1309 results about "Cluster result" patented technology

The invention relates to the technical field of intelligent driving, and particularly provides a personalized driving behavior identification method based on deep embedded clustering. Extracting a low-dimensional depth feature vector of the driving time sequence segment through a deep neural network auto-encoder, performing end-to-end joint optimization on the auto-encoder and unsupervised clustering by using a deep embedded clustering method, generating a driving style clustering result, and labeling a clustering result label; freezing the trained auto-encoder parameters, constructing a driving style classification model, and establishing a mapping relation between driving time sequence fragments and style labels; and distributing semantic tags for clustering results by extracting interpretable features. According to the end-to-end joint optimization technology based on deep embedded clustering, the convergence speed and generalization ability of the model are remarkably improved, and the driving style identification precision is far higher than that of a traditional machine learning method.

The invention relates to the technical field of literature processing, and discloses a literature review generation method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a research theme input by a user, and forming a reference literature set; according to the number of literatures in the reference literature set, a clustering strategy is selected, a structured review is generated based on a clustering result, and generation of the structured review comprises generation of alternative titles, construction of a multi-level outline, generation of research status paragraphs according to the outline and generation of brief reviews; receiving editing operation of a user on the outline, the research status paragraph and the brief review in the process of generating the structured review, and performing quality evaluation and optimization processing on the research status paragraph and the brief review; and when the current paragraph is researched to be generated, the reference literature is matched and the reference mark is inserted, a standard reference literature list is generated, and a traceability link from the in-text reference to the original literature is established. According to the method, literature review with a rigorous structure and coherent logic can be generated, and disordered stacking of information is avoided.

The invention relates to a collision early warning method based on a laser radar in a non-structural environment, belongs to the technical field of vehicle driving safety, and solves the technical problem of low accuracy and effectiveness of collision early warning in the non-structural environment. Comprising the following steps: in a vehicle body coordinate system, collecting point cloud data in a non-structural environment by using a laser radar, estimating a vehicle motion pose by using the point cloud data, and obtaining a real-time pose and speed of a vehicle; generating a vehicle motion prediction trajectory based on the motion pose and speed of the vehicle, and performing ground point cloud filtering and obstacle clustering detection on the point cloud data in the vehicle prediction driving area to obtain an obstacle clustering result; carrying out obstacle geometric collision check based on an obstacle clustering result, determining an obstacle vertex closest to the vehicle in a vehicle prediction trajectory range area, estimating the movement speed of the obstacle vertex, and calculating an RSS distance; if the RSS distance is smaller than the preset safety distance threshold value, collision early warning is triggered, and accurate early warning in the non-structural environment is achieved.

The invention relates to a multi-modal data fusion method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: respectively carrying out feature extraction on image input data from at least two modes to obtain an image feature vector of each mode; on the basis of the image feature vector of each mode, Dirichlet distribution parameters corresponding to each mode are generated, the distribution difference between the modes is evaluated, and the Dirichlet distribution parameters are used for representing the evidence intensity of each category in the classification or clustering task; according to the distribution difference, fusing the Dirichlet distribution parameters of each mode to obtain a basic probability distribution result after fusion; and dynamically adjusting the basic probability distribution result by using a Kalman filter, and outputting a final classification or clustering result. By adopting the method, the fusion weight of each modal data can be dynamically adjusted, and the accuracy and reliability of a multi-modal fusion system are improved.

The invention provides an information query method and device based on a large language model. The method comprises the steps that query information input by a user is received; determining a target context fragment related to the query information based on the semantic similarity between historical context fragments associated with the user in the large language model stored in a database and the query information and a predetermined comprehensive importance score of each historical context fragment; wherein the comprehensive importance score of each historical context fragment is determined on the basis of a clustering result and the generation time of each historical context fragment after the historical context fragments are clustered; and fusing the query information and the target context fragment, inputting the fused information into the large language model, and determining a query result corresponding to the query information. Through the method, the context reasoning capability of the large language model can be improved, and the query result can be more accurately generated for the user.

The embodiment of the invention provides a voltage instability risk assessment and high-risk scene screening method and related equipment, and belongs to the field of power systems and automation thereof. The method comprises the following steps: extracting a voltage distribution balance degree according to a spatial distribution characteristic of a sample; constructing a voltage instability risk assessment index in combination with the voltage distribution balance degree, the new energy transmission margin ratio and the voltage out-of-limit risk factor; according to the voltage instability risk assessment index, abnormal samples in the sample set are screened through an isolation forest algorithm; performing K-means clustering analysis on the screened abnormal samples to subdivide the candidate scene into subclass clusters with clear risk characteristics; performing class cluster risk assessment according to a clustering result to obtain a class cluster comprehensive score, and dividing class clusters into different risk levels; through dispersion analysis, a centralized extreme scene and a discrete extreme scene are distinguished, and class cluster center and boundary scene screening is carried out respectively. According to the invention, efficient and comprehensive high-risk scene screening can be realized.

The invention provides a method for identifying a microscopic association body structure in a heavy oil molecule simulation trajectory, which comprises the following steps of: analyzing a molecular dynamics trajectory, identifying an aromatic ring structure in a heavy oil molecule component, calculating a geometric center of the aromatic ring structure, combining a periodic boundary condition, and applying an improved DBSCAN clustering algorithm to realize automatic identification and clustering of an association body; and performing post-processing optimization on the clustering result, merging the shared clusters to generate a final association body set, and analyzing the number, size distribution, morphological characteristics and dynamic evolution information of the association bodies based on the structural characteristics of the final association body set. The method can accurately capture pi-pi accumulation behaviors of heavy oil molecules, further reveals morphological characteristics and dynamic evolution laws of heavy oil microassociates and influences of the heavy oil microassociates on the apparent viscosity of heavy oil, provides theoretical support for heavy oil viscosity-causing mechanism analysis and viscosity reducer design, can complement experimental research, and has a wide application prospect. And efficient development and green utilization of a complex thickened oil system are promoted.

The invention belongs to the technical field of enterprise information technology resource management, and provides a tool performance evaluation method and device, and the method comprises the steps: carrying out the data preprocessing of a pre-constructed tool performance evaluation index pool matrix, and outputting a standardized matrix; n is the number of tools, and p is the number of selected indexes; performing PCA dimension reduction operation on the standardized matrix to obtain a principal component score matrix and a load matrix; d is the dimension after dimension reduction; performing k-means clustering on the principal component score matrix and the load matrix, outputting cluster division, and generating a clustering label for each cluster; and on the basis of an AHP analytic hierarchy process, tools in each cluster are sorted respectively. According to the method, the tools are grouped based on the clustering result through a clustering independent ranking mechanism, so that the intra-group homogeneity and inter-group difference of the tools are obvious, misjudgment of heterogeneous tools, monopoly sorting of dominant clusters and mismatching of resources are avoided, and the global ranking stability is improved.

The invention belongs to the technical field of electronic search, and relates to a radio reconnaissance method and system based on multichannel parallel processing and intelligent clustering, and the method comprises the steps: carrying out the multi-phase filtering digital channelization processing of a broadband radio signal, so as to obtain adjacent channel sequences with overlapped frequency bands; performing square law detection and constant false alarm detection on each channel sequence to generate an initial pulse description word; establishing a candidate corresponding relationship in adjacent channels based on arrival time similarity and noise consistency, and determining a cross-channel merging relationship according to frequency range continuity and frequency-time relationship slope consistency to generate a complete pulse description word; performing hierarchical clustering sorting based on the arrival angle and the pulse width; and performing multi-stage differential analysis on a clustering result to extract a repetition frequency sequence and determine a repetition frequency type. According to the technical scheme, the stability and consistency of cross-channel pulse merging and sorting results can be kept under the broadband high-density condition, and the radio search processing precision under the complex electromagnetic environment is improved.

The invention relates to the technical field of air conditioner load prediction, and provides an air conditioner load prediction method and system, and the method comprises the steps: extracting intra-day meteorological data features and intra-day air conditioner load data features, and forming multi-dimensional data features; performing dimension reduction processing on the multi-dimensional data features to form a comprehensive feature curve; clustering the comprehensive characteristic curve, dividing the air conditioner load data in the historical day into a data set according to a clustering result, and dividing the data set into a training set and a test set; a plurality of prediction models corresponding to different meteorological scenes are constructed, the prediction models are trained through the corresponding training sets, hyper-parameter tuning is conducted on the prediction models through an improved sodat swarm optimization algorithm, and a plurality of air conditioner load prediction models are formed; and inputting the test set corresponding to various meteorological scenes into the corresponding air conditioner load prediction model, and outputting an air conditioner load prediction result. According to the invention, air conditioner load curves with obvious boundaries in different meteorological scenes can be effectively separated, and air conditioner power load prediction errors in extreme weather are reduced.

The invention belongs to the technical field of computer big data, and discloses a relation graph construction and layout method, device and system based on spectral clustering and a storage medium, a clustering center is initialized through a genetic algorithm, the clustering center serves as genetic information and is coded into a character string, the operation time can be shortened, and the classification precision can be improved; furthermore, a weighted Euclidean distance is constructed as a distance function of a K-means algorithm, mutual relation weighting between the features can be reflected, features of different weights are counted into the distance, the classification precision can be effectively improved, the loss is reduced, and the classification efficiency is improved. According to the method, an initial similarity matrix, obtained through a traditional similarity calculation method, between XML documents is corrected through an affinity propagation algorithm, the similarity between the hidden similar XML documents can be reflected, on the basis, the correct clustering number and the correct clustering result are obtained by applying a multi-path spectral clustering method NJW, the method is irrelevant to the sequence of the XML documents, and the method has the advantages of being high in practicability and easy to popularize. The method is suitable for clustering the retrieval results of the XML documents arranged in any sequence.

The invention discloses an expressway traffic flow prediction method based on a spatial-temporal clustering generative adversarial network under an emergency, belongs to the field of expressway traffic flow prediction, and remarkably improves the precision of expressway traffic flow prediction under the emergency. The method comprises the following steps: step 1, acquiring traffic flow data from a traffic detection system, and constructing a traffic network graph structure; 2, integrating geographical location information, traffic function similarity and delay propagation characteristics to construct a comprehensive clustering algorithm, and realizing the comprehensive clustering algorithm; 3, designing a generator network based on a space-time attention encoder and a discriminator network structure fusing a bidirectional long-short-term memory network and graph convolution based on the clustering result and the feature representation in the step 2, and constructing a complete prediction model; 4, performing prediction model training based on the composite loss function; and step 5, applying the trained prediction model to real-time data prediction to realize traffic flow prediction under emergencies.

The invention discloses an unknown attack detection method and system for intelligent network security situation awareness, and the method comprises the steps: obtaining multi-dimensional data of a power grid monitoring system, carrying out the data preprocessing, carrying out the network attack preliminary detection of the multi-dimensional data through a pre-built network security situation awareness framework, and outputting suspected attack data, the method comprises the steps of performing data training on historical attack data through an improved OCN open set classification network, identifying attack features of each known attack type, performing semantic similarity calculation on the attack features and suspected attack data, performing attack feature mapping and clustering on the suspected attack data based on semantic similarity, and obtaining an unknown attack feature clustering result. And according to the unknown attack clustering result, carrying out attack type classification on unknown attacks in the suspected attack data to obtain an unknown attack detection result. The method has the effects of detecting unknown attack means in time, effectively reducing the risk that the power grid system suffers from network attacks and guaranteeing safe and stable operation of the power grid system.

The invention discloses a microbe grouping analysis method and related equipment. The method comprises the following steps: acquiring microbe data of a target research area; based on microorganism abundance data, a conversion value of each sample is obtained through center logarithm ratio conversion processing, and then a distance value between any samples is obtained through Euclidean distance quantization; adaptively setting a minimum sample number and a neighborhood radius based on a sample number and a species number corresponding to the microbial data; performing density clustering on samples in the microbial data to obtain a clustering result, and further marking the sample type of each sample in combination with the minimum sample number and the neighborhood radius; performing multi-level analysis on the clustering result based on the sample type to obtain an analysis result of the clustering result; and performing correlation analysis based on the analysis result and the methane leakage data to obtain a microbe grouping analysis result of the target research area. The method realizes standardization and automation of the analysis process, can remarkably improve the analysis efficiency, and can be widely applied to the technical field of data processing.

The invention provides a composite material damage type identification method based on multiple characteristic parameters of an acoustic emission signal, which comprises the following steps: designing and carrying out an acoustic emission signal acquisition test aiming at a composite material damage evolution behavior; key acoustic emission signal features are screened out based on a principal component analysis method, and the corresponding relation between a clustering result and a damage form is verified; a transient damage contribution index is proposed based on a clustering result to represent the contribution degree of each damage to the overall damage at a certain moment, and a damage function is proposed in combination with strain energy. The method effectively solves the bottleneck problems of high feature redundancy and serious mode confusion of the traditional acoustic emission signal, perfects the spatial correlation model of the energy release rate and the damage mode in the composite material progressive damage theory, provides an experimental basis for establishing an anisotropic damage constitutive equation considering the laying layer direction, and has a wide application prospect. And a new method is provided for online monitoring of a composite material structure under a complex working condition.

The invention discloses a human-computer interaction efficiency evaluation method for a central control screen of an intelligent automobile, and belongs to the technical field of intelligent automobile interaction. Determining use requirements of the central control screen; the method comprises the following steps: acquiring indexes in three aspects of driver response behavior, vehicle control and user experience, performing fuzzy C-means clustering on three dimensions for each vehicle using scene to obtain clustering scores of each sample on different dimensions in different vehicle using scenes, calculating weights of all dimensions in different vehicle using scenes according to the clustering scores, and calculating the weights of all the dimensions in different vehicle using scenes; calculating average clustering scores of all the samples in all the vehicle use scenes, and judging the man-machine interaction efficiency level of the central control screen of the intelligent vehicle; in combination with a Fuzzy C-means clustering algorithm and an entropy weight method, clustering results of different dimensions in each scene and the weight of each dimension in different vehicle use scenes are adaptively calculated, and the objectivity and generalization ability of data are improved.

A method includes presenting, within a display device of a computing system, a graphical user interface (GUI) comprising a map of an area of interest (AOI) of a cellular network. The method retrieves known geolocations within the AOI of subscriber devices and eliminates, for each site, one or more of the known geolocations that are beyond a threshold distance away from the site. The method clusters, for each sector of each site, remaining geolocations to identify a plurality of clusters of the remaining geolocations. The method determines, based on the clustering, a coverage polygon for each sector and determines, for each sector, a largest overlap portion of the coverage polygon of the sector with at least a second coverage polygon from neighboring sites to the sector, so as to determine how to resolve capacity-related issues with at least some of the sectors.

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 text-guided multi-dimensional multi-modal image clustering method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a target clustering dimension and a plurality of to-be-clustered images, and generating a description text and two answer texts for the target clustering dimension for each image; performing feature coding on each image and the corresponding description text and answer text to obtain image features, description text features and answer text features; calculating similarities between the image features and the latter two, and performing weighted fusion based on the similarities to obtain fused text features; performing cross attention calculation on the image features by taking the fused text features as query to obtain image representation focused on a target clustering dimension after text guidance, and clustering according to the image representation to obtain a clustering result; the method has the advantages that the problems of text and image content disjunction and semantic dimension conflict in image clustering can be relieved, and semantic interpretability and accuracy of clustering results are improved.

The invention relates to the field of technological parameter monitoring, in particular to a sealing element injection molding machining technological parameter monitoring method and system. The method comprises the following steps: collecting each technological parameter of injection molding processing of the sealing element, and calculating a temperature-pressure cooperation coefficient of each processing period; analyzing the proportion of the difference condition of the process parameters to obtain a change factor of each process parameter, constructing a process change vector, and calculating the process similarity of each processing cycle; the method comprises the following steps of: calculating a coupling stability tensor at each acquisition moment on the basis of a temperature-pressure cooperation coefficient and a process similarity, constructing a coupling deviation degree, modifying a measurement distance in a DBSCAN density clustering algorithm to obtain a clustering result, obtaining abnormal process parameters on the basis of the clustering result, and completing monitoring on the injection molding processing process parameters of the sealing element; the machining process parameter monitoring efficiency is improved, and the monitoring effect is improved.

The invention discloses a building detail extraction method and device based on oblique photography and composite modal features, and the method comprises the steps: calculating a tilt parameter of a midpoint of a point cloud model based on the point cloud model of oblique photography data of a target building, and converting the tilt parameter into a color parameter; projecting points on the point cloud model to a target building view area of a two-dimensional plane to obtain a plurality of superpixel blocks, inputting features of the superpixel blocks into a variational auto-encoder, and performing optimization to obtain encoder optimization weight data; and inputting the features of the super-pixel blocks into an encoder to obtain corresponding optimized latent variables, clustering the super-pixel blocks based on the optimized latent variables, and mapping a clustering result to the three-dimensional model of the target building to obtain details of the target building. By adopting the technical scheme, the three-dimensional features and the color features are compounded, and the structure details and the distribution characteristics of the complex building are captured by utilizing the relevance between the features; an encoder of a pre-trained variational auto-encoder is used for aggregation, so that the accuracy of an aggregation result is improved.

The invention discloses a central position determination method and apparatus, and an electronic device. The method comprises the following steps: on the basis of deep packet inspection signaling data of an operator, determining feature data of a service cell, the deep packet inspection signaling data at least comprising a user identifier, a service cell identifier and service occurrence time, and the feature data at least comprising stay durations of different users in different service cells; based on the feature data, performing clustering processing on the service cells to obtain a plurality of first clustering results; performing clustering processing on the physical position of the service cell in each first clustering result to obtain a plurality of second clustering results; and determining the central point of the second clustering result according to the mean value of the second clustering result, and determining the position information of the central point. The technical problem that high-precision and low-cost data support cannot be provided for public services due to the fact that the center position of the area cannot be efficiently and accurately determined in the related technology is solved.

The invention discloses a multi-disturbance scene-oriented wind turbine generator coupling modeling and clustering analysis method, and relates to the technical field of wind turbine generator running state monitoring. According to the method, running data of a unit in mechanical, electrical and wind regime physical domains are synchronously acquired through multi-source data, and a cross-domain coupling correlation tensor is constructed based on a phase-space reconstruction theory; extracting a coupling feature representing a multi-physical domain dynamic interaction relationship; forming a fusion scene feature vector by combining the shape feature and the topology invariant of the disturbance signal; based on a self-adaptive hierarchical clustering algorithm of a shape sensing distance, accurate classification of multiple disturbance scenes and typical scene center identification are realized; carrying out clustering result verification and scene identification; a self-adaptive response strategy is generated based on typical scene features, and intelligent optimization and safety early warning of the unit operation state are achieved; and the state sensing and self-adaptive regulation and control capabilities of the wind turbine generator in a multi-disturbance scene are effectively improved.

The invention discloses a segment scanning type DDoS attack detection method and device, electronic equipment and a storage medium. The detection method comprises the following steps: for each IP address in a plurality of IP addresses, acquiring a first traffic feature set comprising traffic features of N dimensions of a first traffic set taking the IP address as a destination IP address in a first time period; calculating weights respectively corresponding to the traffic characteristics of the N dimensions based on a historical traffic set; corresponding weights are given to the traffic features of the N dimensions of the IP address, and a second traffic feature set corresponding to the IP address is obtained; clustering the plurality of IP addresses based on a plurality of second traffic feature sets corresponding to the plurality of IP addresses to obtain a clustering result; and determining the IP address network segments subjected to the segment scanning type DDoS attack in the IP address network segments where the plurality of IP addresses are located based on the clustering result. According to the method and the device provided by the embodiment of the invention, the segment scanning type DDoS attack can be effectively detected, the false alarm and the missing alarm are reduced, and the network security is improved.

The invention provides a content auditing method and device, electronic equipment and a storage medium, and relates to the technical field of content auditing, multi-level feature extraction is performed on an original text to generate text vectors, and a self-adaptive hierarchical clustering algorithm is adopted to cluster the text vectors, so that the content auditing efficiency is improved. And meanwhile, key text features are extracted based on a clustering result to generate hierarchical violation labels, and the violation classification model is updated by utilizing new violation labels through an incremental learning mechanism. The problems that in the prior art, text variants are difficult to recognize due to insufficient keyword filtering semantic understanding, traditional hierarchical clustering calculation is high in complexity and cannot adapt to large-scale data, a machine learning classification model lacks a dynamic updating mechanism, so that new illegal content is missed, and the model is difficult to continuously adapt to new auditing requirements can be solved. The technical effects of improving the semantic recognition accuracy of the violation content, improving the real-time performance of large-scale text auditing, automatically discovering new violation categories, reducing leak detection and enhancing the expansibility and dynamic adaptability of a content auditing system are achieved.

The invention relates to the technical field of highway tunnel lining disease evaluation, and discloses a highway tunnel lining disease dynamic clustering evaluation system, which comprises a data acquisition module used for acquiring multi-dimensional disease data of a tunnel lining and preprocessing the data so as to construct a disease dynamic matrix. And the data analysis module is connected with the data acquisition module, establishes a fuzzy similar matrix among the disease space units based on the disease dynamic matrix, generates a closed similar matrix in combination with a transitive closure algorithm, and further analyzes the closed similar matrix through a dynamic clustering algorithm to obtain a disease clustering result. And the data output module is connected with the data analysis module, constructs a comprehensive evaluation model according to the clustering result and the historical maintenance data, and finally determines and outputs the risk score and the maintenance priority of the tunnel lining disease. According to the invention, tunnel disease identification precision and maintenance resource configuration efficiency are improved, and safe operation of the tunnel is guaranteed.

The invention discloses a lunar surface target three-dimensional point cloud detection and positioning method and system based on semantic information, relates to the technical field of three-dimensional object detection, and receives three-dimensional point cloud data and a two-dimensional image detection result, the two-dimensional image detection result is obtained by detecting acquired image data based on a pre-established two-dimensional target detection model, performing ground segmentation on the three-dimensional point cloud data to obtain non-ground point cloud data, mapping the non-ground point cloud data into a two-dimensional grid with a preset size to obtain a two-dimensional clustering result, and performing clustering on the non-ground point cloud data to obtain a clustering result; mapping the two-dimensional clustering result to the three-dimensional point cloud data to obtain a plurality of point cloud clusters; the points of each point cloud cluster are projected into an image coordinate system, dynamic weighted scores are calculated based on the number and confidence of the projection points of each point cloud cluster falling into the detection frame and the geometrical characteristics of the point cloud clusters, the point cloud cluster with the highest dynamic weighted score is selected for distance calculation, and the distance of the point cloud cluster with the highest dynamic weighted score is calculated; and obtaining a lunar surface target three-dimensional point cloud detection and positioning result.

According to the automatic measurement system and method for the double-view-field structured light turbine blade, the double-view-field structured light measurement system is constructed, automatic viewpoint planning is carried out based on a turbine blade CAD model, discretization processing is carried out on the turbine blade CAD model to obtain point cloud data, a normal vector clustering algorithm is used for carrying out region segmentation on point cloud, and the point cloud data are subjected to region segmentation; estimating a scanning visual angle direction and a viewpoint position based on a clustering result, calculating displacement of each axis by combining an inverse kinematics model of the three-axis motion platform, and controlling the three-axis motion platform to move to a preset pose; double-view-field structured light hybrid measurement and coordinate system unification are executed, point cloud data obtained through multiple times of measurement are subjected to precise registration, point clouds are unified into an approximate global coordinate system through pose information of a three-axis motion platform, precise registration is conducted on the point clouds through an iterative nearest point algorithm, the splicing precision is improved, and accumulative errors are reduced.