432 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.

Provided herein are systems, methods, computer-readable media, and techniques for providing trusted artificial intelligence (AI) using Fully Homomorphic Encryption (FHE), comprising: (A) providing a deep neural network (DNN)-based model with modified architecture, wherein the modified architecture at least (i) uses a Gaussian function as an activation function and (ii) removes one or more pooling layers; (B) obtaining encrypted data, wherein the encrypted data are generated by applying the FHE to plaintext data; and (C) generating an inference with the DNN-based model based on the encrypted data. Further provided herein are systems, methods, computer-readable media, and techniques for providing trusted AI using stochastic computing, using noise based computing, using an artificial immune system, and with secure multi-party computation (SMPC) based at least in part on watermarking.

The invention relates to the technical field of pollution traceability, in particular to a harbor storage yard dust pollution traceability method and system, and the method comprises the following steps: collecting the real dust concentration and dust diffusion influence factors of different monitoring points in a harbor storage yard, and constructing a dust diffusion evaluation model; when the position of the pollution source is not determined, obtaining the position and the source intensity of the pollution source by using an improved sparrow search algorithm based on the real dust concentration, the dust diffusion influence factors and the dust diffusion evaluation model; obtaining a matrix estimation formula for defining an inverse Gaussian function according to a least square method estimation method, and further defining the inverse Gaussian function according to the dust diffusion evaluation model; when the position of the pollution source is determined, the source intensity of the pollution source is obtained based on an inverse Gaussian function according to the real dust concentration and the dust diffusion influence factors. According to the invention, a large amount of monitoring equipment which is high in cost and complex in installation and maintenance does not need to be installed, and the position and the source intensity of the pollution source of the port storage yard can be traced intelligently, accurately and efficiently.

The invention provides a three-dimensional reconstruction method and device, equipment and a storage medium, and belongs to the field of image processing, and the method comprises the steps: obtaining point cloud data of a plurality of visual angles of a target scene of a to-be-constructed model; based on the point cloud data and a preset spherical Gaussian function, initializing parameters of the three-dimensional Gaussian primitive to obtain an initial three-dimensional model; selecting one target image from the plurality of target images as a supervision image; projecting a three-dimensional Gaussian primitive in the initial three-dimensional model to a two-dimensional view space according to a camera parameter matched with a visual angle corresponding to the supervision image to obtain a rendered image; and determining whether the three-dimensional model is accurate or not according to the rendered image and the supervised image, and if the three-dimensional model is not accurate, optimizing parameters of three-dimensional Gaussian primitives in the initial three-dimensional model until an accurate target three-dimensional model is obtained. The three-dimensional Gaussian primitive is constructed based on the preset spherical Gaussian function, so that illumination of the constructed three-dimensional model is closer to a real environment, and a real and accurate three-dimensional model is obtained.

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 belongs to the field of building surveying and mapping, and particularly discloses a building three-dimensional defect detection method, device and equipment based on multi-sensor fusion, and the method comprises the steps: obtaining data detected by a laser radar, an IMU, a wheel speed meter and image capturing equipment; fusing the data observed by the laser radar, the IMU and the wheel speedometer based on the ESKF, and determining the speed and the pose of the vehicle-mounted acquisition equipment; obtaining a three-dimensional point cloud model under a world coordinate system by combining the speed, the pose and the point cloud data observed by the laser radar; generating a corresponding three-dimensional Gaussian function according to the three-dimensional point cloud model, projecting the three-dimensional Gaussian function in a world coordinate system to a coordinate system of an image capturing device in combination with a pose, and performing microrasterization to obtain a preliminarily rendered image; and constructing a loss function in combination with a visual image observed by an image capturing device, optimizing the three-dimensional Gaussian function, and iterating to obtain a rendered three-dimensional visual model. According to the invention, fusion positioning and high-precision mapping of the building are realized in combination with multiple sensors.

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 relates to the field of bridge detection, in particular to a bridge structure detection method and system based on 3D Gaussian scattering. The method comprises the steps that 3D Gaussian scattering data and environment sensor data of a bridge structure are collected at the same time; based on a pre-trained deep learning model, carrying out dynamic fusion on the acquired 3D Gaussian scattering data and environmental sensor data; adjusting the weight of the 3D Gaussian scattering data in real time according to the environment sensor data, compensating the influence of environment factors on the scattering data, and obtaining a fused comprehensive data set; modeling the fused comprehensive data set by using a 3D Gaussian function to generate a Gaussian radiation field model; and the health condition of the bridge structure is intelligently detected, a detection result is obtained, potential damage is identified, and maintenance suggestions are provided. According to the invention, the bridge detection precision is improved, the interference of environmental factors is reduced, and dynamic monitoring and intelligent evaluation are realized.

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 provides a traveling wave head extraction method and system, electronic equipment and a storage medium. The method comprises the following steps: detecting an original traveling wave signal to obtain the original traveling wave signal; calculating the fast Fourier transform of the original traveling wave signal and the fast Fourier transform of the window function introducing the correction factor so as to respectively obtain a discrete form of the original traveling wave signal and a Gaussian function under the optimal frequency; calculating a product of a discrete form of the original traveling wave signal and a Gaussian function under the optimal frequency according to the frequency sampling point, and calculating to obtain discrete S transformation; intercepting two-dimensional diagrams of the traveling wave fault signal with time and amplitude as an x axis and a y axis; on the basis of discrete S transformation of the traveling wave, the amplitude difference of adjacent time nodes in the two-dimensional diagram is adopted, a threshold value is set at the same time, the detection time of a first traveling wave head is obtained, the position of a sudden change point is determined, and the traveling wave head is extracted. According to the method, various fault transient states can be detected, the detection speed is high, the sensitivity is enough, and the influence of noise can be effectively reduced.

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 relates to a semantic and pose coupled image reconstruction method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring depth information and semantic information of each frame of image; generating an initial local point cloud corresponding to each frame of image according to the depth information; based on a two-dimensional Gaussian function, converting the initial local point cloud into an initial local Gaussian point cloud, and according to the initial local Gaussian point cloud, adjusting a camera pose corresponding to each frame of image to obtain an initial camera pose; performing global calibration on the initial camera pose according to the semantic information to obtain a calibrated camera pose; and performing three-dimensional image reconstruction according to the calibrated camera pose. The method can give consideration to both efficiency and accuracy.

The invention relates to a novel pulse neural network synaptic weight adjustment method, and belongs to the field of artificial intelligence. Firstly, a traditional pulse time sequence dependence plasticity rule is improved based on the distribution trend of a Gaussian function, so that the weight change corresponding to the optimized synaptic plasticity rule when the time interval tends to 0 is larger, and when the time interval is gradually increased, the weight change is in an accelerated descending trend; the method comprises the following steps: firstly, performing synaptic weight local regulation on a neuron model to improve the local regulation capability of the synaptic plasticity of the neuron, then introducing reinforcement learning, associating Q learning with the synaptic plasticity in the neuron model, and realizing the global regulation and control of the plasticity on the basis of the synaptic weight local regulation. According to the above synaptic plasticity rule, the spiking neural network constructed based on Leaky Integrating and Fire is subjected to weight adjustment, so that the spiking neural network has better performance.

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 an overlapping response separation and peak positioning method for thickness measurement of an ultrathin multilayer transparent material in spectral confocal. The method comprises the following steps: 1, acquiring a spectral confocal calibration spectrum and a transparent material overlapping response spectrum; 2, evaluating random noise distribution, and obtaining an intensity detection threshold value and a shape coefficient; 3, obtaining an overlapping response initial peak value position by using the second derivative of the overlapping response spectrum; 4, calculating a generalized Gaussian function fitting initial parameter of overlapping response; 5, iterating the overlapping response parameters until the fitting residual change before and after iteration meets the precision requirement; 6, discarding overlapping responses which do not meet the measurement constraint; 7, the step 5 and the step 6 are executed again until all the overlapping responses meet the measurement constraint, and the peak value position of each overlapping response is obtained. According to the invention, the separation of spectrum confocal overlapping response is realized, the peak value positioning precision is improved, and the thickness of the ultrathin multilayer transparent material can be measured.

The invention discloses a semi-supervised hyper-spectral image intelligent classification method. Marked samples and unmarked samples are utilized to carry out accurate ground feature recognition. According to the method, firstly, a multi-scale deformable spectral spatial feature extraction module is constructed, features can be extracted and fused on multiple scales, and the introduction of deformable convolution can extract distribution features more conforming to samples. Then, through a proposed pseudo label generation strategy, Gaussian function weighting is adopted, and the utilization efficiency and the model performance of the pseudo labels are further improved. In the training process, a high number and high quality pseudo labels are kept to effectively utilize unmarked samples. And finally, capturing remote dependence and learning effective feature representation of the hyperspectral remote sensing image through Transform. Experimental results prove that the method has effectiveness and superiority under the condition of limited labeled samples.

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 relates to the technical field of image processing, in particular to a mining truck edge detection method based on a multi-scale edge membership map. The method comprises the following steps: 1, constructing a multi-scale image pyramid: acquiring a grayscale image as input, performing multi-scale pixel sampling, and performing fuzzy processing by using a Gaussian function to construct a Gaussian image pyramid; 2, gradient calculation: respectively calculating the gradient of the image in the x direction and the y direction by using a Scharr operator; 3, the standard deviation of the local gradient difference is calculated, edge pixels and noise points are further effectively distinguished by calculating the standard deviation of the local gradient difference through the statistical characteristics of the local gradient difference, and therefore the edge detection precision is improved; 4, calculating an edge membership degree; 5, fusing a multi-scale edge membership map; 6, adaptive lagging connection: adopting lagging connection to further process the multi-scale edge membership fusion image so as to improve edge continuity and positioning accuracy; according to the invention, the reliability and safety of automatic loading operation are 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.