232 results about "Kernel density estimation" patented technology

The invention discloses a three-dimensional scene reconstruction method and device based on large model geometric prior, and a medium, and aims to solve the problems that a conventional 3DGS is liable to have artifacts and detail loss in geometric discontinuity, data redundancy and illumination variation scenes, and predicts a dense depth map and a normal map from a monocular image by using a pre-trained large model. The position and form of the Gaussian kernel are constrained as additional geometric priori; a primitive adjustment strategy based on kernel density estimation is introduced in the training stage, small Gaussian primitives with similar structures and adjacent spaces are combined into a large Gaussian primitive, the rendering quality is kept, redundancy is reduced, and the volume of the model is reduced; an exposure coefficient is adaptively estimated for each input image, an exposure compensation image loss function is constructed, and floating artifacts caused by illumination differences at shooting moments are eliminated. Experiments show that compared with the prior art, the method improves the three-dimensional reconstruction precision and real-time rendering quality of complex illumination and less-texture areas in a public data set and an unmanned aerial vehicle aerial photography scene.

The invention relates to the technical field of power system planning, and discloses a new energy grid-connectable capacity margin evaluation method based on spatio-temporal distribution characteristics, which comprises the following steps: constructing a joint probability distribution model based on nonparametric kernel density estimation and a Copula function by using historical and meteorological data of a wind and light station, and generating a random output scene reflecting the spatio-temporal complementarity of wind and light resources; constructing a multi-dimensional stability constraint set containing a generalized short circuit ratio and broadband oscillation as a safety boundary of a random output scene; establishing a grid source interaction control strategy considering energy storage adjustment and direct current power modulation by taking grid-connected capacity maximization as a target, solving an optimal installed capacity scheme, and activating the grid source interaction control strategy to perform dynamic correction when a scene is out of limit; the maximum grid-connected capacity distribution meeting the constraint is counted, and the shadow price of the constraint is calculated to identify the bottleneck. According to the method, the wind and light resource complementary benefit and the system regulation potential can be quantified, and the new energy grid-connected capacity evaluation margin is improved on the premise of guaranteeing the safety.

The invention provides a non-stationary industrial process monitoring method and system. The method comprises an offline training stage: calculating a time Laplacian matrix and a space Laplacian matrix based on a historical data matrix; constructing an objective function of the stationary subspace analysis method, and adding a time constraint term of a time Laplacian matrix and a space constraint term of a space Laplacian matrix into the objective function; solving the objective function to obtain a stable projection matrix; calculating a stationary component and a monitoring index of each sample in the data matrix X in sequence; determining a control limit by using a kernel density estimation method; an online monitoring stage: based on the real-time operation data x, calculating a stationary component of the real-time operation data x and a corresponding real-time monitoring index according to the stationary projection matrix, and if the real-time monitoring index is greater than a control limit, judging that the operation of the non-stationary process has a fault; the monitoring accuracy can be improved.

The invention relates to a spacecraft maneuver detection method based on TLE data and kernel density estimation. The invention relates to the technical field of spacecraft maneuver detection. The method comprises the following steps: acquiring and inputting satellite historical TLE data; gaussian filtering denoising is carried out on the input data; based on the de-noised data, propagation forecasting is carried out through an SGP4 model; constructing a joint residual sequence, and carrying out error distribution modeling and sample scoring; determining a maneuvering threshold value, and judging to obtain abnormal data; and aggregating abnormal data and carrying out maneuvering identification. The method can still realize accurate and stable track maneuver automatic detection under the constraint conditions of unstable TLE data, complex error distribution, no external auxiliary data and the like. The method has good adaptability, robustness and interpretability, and is a track anomaly detection technical scheme which is complete in structure, rigorous in logic and capable of achieving engineering landing.

The invention relates to the technical field of intelligent operation and maintenance of an electric power system, in particular to an inspection sample image data enhancement method for an electric power artificial intelligence platform, which is used for solving the problems that in the prior art, history and equipment knowledge cannot be fused to construct a forbidden area, a co-occurrence rule and component association, cross-component defect positions cannot be effectively adjusted, and the detection accuracy is poor. Defect distribution is difficult to accurately control, and physical rationality and engineering credibility are reduced. According to the method, a forbidden area, a co-occurrence rule and component association are constructed by fusing history and equipment knowledge, masks are generated through kernel density estimation to suppress invalid defects, co-occurrence frequencies are counted based on feature vectors, co-occurrence relationships are determined by combining distances and similarities, and the masks, matrixes and graphs are embedded and coded into conditional vectors, so that the non-ineffective defects are suppressed. And zero setting is performed on a forbidden area in the generative network, illegal co-occurrence is filtered, and cross-component defect positions are adjusted, so that defect distribution is accurately controlled, and physical rationality and engineering credibility are enhanced.

The invention discloses a GIS partial discharge fault positioning method based on multi-feature fusion and kernel density estimation optimization, and the method comprises the following steps: collecting a partial discharge original pulse signal, carrying out the signal processing, and outputting a first processing signal; performing adaptive robust baseline determination on the first processing signal, detecting a wave head position based on multi-feature fusion and a dynamic threshold voting mechanism, and outputting a wave head arrival time; carrying out time synchronization matching on the wave head arrival time, and calculating and outputting a preliminary positioning result based on a time difference of arrival method; and accumulating a plurality of preliminary positioning results, performing optimization by adopting a kernel density estimation method, and outputting a final partial discharge fault position. According to the method, impulse noise and white noise are effectively suppressed, stable signals are provided for subsequent processing, the detection precision of the wave head arrival time is remarkably improved, the problem that false detection and missing detection are caused by the fact that a traditional fixed threshold value or a single feature is prone to noise interference is solved, the random error of single-time positioning is greatly compressed to be within 0.07 m from 0.33 m on average, and the overall positioning precision is improved by 80%.

The invention relates to the technical field of unmanned aerial vehicle management and control, and discloses a low slow small unmanned aerial vehicle countering method and system, which eliminates space-time deviation and redundant information of different sensor data by acquiring multi-source data and performing fusion processing. Furthermore, the target tracking trajectory of the low-slow small unmanned aerial vehicle is processed and generated through an adaptive Kalman filtering algorithm, and real-time smooth tracking of the trajectory is realized. Furthermore, probabilistic region prediction is performed by combining a Monte Carlo method and a kernel density estimation method, and a threat region prediction result of the low-slow small unmanned aerial vehicle is generated, so that the possible motion range of the target is comprehensively covered, and the motion intention of the target is accurately captured. Furthermore, in combination with a preset constraint condition set, a target tracking trajectory and a threat area prediction result, the interception success rate mathematical model is solved by using an improved particle swarm algorithm, and a collaborative countering strategy is obtained, so that the limitation of an existing countering method is avoided, and the accuracy and efficiency of countering are improved.

The invention discloses a power grid state prediction method under high wind power permeability based on multi-source data fusion, and the method comprises the steps: firstly analyzing the correlation between variables through feature engineering and a Pearson's correlation coefficient, and expanding a data set through a sliding time window to mine time domain information; establishing a graph convolution network model of an adaptive adjacency matrix, dynamically capturing the relationship between fans, and extracting spatial features through multilayer convolution to realize information transmission; and carrying out probability density analysis on the wind power prediction error by adopting a kernel density estimation method, determining an error interval boundary value, and realizing interval prediction by combining a deterministic prediction result. The system based on the method comprises a data preprocessing module, a model building module and an uncertainty analysis module which are matched with a processor and a memory to operate. According to the scheme, the accuracy and stability of power grid state prediction in a high wind power permeability scene can be effectively improved, the certainty and the interval prediction result are considered, and a reliable basis is provided for power grid dispatching and optimized operation.

The invention relates to a method for determining at least one safety-critical peak region (R1, R2, R3) on the basis of an indicator value (Z) for statistical characterization of event descriptions detected by at least one vehicle in a location-related manner, wherein those inflow- and outflow-free road sections of which the section length is greater than or equal to a predetermined minimum section length are identified on the basis of a road map or navigation map. Identified road sections which adjoin one another are connected to form in each case a section chain (10) with a starting point (11) and an end point (12). Event descriptions comprising a geoposition (P1, P2) and at least one parameter of a vehicle environment model are detected by vehicles and transferred to an analysis system. The analysis system in each case assigns a nearest road network location (O1, O2) of a section chain (10) to a geoposition (P1, P2) of an event description and describes it by a longitudinal distance (L, L1, L2) in relation to the starting point (11) of the section chain (10). For at least one section chain (10), a profile of a density value (p) of event descriptions that is related to the longitudinal distance (L, L1, L2) from the starting point (11) is determined by means of a kernel density estimator of a predetermined bandwidth. The profile of the density value (p) is transformed, using local, bandwidth-related, Z standardization, into a Z profile (Z1, Z2) of a Z value (Z) which is mean-free in relation to bandwidth. In each case a region of road network locations (O1, O2) along the motorway-like road (100) which are connected along a section chain (10) between a peak beginning (Lmin) and a peak end (Lmax) and at which the Z value (Z) exceeds a predetermined threshold value (θ) is identified as a safety-critical peak region (R1, R2, R3).

The invention discloses an automatic driving laser radar simulation method for high-fidelity material modeling, and the method comprises the steps: carrying out the high-fidelity modeling of a complete physical process of a single laser channel, and designing a complete technical chain from laser beam modeling, material reflectivity modeling, light path modeling to post-processing. In laser beam modeling, energy space distribution is accurately depicted by introducing an angle attenuation conical beam model; in material reflectivity modeling, a bidirectional reflection distribution function system covering Lambert body, specular reflection, transmission, retroreflection and general materials is established; in light path modeling, traditional single reflection hypothesis is broken through, time-resolved Monte Carlo path tracking and kernel density estimation are adopted, simulation of multi-path reflection and transmission effects is achieved, traditional model bottlenecks are broken through, complex optical phenomena are simulated, the generation mode has expandability and controllability, rare key scenes are efficiently synthesized, and the method is suitable for large-scale popularization and application. And the method has wide field extension potential and high technical universality.

A high arch dam concrete temperature control stage temperature state dynamic early warning method based on nuclear density estimation comprises the steps that concrete temperature data are collected, and a temperature time sequence sample is formed; reading temperature time sequence data, extracting a temperature value at each moment, and calculating a temperature change rate to obtain a double-control index sample of a concrete temperature state; establishing a probability density function by adopting a kernel density estimation method; a sensitivity factor is introduced, the bandwidth is automatically adjusted according to the local density of the data, and the sensitivity factor is optimized by using a particle swarm optimization algorithm; establishing a probability distribution model, and determining dynamic threshold upper and lower limits of the concrete temperature and the temperature change rate in combination with an allowable failure probability; and calculating concrete temperature data monitored in real time, and outputting a temperature and temperature change rate result. According to the method, the accuracy and real-time performance of temperature early warning can be effectively improved, the problems that a threshold value is fixed and adaptability is poor in a traditional method are solved, and the comprehensive influence of the temperature level and the temperature change rate on concrete safety can be considered.

The invention discloses an SRAF placement method based on a Bayesian model. The method comprises the steps that historical SRAF configuration parameters and photoetching simulation data are collected and preprocessed; based on the preprocessed data, using kernel density estimation to construct prior probability distribution; constructing a likelihood function of multiple photoetching result indexes in combination with a Hopins photoetching model; calculating approximate distribution of a posterior probability through variation inference by using the Bayesian theorem; and finally selecting an optimal SRAF configuration parameter according to the posterior distribution, and verifying the manufacturing feasibility of the process window and the mask. According to the method, historical prior knowledge and real-time photoetching data are organically integrated through the Bayesian model, and high-precision, high-efficiency and high-robustness optimization of the SRAF layout is realized by combining specific technical characteristics such as preprocessing, kernel density estimation, a Hopkinson physical model and variation approximation; the problems of complex rule base, time-consuming calculation and insufficient adaptability in the prior art are effectively solved.

The present application relates to transmission link early warning technical field, disclose a real-time analysis and early warning system for rotating machine pump vibration data based on edge side calculation, include: capture the physical layer error code time sequence of transmission link and build the differential error code time sequence vector which removes the influence of absolute time drift;A dynamic rotating virtual electric vector space is constructed in the edge computing node, and the gradient ascent algorithm is used to optimize the target functional which measures the phase clustering strength, and the virtual space rotating frequency is adaptively adjusted to lock the best virtual frequency which makes the error code projection most concentrated;Based on the best frequency, the probability density distribution of error code phase is reconstructed by using kernel density estimation, and the generalized Renyi entropy is calculated as the characteristic of transmission error code phase clustering dispersion degree;By comparing the entropy characteristic with the dynamic threshold based on the environmental noise base, the transmission link abnormality is determined.

The application relates to a traffic capacity estimation method based on traffic basic graph parameter calibration, belongs to the technical field of intelligent traffic systems and traffic flow modeling, and particularly relates to a traffic capacity estimation method based on traffic basic graph parameter calibration. The application aims to solve the problem that the existing method cannot obtain accurate traffic basic graph parameters, resulting in low traffic capacity estimation precision. The process is as follows: step one, obtaining historical traffic flow single vehicle data samples of a road section; processing the obtained single vehicle data samples to obtain a final aggregated data set; step two, obtaining adaptive weights based on kernel density estimation based on the obtained final aggregated data set; step three, obtaining an optimal parameter set of a traffic basic graph model by adopting a particle swarm optimization algorithm based on the adaptive weights; and step four, obtaining traffic capacity based on the optimal parameter set of the traffic basic graph model.

The invention relates to the technical field of regional soil pollution health risk assessment, in particular to a regional soil pollution health risk assessment method based on POI data, and the method comprises the steps: firstly obtaining and preprocessing multi-source heterogeneous data, and constructing a POI spatial database; adopting a Kriging spatial interpolation method to generate a pollutant concentration spatial distribution grid map layer; then combining kernel density estimation and dynamic weight correction to construct a population activity intensity index surface; dynamically generating an exposure evaluation unit, and calculating the multi-path daily average exposure dose of people of different ages; and finally, quantifying the multi-path health risk, generating a risk level distribution map, and identifying the dominant POI category of the high-risk area. According to the method, deep fusion of multi-source data is realized, the association between population activity characteristics and pollution exposure is accurately depicted, the evaluation accuracy and suitability are improved, and a scientific decision basis is provided for pollution prevention and control and risk management and control.

The invention relates to the technical field of formation data acquisition, in particular to a logging formation information intelligent analysis system which comprises a response model construction module, a dynamic weight generation module, a composite evidence distribution module and a multi-source information fusion module. According to the method, core calibration data are obtained, a probability density distribution function of basic lithologic logging response is constructed through a kernel density estimation algorithm, dependence on a fixed plate and a static cut-off value is eliminated, dynamic probabilistic description of stratum response characteristics is achieved, and then the initial reliability and sensitivity weight of response numerical values are calculated based on distribution; the method is used for adjusting evidence distribution, then calling an evidence theory combination rule to fuse adjusted multi-source information, determining the stratum state according to the highest cumulative reliability, effectively solving the problem of misjudgment caused by response overlapping, and remarkably improving the accuracy and objectivity of stratum lithology and fluid property identification.

The application provides a GM-AI-PHD multi-target tracking method based on kernel density estimation, the filter uses a kernel density estimation method to estimate an amplitude probability density function of a target and clutter in real time, solves the problem that a GM-AI-PHD multi-target tracking method based on parameter estimation can only be applied to specific scenes, and can realize real-time and accurate positioning and tracking of multi-targets in a complex environment (a large number of clutters, unknown statistical distribution of a real likelihood function).

The present application relates to the technical field of mutation recognition, in particular to a chronic disease sign mutation recognition method and system, comprising the following steps: acquiring data and calculating local trend reversal rate, generating stable domain boundary by using reversal rate amplitude difference value, segmenting according to the boundary and constructing trend amplitude asymmetry feature, calculating trend amplitude ratio and inputting kernel density estimation model to mark mutation candidate points, generating risk fluctuation deviation rate and defining mutation trigger domain based on sliding growth rate. In the present application, the stable domain boundary is generated by calculating the local trend reversal rate to realize dynamic segmentation, the trend amplitude asymmetry feature is constructed, the mutation candidate points are marked in combination with the kernel density distribution, and the trigger domain is defined by using the sliding growth rate, so that the nonlinear fluctuation law and the asymmetric evolution feature of the sign data are effectively quantified, the dependence on artificial threshold is eliminated, and the capture accuracy and state recognition reliability of weak mutation signals of multi-dimensional complex signs are improved.

The invention provides a carbon emission efficiency identification and evaluation method based on cultivated land transformation. The method comprises the following steps: firstly, collecting basic data of multi-source cultivated land transformation and carbon emission, and carrying out standardization processing such as space-time matching and abnormal value correction; constructing an evaluation system of four types of indexes including carbon emission source and sink, transformation characteristics and the like, and determining weights by using an analytic hierarchy process and a variable coefficient combination method; the net carbon emission is calculated through a transformation stage dynamic coefficient method, and a super-efficiency SBM model containing adjusting parameters is constructed to calculate an efficiency value; and generating a comprehensive evaluation result in combination with kernel density estimation and K-means clustering division efficiency levels. The method fits the dynamic characteristics and regional differences of the cultivated land transformation complete period, solves the problems of low distinction degree and insufficient practicability, improves the accuracy and pertinence of carbon emission efficiency identification and evaluation, and provides scientific guidance for cultivated land carbon emission management and control.

A method for generating typical watershed hydro-wind-solar hybrid scenarios based on an improved C-vine Copula model is proposed. First, based on long-term multi-energy complementarity requirements, multi-year runoff data from a power station and power output data from wind and solar power stations within the watershed are selected, outliers are removed, and missing values ​​are filled, completing data preprocessing. Next, runoff and wind / solar output are set as random variables, and nonparametric kernel density estimation is used to obtain the marginal distribution functions of each variable. Then, the improved C-vine Copula model is used to accurately characterize the spatiotemporal correlation between water, wind, and solar resources, deriving the joint probability distribution. Then, Latin hypercube sampling is used to collect uniformly random samples stratified, and K-means clustering is used to generate typical scenarios. Finally, the effectiveness of the scenarios is evaluated from the perspectives of temporal and spatial correlation and randomness. This technology can effectively address the randomness and complexity of water, wind, and solar resources, generating realistic typical scenarios, providing valuable reference for the planning and scheduling of watershed hydro-wind-solar hybrid systems.

A kind of rock burst time series prediction model construction method based on small sample learning, the original microseismic data collected by microseismic sensor is transmitted to the host computer of ground control room, the original microseismic data is standardized by the host computer, then the microseismic data after standardization is processed using principal component analysis method and kernel density estimation method, corresponding two-dimensional time series data is obtained, the correlation between the impending large energy mine seismic event and the past mine seismic event is studied, the density index is constructed, and the data labeling is completed, finally the long short-term neural network based on small sample learning is constructed, the time series data and density index are put into the constructed neural network for training, and the prediction of the time period of future large energy event is completed.The application can reduce the dependence of long short-term memory recurrent neural network on data, improve the generalization of network, so that it can also predict large energy mine earthquake when the amount of data is small, and provide early warning for rock burst.

This application relates to the field of open-pit mining technology, and provides an adaptive open-pit geological spatial grid mapping method and system. In this method, based on the Hough space curve of the geographic coordinates of open-pit geological exploration boreholes, a first kernel density estimation curve for the boreholes is determined. After constructing a linear space for the boreholes based on the first kernel density estimation curve, the geographic coordinates of the boreholes are converted into linear spatial coordinates, and a second kernel density estimation curve for the boreholes is determined based on the linear spatial coordinates. A peak matrix is ​​constructed based on the coordinate axis values ​​corresponding to the peak values ​​of the second kernel density estimation curve, and the adaptive grid position of the boreholes is determined based on the linear spatial coordinates and the peak matrix. This achieves an adaptive open-pit exploration borehole grid and determines the position of each open-pit geological exploration borehole within the grid, providing a new means for three-dimensional geological modeling of open-pit mines and meeting the practical needs of combining open-pit geological exploration with three-dimensional geological modeling.

The present application relates to geodetic and engineering surveying technical field, specifically to a kind of plane coordinate point cloud fitting arbitrary close degree error ellipse in control network design stage.The method includes: the pre-processing of control point coordinate point cloud, removes abnormal value;Accurate spatial precision evaluation density field is constructed to calculate grid point density value;According to the specified confidence, the density threshold value is calculated, the contour is extracted and the error ellipse parameter is fitted.The present application solves the ellipse fitting problem of any specified confidence of non-normal distribution point cloud by adaptive bandwidth kernel density estimation, and significantly improves the reliability of precision evaluation under complex scene.

The application discloses a soft measurement modeling method based on integrated neural network and weighted learning and applied to industrial soft measurement of a urea synthesis process, which comprises the following steps: firstly, weak label data learning is carried out based on a neural network set, a noise data fitting neural network set is trained and constructed, label prediction is carried out on a label noise verification set, and kernel density estimation is carried out by using a prediction value set of the model set; then, the probability density value of the original label in a KDE fitting probability density diagram is calculated, the probability density ratio of the peak value and the original label with noise is calculated, and thus the credibility weight of the data label is obtained; the verification set data is used as training data of a downstream student model, the data label credibility predicted by the upstream model is input into the student model, and the student model is trained; and finally, when the student model is tested, data directly flows into the downstream network, the input of the label credibility value in the feature dimension of the model is all set to 1, and thus the soft measurement prediction of the denoised key quality index of the industrial process is realized.

The present application relates to a kind of multi-agent collaborative induction and conflict solution method and system based on probability density evolution and wasserstein gradient flow.The method is mapped into continuous probability density field by stratified anisotropic kernel density estimation to the discrete position of multi-agent, constructs control free energy functional in probability space, which fuses target attraction, obstacle repulsion, collision avoidance interaction and Fisher information smoothing regularization term;Based on optimal transport and variational method, derive the macroscopic velocity field that enables the fastest decline of energy, and map into individual control instruction that meets the dynamic constraint.The scheme can improve the safety, smoothness and convergence of cluster flight, and is suitable for unmanned aerial vehicle cluster control and urban low-altitude traffic management.

This invention discloses a monitoring method and system for the preparation process of ternary cathode materials based on RVAE. It constructs a nonlinear dynamic system model of the sintering process based on a variational autoencoder; assigns different weights to samples at different times in the constructed nonlinear dynamic system model of the sintering process, derives the loss function of the nonlinear dynamic system model of the sintering process, and trains the model parameters through backpropagation; defines the statistics of the nonlinear dynamic system model of the sintering process based on the cyclic variational autoencoder, and obtains the control threshold of the nonlinear dynamic system model of the sintering process through kernel density estimation; collects online data as a test set for the nonlinear dynamic system model, calculates the monitoring statistics online and compares them with the control limits to determine whether a fault has occurred. This invention can significantly improve the fault detection rate and false alarm rate, providing a strong guarantee for the stable operation of the sintering process.

The invention provides a cultivated land feature extraction method for satellite monitoring pattern spots, a medium and a system, and belongs to the technical field of electrical digital data process.The cultivated land feature extraction method for satellite monitoring pattern spots comprises the steps that firstly, multi-temporal satellite remote sensing data is obtained and preprocessed, then vegetation indexes are calculated, and a time sequence feature matrix is constructed; pattern spot change features are obtained through spatial-temporal scale decomposition, a multi-dimensional feature extraction model is established, accurate recognition of cultivated land pattern spots is achieved, finally, a feature distribution matrix is generated through an evaluation model, and accurate extraction and characterization of cultivated land pattern spot features are completed. According to the method, the boundary and the internal structure of the cultivated land pattern spots are accurately identified by analyzing the time sequence change characteristics of the vegetation indexes and combining spatial autocorrelation analysis. According to the method, the accuracy of feature extraction is improved through kernel density estimation and spatial clustering analysis, and the technical problem that farmland pattern spot features in a multi-temporal remote sensing image are difficult to accurately recognize and extract in the prior art is solved.

The invention discloses an unmanned aerial vehicle route planning method for wharf inspection. The method comprises the following steps: acquiring reference map layer data through hierarchical fusion and verification based on multi-source geographic data; aggregating multi-source data into a data pool by constructing an index and managing a life cycle; analyzing result data in real time through computer vision and streaming processing based on the data pool, wherein the analysis result data at least comprises abnormal events and alarms; based on the analysis result data, generating and obtaining inspection data through a preset template and a natural language, wherein the inspection data comprises an inspection report and an intelligent abstract; acquiring route planning data through kernel density estimation and adaptive sampling based on the event distribution hot spots and violation frequency data of the inspection data; acquiring task data of an air route task based on the air route task of the air route planning data, updating the air route planning data and updating the data pool; and updating the inspection strategy based on the data pool through iteration of the model and the rule.

The application provides a complex curved surface machining tool path adaptive generation method and system, and the method specifically comprises the following steps: obtaining complex curved surface triangular mesh model data, calculating maximum and minimum principal curvatures of each vertex through local quadratic surface fitting; performing kernel density estimation based on a maximum principal curvature value set, and completing initial partition of the vertexes by taking a local minimum value of a probability density function as a threshold; constructing a curved surface graph model, establishing a Markov random field energy function, optimizing the initial partition by minimizing a function, and obtaining a machining partition with smooth boundaries; generating parallel machining tool paths in a smaller curvature area, and generating streamline tool paths along a minimum principal curvature direction field in a larger curvature area; on both sides of a shared boundary between adjacent partitions, a transition area is established by expanding a preset geodesic line distance inward along a normal direction, the direction and row distance of the tool path in the transition area are determined according to a geodesic line distance of a point to the boundary, weighted fusion of tool path generation rule results of both sides of the partition is performed, and smooth transition of parameters is realized.

The invention belongs to the technical field of avionics safety and airspace fine management, and discloses a GNSS interference influence zone division method and system, which uses the quality index and track change characteristics of ADS-B data under GNSS interference to realize the extraction of abnormal data, and then uses a multi-core fusion model to determine an interference influence zone according to the extracted abnormal data; constructing a multi-kernel fusion model by adopting the thought of density estimation weighted summation of a Gaussian kernel and a Laplacian kernel; and the distribution characteristics of the data, the mutual distances between the track points and the grid units and the corresponding quality index information are comprehensively utilized, so that effective division of the interference influence area is realized. The method can be compatible with different airborne hardware versions, and the accuracy of abnormal data extraction is improved; the multi-core fusion model gives consideration to the characteristics of long-distance distribution and local distribution between data, so that the multi-scale characteristics of the data are adaptively captured, and the method has commercial value and practical engineering application value for guaranteeing flight safety.