244 results about "Gaussian function" patented technology

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 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 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 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 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 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 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.

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 an offshore wind power plant regular layout optimization method considering submarine topography and turbulence intensity, and the method comprises the steps: carrying out the grid discretization of a target region, and defining a layout rotation angle; a mixed integer linear fractional programming model with the minimum leveling cost per kilowatt-hour as the target is constructed, a wake flow model based on an axisymmetric cosine function and a turbulence model based on an axisymmetric double-Gaussian function are comprehensively integrated, and piecewise linearization is conducted on a turbulence nonlinear term; the method comprises the following steps: pre-defining a layout mode and regular layout constraints, and matching with engineering constraints such as fan number, power and safety distance; and a bounded Dinkelbach algorithm is adopted to carry out iterative solution on the sub-problems decomposed according to angles, and a global optimal layout is obtained. According to the method, wake flow loss, turbulence fatigue and terrain-related cost can be effectively and synergistically optimized, and an economical and efficient layout scheme is provided for offshore wind plant planning.

The invention discloses a reliability-based titanium alloy medium plate welding optimization control system and method for a new energy vehicle, and the system comprises a data obtaining and preprocessing module, a performance prediction module, a reliability evaluation module and a process parameter optimization module, welding process parameters and the tensile strength of a welding joint, the welding width of the bottom surface of a welding seam and angular deformation under the corresponding welding process parameters are recorded; the performance prediction module is used for training and learning the sample data acquired in the data acquisition module, the trained performance prediction module is used for prediction, and the weight of an RBF neural network and the initial value of a Gaussian function parameter are optimized based on an improved genetic algorithm in the module; the reliability evaluation module is used for establishing a welding non-probabilistic reliability model based on an ellipsoid convex model and considering the tensile strength of a welding joint, the weld penetration depth and deformation, and evaluating the rationality of the current process parameter group by comparing the reliability obtained through calculation with a reliability threshold value; the process parameter optimization module is used for constructing a fitness function about the tensile strength of a welding joint, the weld width of the bottom surface of a welding seam and angular deformation by adopting a weight method, and optimizing an optimal process parameter group based on a particle swarm optimization algorithm of simulated annealing; according to the invention, efficient and reliable welding process optimization can be realized, the reliability and the performance stability of a welding joint are ensured, and the experiment trial and error cost is reduced.

The invention relates to the technical field of supply chain scheduling, and discloses an AI-based supply chain scheduling method and system, and the method comprises the steps: obtaining the order, resource, logistics topological data and historical performance data of a supply chain; generating a delivery uncertainty embedding vector by using the graph neural network; determining a central moment and a slope of an asymmetric trapezoidal function and a mean value and a variance of a Gaussian function according to the delivery uncertainty embedded vector, and constructing a fuzzy time window for a scheduling task of each destination node; obtaining a reinforcement learning model, and outputting a weight combination of a cost item, a time item and a robustness item in the scheduling objective function; determining a scheduling objective function according to the weight combination, and solving the scheduling objective function; the crossover operation of the genetic algorithm is key sub-graph exchange, and is realized by identifying and exchanging key path sub-graphs of two parent scheduling schemes. According to the method, complex performance risk distribution can be accurately described, real-time changes can be adapted, and meanwhile the calculation amount is reduced.

The invention relates to the technical field of functional group analysis, and discloses an adsorption material surface functional group analysis method and system based on a Gaussian function model.According to the method, proton binding isotherm data of an adsorption material under different pH conditions are obtained through an incremental titration method, and the proton binding isotherm data are input into a Gaussian function model; the method comprises the following steps: carrying out characterization analysis on different types of functional groups, fitting experimental data by using a nonlinear least square method to obtain optimized parameters, verifying the accuracy of the optimized parameters by using a preparatory test means, analyzing the proportions of various functional groups in the adsorption material based on the verified optimized parameters, and generating a surface functional group distribution map. According to the method, distribution of different functional groups is accurately analyzed through the Gaussian function model, the defect that quantitative analysis of a traditional method is not accurate is overcome, high accuracy of a result is ensured, detailed data support is provided for design and optimization of an adsorption material, and the problem that in the prior art, when multiple functional groups coexist, the detection effect is not accurate is solved.

The invention discloses a virtual reality level simulation method based on a material point method and gridding surface reconstruction, and belongs to the crossing field of computer graphics, physical simulation and virtual reality technologies. The method comprises the following steps: S1, classifying and storing position coordinates of material points according to material numbers of the material points, and traversing the coordinates to obtain a boundary of a bounding box of the material points; s2, selecting a grid size and a boundary extension distance for reconstructing the surface in a self-defined manner, and generating a grid; s3, selecting a Gaussian function to calculate the contribution of each substance point to the overall substance point density field, and obtaining parameters; s4, calculating the contribution of each substance point to the overall substance point density field, and performing accumulation to obtain the overall substance point density field; and S5, obtaining a contour surface of the overall material point density field, and exporting the contour surface as a rendering engine general three-dimensional model to obtain a simulation visual effect. According to the method, the problems that in the prior art, tens of millions of substance points cannot be processed, reconstruction parameters are difficult to select, and connection with a high-end rendering engine is not smooth can be solved, and the virtual reality level simulation visual effect is achieved.

The invention discloses a tunnel internal illumination control method based on multi-data analysis, and relates to the technical field of tunnel illumination control. According to the invention, the construction of the brightness control curve takes the Gaussian function as the core, the continuous attenuation of the brightness from the natural brightness of the hole to the reference brightness in the hole is realized through the brightness transition curve, and the Gaussian attenuation coefficient is solved in combination with the length of the transition section, so that the smoothness of the brightness along with the position change is ensured; the problem of visual discomfort caused by sudden brightness change in traditional stepped dimming is solved; in addition, by judging whether the intersection point exists in the brightness transition curves of the transition sections of the entrance and the exit, the curves are intercepted or recalculated, and the illumination control conflict caused by overlapping of the transition sections in the short tunnel is effectively solved. According to the technical scheme, the illumination in the tunnel is accurately controlled, so that the vision of a driver is always in a comfortable and adaptive state in the whole process of entering, driving out of the tunnel and passing through the short tunnel, the obstacle recognition distance is remarkably prolonged, and the risk of accidents such as rear-end collision and lane departure is reduced.

The invention discloses a roadside multi-mode fusion sensing method. According to the method, an initial 3D view cone tensor with depth information is generated through a depth estimation network, features of pixel points are projected to a coordinate system in combination with internal and external parameter matrixes of a camera, then a three-dimensional space point coordinate set is generated according to projected feature position information, a Gaussian function is adopted to distribute weights of neighborhood grids, and a three-dimensional space point coordinate set is obtained. And finally, inputting the image features and the features into a fusion unit, respectively carrying out uncertainty modeling on the two types of features, automatically generating a fusion weight, and finally, carrying out normalization weighted aggregation on the features in the neighborhood so as to reduce the position approximation error and obtain the calibrated image features, and finally, inputting the image features and the features into the fusion unit, and respectively carrying out uncertainty modeling on the two types of features and automatically generating a fusion weight. And dynamic optimization is carried out according to a self-supervised loss function in the fusion process, so that the stability of multi-modal feature fusion is ensured, the detection accuracy is high, the performance is relatively good, and the problem of global feature dislocation caused by external parameter deviation and depth error can be avoided.

The present application relates to the technical field of aquaculture, and particularly relates to an improved PSO-based water quality parameter prediction method and system for aquaculture, which comprises collecting water quality parameters at different positions and depths in a breeding pond; training an improved radial basis function (RBF) neural network using training set data; and optimizing the parameters of the improved RBF neural network model using an improved particle swarm optimization (PSO) algorithm. The present application introduces a mixed Gaussian function and an abnormal S-shaped function into the radial basis function of the traditional RBF neural network, thereby solving the problem of weak capability of the model in nonlinear data modeling. Furthermore, the present application improves the inertia factor and the learning factor in the traditional PSO algorithm, thereby solving the problems of slow parameter convergence speed and poor global search capability in the RBF neural network.

The invention discloses a comprehensive assessment method for weak links of a power system involving multi-dimensional indexes in extreme weather. The method comprises the following steps: firstly, establishing a multi-dimensional index system containing safety and optimized operation, wherein the system covers the line outage probability, the load loss probability, the electric energy insufficiency expectation, the line heavy load rate and the network loss rate; secondly, constructing a line fault model and a cascading fault model under extreme storm weather, describing fault probability change in combination with a Gaussian function and Weibull distribution, and simulating line segment failure by adopting Poisson distribution; a system state after a fault is calculated through a direct-current optimal power flow model, and a large amount of simulation data is obtained in combination with a non-sequential Monte Carlo method. Furthermore, a fuzzy analytic hierarchy process and a CRITIC method are adopted to calculate subjective and objective weights respectively, subjective and objective comprehensive evaluation is realized through weight fusion, and weak links of the system are identified. The method gives consideration to evaluation scientificity and calculation efficiency, and is suitable for real-time risk evaluation and weak link identification of a large-scale power system in extreme weather.

The invention discloses a high-frequency transformer winding potential distribution Gaussian fitting calculation method, system, medium and equipment, and the method comprises the steps: measuring the complex permeability of a high-frequency transformer magnetic core material and the complex dielectric constant of a packaging epoxy material; a finite element model is built according to the actual structure and size of the high-frequency transformer, magnetic core complex permeability and epoxy complex permittivity are substituted, equivalent electrical parameters of the high-frequency transformer are extracted through simulation analysis, and a broadband equivalent circuit model of the high-frequency transformer is built; heating the high-frequency transformer by using a drying oven, and measuring the impedance amplitude and phase angle frequency response of the high-frequency transformer to obtain the impedance frequency response at the corresponding temperature; and taking the measured impedance frequency response characteristic curve as a target function, multiplying equivalent parameters in the initial broadband equivalent circuit model by a frequency-dependent scaling coefficient formed by a multi-peak Gaussian function, and determining optimal Gaussian function fitting parameters by using an optimization algorithm so as to obtain an accurate broadband frequency-variable equivalent circuit model.

The invention provides a multi-component spectral feature selection method based on double-layer optimization, and aims to solve the problem of model performance reduction caused by high dimension, redundancy and noise interference in hyperspectral data. The method comprises the following steps: performing binary and real value vector double-layer coding on individuals, and randomly generating an initial population based on a fitness value; and then, calculating an importance score delta of each spectral variable in evolutionary iteration in combination with a filtering type Relief feature selection method so as to dynamically guide population optimization. A Gaussian function dynamic noise reduction method based on importance scores is designed in the optimization process, and interference noise existing in spectral variables is optimized and fitted through real number vectors; and finally, obtaining an optimal spectral feature subset through an environment selection strategy. According to the method, the data dimension can be effectively reduced, noisy and redundant spectral variables are eliminated, and the accuracy of the model is remarkably improved.

The invention discloses a self-adaptive sound equipment equalization calibration method. The method comprises the following steps: acquiring an audio signal of an audio system and carrying out short-time Fourier transform on the audio signal to obtain a frequency response curve; calculating a target level according to the frequency response curve; carrying out smoothing processing on the acquired frequency response curve; whether a peak value larger than a threshold value exists in the smoothed frequency response curve or not is judged, the influence of a current peak filter on frequency response is simulated through a Gaussian function according to peak filter parameters, the influence is superposed on the smoothed frequency response curve, and a new adjusted frequency response curve is obtained; if the number of iterations is greater than or equal to a preset value, ending iteration, outputting a peak filter parameter, and configuring a peak filter by adopting the peak filter parameter; and inputting the audio signal into a plurality of configured peak filters to obtain an audio signal after equalization calibration. The sound equipment equalization calibration efficiency is high.

This invention provides a method and system for detecting impurities in flunarizine hydrochloride raw material. The detection method includes: acquiring the original chromatographic signal, performing empirical mode decomposition for denoising and baseline optimization to obtain a baseline-corrected and denoised chromatogram; determining the signal-to-noise ratio based on the energy relationship between noise and signal in preprocessing, determining the optimal analytical scale through a preset function, determining the number of chromatographic peak components by screening ridges through continuous wavelet transform, and extracting initial model parameters; establishing a mathematical model for the convolution of a standard Gaussian function and a modified Logistic distribution function for each component peak, using a trust-region reflection algorithm for nonlinear iterative fitting, with the objective function containing a least-squares residual term and a weighted regularization term; after the objective function converges, obtaining the optimal model parameters, and calculating the integral area of ​​each impurity component peak to determine its content.

The present invention relates to the field of image processing technology, and discloses an image clarity determination method, system, and image uploading method. The image clarity determination method comprises: processing an image using a Gaussian function and an edge operator to obtain a gradient intensity map at each pixel point of the image; determining the number of first pixel points and the number of second pixel points of the image based on the gradient intensity map at each pixel point, wherein the gradient intensity of the first pixel point is greater than a first threshold value, and the gradient intensity of the second pixel point is greater than a second threshold value; and determining the clarity of the image based on the number of the first pixel points and the number of the second pixel points. The present invention can use a machine with low computing power to more accurately determine the image clarity, so that the image quality can be reviewed before uploading the image to solve the pressure on the server caused by repeated uploading.

The application discloses a kind of vacuum switch contact ablation intelligent evaluation method based on physical information fusion, belong to vacuum circuit breaker technical field.The method of the present application includes: according to the data set of vacuum switch breaking type obtained from vacuum arc experimental data, construct physical information enhanced support vector machine to predict breaking type of working condition;Vacuum arc simulation model is extracted from each working condition Contact surface energy flow density radial distribution data, and is fitted by Gaussian function;Based on the energy flow density radial distribution curve obtained by fitting, a vacuum arc simulation data set is constructed, and a physical information enhanced Gaussian process regression is constructed to predict the energy flow density of the working condition;Combining the heat transfer model to solve the radial distribution of contact surface temperature, further quantitatively evaluate the ablation degree of contact surface, and output the ablation degree level.The present application embeds the physical law of vacuum arc into machine learning model, realizes the breaking type prediction, energy flow density prediction and contact temperature rise calculation and ablation degree calculation and evaluation.

This invention discloses a large image processing method and system based on adaptive slicing and feathering fusion, comprising: acquiring the original image and parameters; adaptively determining the slicing parameters through iterative calculation with a minimum overlap width requirement as a constraint, and outputting slicing configuration information; expanding the boundaries of the original image according to the filling amount, and extracting multiple image slices with sizes conforming to the input size of the target processing unit from the expanded image; and performing feathering fusion and layered stitching on the image slices using a weight mask generated based on a Gaussian function based on the overlap size to reconstruct a final image with the same size as the original image. This invention calculates the minimum slice grid layout required to meet seamless stitching quality requirements. Through the synergy of reflection filling and Gaussian weighted feathering fusion techniques, it achieves imperceptible pixel value changes at the seams while minimizing the total number of calls to downstream processing units, thereby resolving the contradiction between image quality and computational cost.

The demodulation method based on the ultra-weak fiber grating OFDR includes: performing fast Fourier transform on the time domain reflection signal collected by the OFDR system to obtain a frequency domain spectrum; setting an amplitude threshold, identifying the UW-FBG array reflection peak in the frequency domain spectrum, calibrating the spatial position of the sensing unit, and intercepting the window segment spectrum signal corresponding to each UW-FBG; performing inverse Fourier transform on the intercepted window segment spectrum signal to restore the spectrum signal of a single UW-FBG; for the reconstructed spectrum signal, selecting a fitting region containing the Bragg reflection peak, iteratively optimizing the Gaussian function fitting parameters by the least square method, and demodulating the peak Bragg wavelength; traversing the window segment spectrum signals of all UW-FBGs and repeating the above steps to obtain the Bragg wavelength shift distribution of the whole sensing region. The demodulation method based on the ultra-weak fiber grating OFDR improves the high-precision detection characteristics of the OFDR, realizes dynamic demodulation, significantly improves the sensing demodulation rate, and simplifies the algorithm complexity.

The application discloses a path planning method and device, a vehicle and a storage medium. The method comprises the following steps: acquiring obstacle information, the obstacle information being used for indicating the position and shape of an obstacle in an environment in which a vehicle is located; acquiring vehicle speed information; determining a target region containing the obstacle based on the obstacle information and the vehicle speed information; and determining a planning path based on a two-dimensional Gaussian function and the target region, wherein the planning path does not coincide with the target region. The technical scheme provided in the embodiments of the application can provide accurate driving guidance when the vehicle has an obstacle avoidance requirement, improve the accuracy of path planning in an obstacle avoidance scene, and thus improve driving safety.