a GPU. The method comprises the following steps: calculating convolution of a first-order partial derivative and a second-order partial derivative of a two-dimensional Gaussian function and an image; calculating a line normal direction according to a Hessian matrix of the two-dimensional discrete image, and performing Taylor expansion on the gray level distribution function along the normal direction to obtain a line center point sub-pixel position; and connecting the contour points according to the distance between the adjacent contour points, the normal direction and other information and a connection rule, and extracting lines in the image. The invention provides an image line extraction method based on a GPU (Graphics Processing Unit), which can accurately and efficiently extract bright and dark lines in an image.

The invention discloses an optical fiber connector end face defect detection method and system based on a mixed attention mechanism and dynamic enhancement, and belongs to the technical field of industrial visual inspection. According to the method, the zero optical path difference position of a white light interference curve is obtained through quadratic Gaussian function fitting, and the sensitivity to tiny defect textures is enhanced in cooperation with a multi-dimensional mixed attention mechanism, channel attention and space attention; dynamic adaptive data enhancement is provided, an enhancement strategy is adjusted in real time based on a model training state, and sample diversity is optimized; and a layered pruning and dynamic quantification scheme is designed for edge computing equipment, so that high-precision real-time detection is realized, and the method has certain reference significance in surface defect detection application in the field of precision manufacturing.

A method for detecting a surface defect of a thermal cup, a system thereof, a device and a medium are provided. The method includes: acquiring thermal cup images from different angles, and preprocessing thermal cup images to generate enhanced thermal cup images; performing convolution operation for a first-order derivative of a Gaussian function on the enhanced thermal cup images to determine first filtered images; and determining defect regions according to the first filtered images and the thermal cup images by using a threshold segmentation method based on a bilinear interpolation and a second-order derivative of the Gaussian function. The set parameters are adjusted based on a gradient change and the current illumination environment. The defect regions include a final pit defect region, an upper side polishing print defect region and a lower side polishing print defect region.

The invention provides a multi-frequency time-domain interference stimulation simulation optimization method and device. The method comprises the following steps: constructing a corresponding Gaussian function according to time-domain interference stimulation parameters for a target clinical user, and obtaining Fourier series of each channel of a transcranial electrical stimulator; generating a brain model based on the brain T1 weighted imaging data, and setting corresponding target spot position information to optimize the target spot electric field direction in any direction; determining an effective electrode arrangement area of each axial section of the craniocerebral model and arranging electrodes; and optimizing the electrodes arranged on each axial section so as to determine each grid of the craniocerebral model and the intensity amplitude of the time domain interference stimulation electric field, and optimizing the input current of each channel. According to the invention, multi-frequency stimulation simulation optimization for each channel of the transcranial electrical stimulator can be realized, the focusing performance of time domain interference stimulation can be improved, the calculation complexity of time domain interference stimulation parameter configuration can be reduced, and personalized time domain interference stimulation simulation can be effectively and quickly realized in combination with clinical data.

The invention discloses a runoff prediction method and device based on a Gaussian function and a medium, and relates to the technical field of runoff prediction, and the method comprises the steps: determining the fitting values of a plurality of Gaussian parameters of each historical year based on the monitoring value of the runoff of each month in each historical year; respectively inputting the fitted values of the Gaussian parameters in all historical years into a Gaussian parameter prediction model of the corresponding Gaussian parameters to obtain predicted values of the Gaussian parameters in the future year; determining a Gaussian function value of each month in the future year based on the predicted value of each Gaussian parameter in the future year; determining a predicted value of the runoff volume of each month in the future year based on the Gaussian function value of each month in the future year and a runoff volume prediction model; the runoff prediction model is obtained by training a back propagation neural network. According to the invention, the prediction precision of the runoff volume is improved.