63 results about "Density based" patented technology

The invention relates to a terrain real scene modeling method, device and equipment and a storage medium thereof. According to the method, multi-source heterogeneous data related to the terrain in a target area is integrated, a multi-modal data cube is generated through noise filtering and data fusion under a unified coordinate system, then a data cavity area is identified based on density clustering and elevation gradient analysis, and geophysical features are extracted. Innovatively constructing a neural implicit inference model fused with physical constraints to perform joint modeling on the earth surface and underground structures, and finally converting implicit terrain representation into an explicit three-dimensional terrain model; seamless fusion modeling of the earth surface and the underground space under the complex shielding environment is achieved, the physical rationality of the model and the hidden area reconstruction precision are remarkably improved, meanwhile, it is ensured that the generated model strictly conforms to the geological physical law, and the geological engineering safety and the planning reliability are effectively guaranteed.

The invention discloses a target detection method and system based on a millimeter wave radar. The target detection method and system are used for realizing accurate detection, positioning and dynamic and static recognition of multiple targets under a complex background. According to the method, a distance-Doppler spectrogram is generated through the technical means of sliding window construction, spectral analysis, clutter suppression and the like, and candidate target points are detected by adopting an SO-CFAR algorithm. Then, determining a target position through high-resolution direction estimation and coordinate transformation, performing spatial clustering in combination with a density-based DBSCAN algorithm, and extracting a target geometric center and a bounding box; in the aspect of target tracking, Kalman filtering is used for predicting and updating the position and speed of the target, and a beam forming technology is used for enhancing a target signal, so that the target recognition stability is improved. And finally, the system performs robust dynamic and static state recognition on the target through a dynamic and static judgment module, so that high precision and robustness of the target detection process are ensured. The method can effectively cope with static background interference and dynamic target changes, and is suitable for target detection and tracking in a complex environment.

The invention discloses a wind and light output scene generation method based on depth feature mining and adaptive clustering, and the method comprises the steps: cleaning wind and light output data, carrying out the normalization processing of the data, and enabling the data to be mapped to a preset interval, so as to eliminate the dimension influence; constructing a deep convolutional feature extraction network to extract a corresponding feature map from the normalized wind and light output data; a K-Means + + algorithm is adopted to initialize a clustering center, an improved ISODATA clustering algorithm is executed based on a density threshold dynamic splitting mechanism, clustering parameters are optimized through Bayesian optimization, and a typical scene is generated. The accuracy of the wind and light output scene is remarkably improved.

The invention provides a trajectory clustering method based on space-time weighting and a density peak value, and relates to the technical field of trajectory clustering. The method provided by the invention comprises the following steps: based on a minimum description length criterion, fusing an adaptive space-time weight and a space-time geometric distance to segment trajectory data to obtain sub-trajectory segments; calculating a space-time local density, a relative distance and a decision value of each sub-track segment based on a density peak clustering algorithm; performing noise identification on the sub-track segments based on the average space-time geometric distance and the average local space-time density, iteratively selecting a class cluster center based on the decision values of the non-noise sub-track segments and the time-space factor decision values, and generating a class cluster set; and determining a candidate representative trajectory set from the class cluster set based on the spatio-temporal local density and the maximum density, extracting continuous sub-trajectory segments from the candidate representative trajectory set based on spatio-temporal continuity constraints, and sequentially connecting end points of the continuous sub-trajectory segments to generate a representative trajectory. According to the method, the trajectory clustering effect is improved through space-time weighted segmentation and density peak trajectory clustering.

The invention relates to the technical field of protein prediction, in particular to a protein binding site prediction method based on geometric deep learning. According to the method, protein geometric description and a corresponding geometric diagram learning scheme are introduced, and a feasible way is provided for modeling three-dimensional structural features; in addition, the invention also provides an RSA-guided two-stage hybrid transfer learning strategy, firstly, a model is trained on a larger data set to learn general representation, and then fine tuning is carried out for binding site prediction, so that the overfitting problem in limited data training is relieved; according to the invention, a prediction post-processing module and an integrated learning module based on density clustering are designed, and the high variance of the model in multiple times of training operation is significantly reduced.

The embodiment of the invention provides a point cloud denoising method and device, a medium and a product, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring target point cloud data subjected to abnormal point elimination; according to the target point cloud data, constructing a density-based hierarchical clustering algorithm and a de-noising network of an operation selection strategy, and generating a de-noising model based on the de-noising network; and inputting the to-be-denoised point cloud data into the denoising model, and outputting the denoised point cloud data. According to the scheme, isolated noise possibly misleading path decision is filtered in advance, and interference is cleared for follow-up path selection; a denoising network based on a density hierarchical clustering algorithm and an operation selection strategy is constructed, a point cloud structure is accurately divided, representative elite points are screened in combination with the operation selection strategy, under extreme conditions, the elite points are preferentially used as core extension paths, noise point dominant decision making is avoided, and the accuracy of the system is improved. The problem that in the prior art, a single path is poor in adaptability in an extreme scene is solved, and the denoising accuracy is improved.

The invention discloses a shallow sea area water depth inversion method based on a satellite-borne laser radar, and particularly relates to the technical field of single-photon water depth information extraction. The denoising method comprises the following steps: S1, data preprocessing: reading data, carrying out visual presentation, dividing photons into offshore photons, sea surface photons and undersea photons, and intercepting an effective photon region; s2, coarse denoising: denoising the area intercepted in the step S1 by adopting a DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm; s3, fine denoising: screening effective signals of sea surface photons by adopting a Gaussian fitting algorithm, removing sea photons, separating subsea photons from the sea surface photons, performing density analysis on the subsea photons, extracting a density peak point, and constructing a density trend curve of the subsea photons by adopting a cubic spline interpolation method, eliminating residual noise deviating from normal distribution according to the trend curve; and S4, performing water depth inversion: performing refraction correction on the subsea photons subjected to fine denoising, fusing the corrected subsea photons and the sea surface photons subjected to fine denoising, and generating a water depth distribution diagram of the shallow sea area.

Disclosed in the present invention is a three-dimensional circle detection method for a freeform surface, comprising: acquiring freeform surface point cloud data, and using a normal vector-guided rolling ball method to extract a freeform surface circular-hole point cloud boundary contour from the point cloud data; performing Euclidean clustering-based boundary segmentation on the point cloud boundary contour to obtain a clustering result of all boundary point clouds, the clustering result consisting of a plurality of circular-hole contours; performing a density-based weighted iterative algorithm on each circular-hole contour to obtain a freeform surface circular-hole normal vector; and projecting a freeform surface circular hole along the freeform surface circular-hole normal vector onto a plane, and then performing circular-hole iterative fitting based on solving an overdetermined equation on the projected circular-hole point cloud to obtain final positioning information. The present invention is applicable to circular-hole positioning on freeform surfaces of different curvatures, has strong robustness, and effectively improves the accuracy of circular-hole positioning on the freeform surfaces.

The invention discloses a method and device for constructing a three-dimensional fault plane according to an earthquake catalog, and the method comprises the steps: extracting the position information of an earthquake source according to the earthquake catalog of a target region, obtaining the density information of each data point, and generating a density map; based on the density information and the density map, utilizing an OPTICS algorithm to carry out preliminary clustering analysis on the seismic source position information, outputting an accessibility map, and determining a clustering number N; calculating the local density and the center offset distance of each seismic source point based on the Euclidean distance and the truncation distance between every two seismic source points, generating a decision diagram, and selecting the first N seismic source points with the maximum product of the local density and the center offset distance as a clustering center; and PCA analysis and PCA plane determination are carried out on the DPC classification result, and plane features of the fault plane are obtained when set conditions are satisfied. According to the method, an OPTICS algorithm, a DPC algorithm and PCA analysis are combined, and end-to-end parameterization-free three-dimensional fault plane modeling is achieved only by means of seismic source position information.

The invention discloses an industrial key point data cleaning and abnormal point removing method and a related device, and the method comprises the steps: obtaining to-be-processed industrial key point coordinate data, and carrying out the preprocessing of the coordinate data; dynamically determining the optimal clustering number of the preprocessed coordinate data; based on the optimal clustering number, clustering the coordinate data by adopting a first clustering algorithm, and identifying a first type of abnormal points according to distance distribution statistical characteristics in each cluster; performing secondary cleaning on the data from which the first type of abnormal points are removed by adopting a density-based second clustering algorithm so as to identify a second type of abnormal points which are judged as noise points; and integrating the first type of abnormal points and the second type of abnormal points to generate a cleaned data set. According to the method, dynamic clustering, anomaly detection based on intra-cluster statistics and density secondary cleaning are combined, so that the adaptive capacity, accuracy and robustness of the data cleaning process are enhanced, and a purer and more reliable data set can be produced.

The invention provides a three-dimensional steel structure full-coverage path planning inspection method based on topology reconstruction, and relates to the technical field of industrial robots, and the method comprises the steps: obtaining a model file of a to-be-inspected three-dimensional steel structure; constructing an original vertex set and an original edge set according to the model file; performing adaptive clustering on each original vertex in the original vertex set through a density-based spatial clustering algorithm to obtain a cluster; taking the geometric center of the original vertex in each cluster as a topological node, and establishing a mapping relation between the original vertex and the topological node; carrying out topology reconstruction on the original edge set, and constructing an undirected weighted graph; stitching non-connected sub-graphs in the undirected weighted graph through a K-dimensional tree and a minimum spanning tree algorithm to obtain a fully connected graph; generating a full-coverage continuous path sequence through a Chinese postman algorithm; the full-coverage continuous path sequence is converted into a three-dimensional space coordinate sequence, and the three-dimensional steel structure to be inspected is inspected according to the three-dimensional space coordinate sequence.

The invention provides a logistics site selection method and system based on combination of density peak clustering and a Gaussian model, and belongs to the field of logistics planning and data analysis. The method comprises the steps of collecting multi-dimensional data in logistics site selection, and performing preprocessing; carrying out clustering analysis on the preprocessed multi-dimensional data by adopting an information entropy-based density peak value clustering algorithm and a Gaussian mixture model combined clustering algorithm, and extracting a clustering center of the data as a potential logistics site selection point; and according to a preset logistics site selection target, constructing a target function and setting a constraint condition, and solving an optimal logistics site selection point from the potential logistics site selection points through an optimization algorithm. Therefore, the internal structure and rule behind the data are disclosed. The limitation that a traditional method depends on a single factor is avoided, the objectivity and reliability of the site selection scheme are improved, the site selection scheme can be rapidly adjusted through data updating and re-clustering, and the optimality is kept.

The invention discloses an electric power spot market price abnormity early warning method and device, equipment and a medium, and the method comprises the steps: firstly obtaining historical price data and first multiple space-time sensitive factor data, and generating second multiple space-time sensitive factor data of a future time point through a pre-trained sensitive factor prediction model; inputting the second data into a market price situation awareness model, and predicting price data at each time point in the future; and finally, carrying out clustering analysis on the historical and predicted price data by adopting a density-based spatial clustering algorithm, identifying abnormal price data and outputting early warning information. According to the method and the system, dual modeling of multi-time-space sensitive factor prediction and market price situation awareness is fused, and the clustering algorithm is combined to intelligently identify the abnormal price, so that the adaptability of the early warning system to the complex environment change of the power market is effectively improved, and the accuracy is better. The method can be widely applied to the technical field of new energy.

The invention provides a micro-motion H / V spectrum ratio data processing method and device based on machine learning and a medium, and the method can comprise the steps: obtaining micro-motion H / V spectrum ratio data containing a plurality of curves, recognizing and removing the curves containing abnormal morphological characteristics through a preset curve removing model, and obtaining a candidate data set; classifying peak values of the curves in the candidate data set according to a preset peak value classification model to obtain an effective peak value set; clustering the effective peak value set based on a density clustering algorithm to obtain effective peak value clusters in different frequency intervals; and determining the effective peak cluster with the lowest frequency as a main peak cluster, and rejecting curves of peak values with abnormal frequencies and amplitudes in the main peak cluster to obtain processed micro-motion H / V spectrum ratio data. The method has higher stability and accuracy, the spectrum distribution of the processed curve is more concentrated, the standard deviation curve is more convergent, and the peak value is clearer and more stable.

A transformer partial discharge positioning detection method based on ultrahigh frequency signals is characterized by comprising the following steps: S1, estimating time delay of signals obtained by an ultrahigh frequency (UHF) sensor array based on a generalized cross-correlation method; and S2, arranging and combining signals of the UHF sensor array, and solving coordinates of a positioning point based on an improved multi-moth optimization (MFO) algorithm according to the time delay obtained in the step S1. And S3, clustering the coordinates of the plurality of positioning points obtained in the step S2 based on a density clustering algorithm (DBSCAN), and selecting the geometric center position of the category with the maximum sample number as the final local discharge source coordinate.

Methods for detecting power system events in an electrical power system. An example method comprises determining a signal envelope for each of at least first and second power system signals and performing density-based spatial clustering of a series of points formed by combining respective values of the signal envelopes for at least the first and second power system signals. The example method further comprises detecting one or more power system events by identifying outlier points or groups of points in the spatial clustering. Some methods may further comprise automatically classifying power system events by collecting a data set comprising, for each detected power system event, power system signal features corresponding to the detected power system event, and classifying each of the detected power system events using the data set and a machine-learning-based classification algorithm, where said classifying comprises determining a classification label from among two or more predetermined classification labels.

According to an aspect, there is provided a computer-implemented method comprising the following. Initially, information on a plurality of anomaly events relating to operation of a target system is obtained. The information comprises one or more time series of anomaly score data. Segments satisfying one or more pre-defined criteria for anomalous operation are detected from the one or more time series. The one or more pre-defined criteria are defined to exclude fully non-anomalous anomaly score data. Anomaly durations and standardized anomaly scores are determined for the segments. Partition or density based clustering is performed in a two-dimensional space formed by the standardized anomaly scores and the anomaly durations to form n clusters, and m smallest clusters of the n clusters are identified. At least one of the following is performed: outputting information on the m smallest clusters or causing adjusting of operation of the target system based on the m smallest clusters.

Techniques are described for providing a ML data analytics application including guided ML workflows that facilitate the end-to-end training and use of various types of ML models, where such guided workflows may also be referred to as ML “experiments.” For example, the ML data analytics application may enable users to create experiments related to prediction of numeric fields (for example, using linear regression techniques), predicting categorical fields (for example, using logistic regression), detecting numerical outliers (for example, using various distribution statistics), detecting categorical outliers (for example, using probabilistic statistics), forecasting time series data, and clustering numeric events (for example, using k-means, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, or other techniques), among other possible uses of various types of ML models to analyze data.

The invention belongs to the technical field of disaster recognition, and particularly relates to a density-based parameter adaptive movable landslide space clustering recognition method. The method comprises the steps of firstly obtaining deformation field data of a to-be-detected area through an InSAR technology, then obtaining gradient matrixes of an input image in eight directions through an edge detection algorithm on the basis of an InSAR deformation rate result, removing isolated pixels in the gradient matrixes through Gaussian filtering, calculating a mean value and a standard deviation of the gradient matrixes, and finally obtaining the deformation field data of the to-be-detected area according to the mean value and the standard deviation. Taking the maximum value of the two as a mask threshold value of the input image to obtain a refined deformation rate field; secondly, a fine deformation rate field is used for positioning motion pixels which are possibly movable landslides; after all moving pixels possibly being movable landslides are positioned, a snow ablation optimizer is introduced to optimize core parameters of the DBSCAN, a parameter-adaptive SAO-DBSCAN spatial clustering method is formed to automatically cluster the moving pixels into landslide masses, and the landslide automatic clustering performance and precision are greatly improved.

The application discloses a data clustering method and device based on density peaks, a medium, and direct subordination is used to describe the relative density of each data point, wherein the data point is a picture or a word feature in a data set, a mixed density value combining the relative density and the absolute density is designed to identify a clustering center, and the effective subordination is defined through similarity reachability, main distribution of non-center points is performed, and the label of a remaining point is determined in combination with the k-layer upper distribution of different clusters, so that the data points of the same category in the data set can be effectively gathered in one category, and the classification of the data set is efficiently and accurately completed.

The invention discloses a three-dimensional object collision body optimization method based on a density clustering algorithm, and the method comprises the steps: S1, extracting a position representation feature containing position information of each primitive on the surface of a three-dimensional object, and extracting N types of additional geometric features of each primitive on the surface of the three-dimensional object, jointly constructing feature description of the surface of the three-dimensional object through the extracted position characterization features and the N additional geometric features; s2, a density clustering algorithm is adopted, distance measurement and similarity measurement between the primitives are evaluated based on the feature description constructed in the step S1, and the primitives with the distance measurement smaller than a first preset threshold value theta1 and the similarity measurement smaller than a second preset threshold value theta2 are divided into the same cluster. According to the method, the hierarchical bounding box structure with the local collision body as the leaf node can be constructed, the local geometric features of the three-dimensional object can be reserved, and the fitting precision of the three-dimensional object can be improved, so that misjudgment collision can be avoided when the hierarchical bounding box structure is used for collision detection.

The invention provides a dynamic anchor position detection method based on density clustering algorithm optimization. The method comprises the following steps: data cleaning: decoding, abnormal value filtering and standardization processing are carried out on ship AIS data; dBSCAN track point clustering: identifying a ship anchoring and gathering area based on a density clustering algorithm; post-processing a clustering result: filtering noise points, and selecting a cluster containing the most data points as an anchor area; and anchor location point positioning: calculating a coordinate mean value of all points in the anchor location area, and outputting a real anchor location point through anti-standardization. Intelligent transition from a fuzzy'anchoring area 'to an accurate'physical anchoring position point' is realized, the anchoring position can be positioned more accurately, environmental change can be adapted more dynamically, data noise can be resisted effectively, and a real and reliable technical basis is provided for intelligent anchoring monitoring, collision risk early warning and fine management of an anchoring ground.

This invention belongs to the field of robotic arm motion planning and control technology, and discloses a control method for a harvesting robotic arm based on density clustering and agent integration. First, the DBSCAN algorithm is used to simplify the harvesting scenario, determining the objective function, constraints, input state space, action space, reward function, and termination condition of the agent in the harvesting scenario model. Based on the clustering results of the DBSCAN algorithm, the agent is prepared for training. Then, the selected agent algorithm is trained. Finally, an error reciprocal weighted combination method based on agent reward is used to integrate the motion control decisions of each trained agent algorithm for the same robotic arm and accumulate the total reward value after integration processing. This invention is applicable to the control of harvesting robotic arms, realizes multi-angle decision-making, effectively compensates for the shortcomings of single algorithms, improves the accuracy of robotic arm motion, and thus provides reliable support for modern agricultural automation technology.

The invention discloses an ecological product value evaluation method, and relates to the technical field of urban traffic, and the method comprises the steps: 1, converting remote sensing image land classification data into vector space point data, and recording the vector space point data as a data set A; step 2, spatial clustering processing is performed on the data set A based on a DBSCAN density clustering algorithm to obtain a DBSCAN density clustering result, convex hull and concave hull processing is performed on the DBSCAN density clustering result to obtain a minimum convex hull and a minimum concave hull of each category, all the minimum convex hulls form a first clustering result s, and all the minimum concave hulls form a second clustering result S1; 3, performing index calculation on the first clustering result S in combination with the second clustering result S1; step 4, performing clustering processing on the index calculation result I of the S to obtain a result C; and step 5, carrying out classification spatial distribution visualization processing on S according to C. According to the method, the value of the ecological product can be evaluated by constructing the spatial form and the population analysis index of the ecological product.

The present invention relates to artificial intelligence and provides a density radius-based clustering method, system, device, and storage medium. The method comprises: obtaining a sample data set, first cluster quantity data, and a cluster set, wherein the sample data set includes multiple cluster data; calculating the distance between any two cluster data to obtain multiple adjacent distance data; calculating density radius data on the adjacent distance data based on first sorting information and the first cluster quantity data; performing clustering processing with each cluster data as the center based on the density radius data and the adjacent distance data to obtain multiple clusters; when a cluster cluster meets a preset deduplication joining condition, adding the cluster cluster to the cluster set; and when the cluster set meets a preset cluster termination condition, outputting the cluster set. The present invention can automatically calculate the density radius for cluster data with different shapes, realize multi-mapping of cluster data in the cluster cluster, and improve the clustering effect.

A method for determining the association and continuity combination of top-side-rock structural surfaces based on unsupervised learning is proposed. This method involves investigating the structural surfaces of the roof and side-rock masses in underground engineering projects to obtain geometric and statistical information. A unified encoding is performed to construct a joint feature set of roof-side-rock structural surfaces. An unsupervised density-based clustering algorithm is used for initial cluster analysis of the joint feature set. A swarm intelligence-based Lüperfox optimization algorithm is introduced to optimize key parameters of the DBSCAN clustering algorithm. A Latin hypercube sampling method is used to improve the population initialization process of the Lüperfox optimization algorithm, resulting in better DBSCAN hyperparameters and optimized structural surface clustering results. The method determines the structural surface system affiliation of structural surfaces in different locations, quantitatively calculates the spatial continuity and combination relationship of structural surfaces, and outputs the spatial association determination results and continuity levels of the roof-side-rock structural surfaces. This method enables intelligent identification and quantitative analysis of the spatial relationship between roof and side-rock rock structural surfaces.

The present application relates to wine grape thinning technical field, specifically, it is a kind of wine grape thinning device and method based on machine vision, method includes the following steps: step S1: multimodal image acquisition and adaptive enhancement preprocessing;Step S2: accurate segmentation of cluster and peduncle based on improved loss function;Step S3: dynamic decision of peduncle clamping point based on mechanical optimal model;Step S4: thinning intensity decision based on density clustering and random forest regression;Step S5: trajectory planning of mechanical arm and mechanical claw collaborative pruning;Step S6: thinning effect verification and closed-loop correction.The present application can preferably carry out wine grape thinning.

The invention discloses a three-dimensional point cloud-based adaptive plane stacked object segmentation method and system, and the method comprises the following steps: obtaining three-dimensional point cloud data of a to-be-processed scene, and carrying out the preprocessing of the three-dimensional point cloud data; performing space cutting on the preprocessed three-dimensional point cloud data, and executing adaptive voxel downsampling according to point cloud space distribution characteristics to form a point cloud data set with balanced density; performing plane fitting on the point cloud data set based on a random consistency sampling algorithm, identifying and separating point clouds meeting ground judgment conditions as ground point clouds, and obtaining non-ground point clouds at the same time; constructing a spatial index structure for the non-ground point cloud, and performing main clustering processing based on a density constraint clustering algorithm to obtain a plurality of initial clusters; and calculating the height distribution characteristic of each initial cluster, and executing secondary clustering processing on the corresponding initial cluster when the height difference meets a preset trigger condition. According to the invention, through height feature analysis and a secondary clustering mechanism, automatic and accurate segmentation of stacked objects is realized.

Groups of patrons may be discovered by measuring website and mobile site patron clickstream data in a mathematical and unsupervised way over a predetermined time and by graphically clustering the patron clickstream data using non-linear dimensionality reduction in the form of a Uniform Manifold Approximation and Projection algorithm (UMAP). The data from the UMAP may then be feed into a Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) in order to identify a center of each cluster. Next, using the data from the UMAP and the center of each cluster from the DBSCAN, a K-Nearest Neighbor algorithm (KNN) may be applied to identify data points closest to the center of each cluster and to shade each of the data points to graphically identify each cluster of the plurality of clusters. Next, illustrate a graph on the display representative of the data points shaded following application of the KNN.

The application discloses an intelligent division method for police case high-occurrence areas based on density clustering and k-means clustering, and the method comprises the following steps: obtaining police case data of a city; preprocessing original police case data to obtain data after unification and standardization; clustering the data after preprocessing by using a density algorithm to obtain all categories and corresponding data of each category; clustering the police case data of all categories by using a k-means algorithm; then clustering all police cases of each police case data by using the density algorithm to obtain all categories and corresponding data of each category; extracting boundary points of each category data to obtain corresponding boundary contours, that is, to obtain police case high-occurrence areas. The application can accurately identify police case high-occurrence areas, and can effectively balance the uneven distribution of police case high-occurrence areas in urban areas and suburban areas, thereby providing effective data support for subsequent police deployment, unified dispatching and command.