78 results about "Density based clustering" patented technology

The invention provides a side slope radar point cloud data slice abnormal value complementing method based on space-time fusion, which comprises the following steps: acquiring point cloud data of a plurality of time slices obtained by scanning of a side slope radar, and mapping the point cloud data into a three-dimensional point cloud; identifying a piece of abnormal regions of the three-dimensional point cloud through statistical filtering and a density-based clustering method; constructing a point cloud processing model based on PointNet + +, generating masked data and a mask index by adopting a mask strategy, and masking off pieces of point clouds; spatial features are extracted in different regions, static space weight matrixes generated by longitude and latitude and time difference global features are fused, deformation values of mask regions are predicted through a full-connection network, and model parameters are optimized; and inputting the trained model into latitude and longitude coordinates of an abnormal region of actual missing point cloud data, performing abnormal value completion, outputting a completed deformation value, and calculating a completion precision index. According to the method, the completion effect is good, all abnormal values of multiple time slices can be completed at a time, and the calculation efficiency is improved.

The invention discloses an advanced blasting pressure relief method based on rock burst prediction, and the method achieves the precise recognition and efficient intervention of rock burst risks through the combination of a micro-seismic monitoring technology, energy thermodynamic diagram modeling and an intelligent algorithm. According to the method, micro-seismic data is deeply analyzed, an abnormal activity area is identified, an energy thermodynamic diagram is constructed, a stress concentration situation is visually displayed, and risk level prediction is carried out in combination with historical data, so that early warning accuracy and response timeliness are improved, an optimal blast hole position in a high-risk area is automatically identified by adopting a density-based clustering algorithm, and the early warning efficiency is improved. Classification and directional blasting pressure relief are implemented according to risk levels, local stress is effectively released, the occurrence probability of rock burst is reduced, the overall process is highly automatic, monitoring, prediction, decision making and intervention are integrated, and the method is suitable for various complex coal mine geological environments.

The application discloses a kind of multi-path ghost suppression method of millimeter wave radar tunnel scene, it is applied to millimeter wave radar target detection field, for the poor multi-path ghost suppression effect in tunnel scene of prior art;The application first constructs confidence criterion using the multidimensional information point cloud data generated by radar original echo, and carries out density-based clustering to point cloud data;Then linear fitting is carried out to zero doppler point cloud to obtain the slope information of tunnel wall, simultaneously, the median line slope of dynamic point cloud pair is matched with the slope set, and the target pair associated successfully is judged as multi-path ghost with lower confidence;Finally, considering the motion characteristics of target in tunnel, as well as multi-path reflection geometric model, the double rectangular region division of transverse and longitudinal is carried out to dynamic point cloud, and the point cloud data in region is subjected to Doppler information constraint, and the target satisfying the condition and with lower confidence is screened out and judged as multi-path ghost.The application can detect vehicle target in semi-closed environment, while retaining real target, can effectively identify and suppress the multi-path ghost generated by reflection of tunnel wall and railing etc..

The invention discloses an analysis method and system based on AI video recognition behavior monitoring, and relates to the technical field of video recognition, and the method comprises the steps: collecting video data, calculating the overall pixel gradient change of adjacent frames, marking preliminary candidate frames, calculating the edge saliency intensity of each frame, and analyzing a salient region point set; clustering and calculating the geometric center point of each cluster as an anchor point value, and forming an anchor point set through the center points of each cluster; according to the method, concentrated extraction of salient points is realized through a density-based clustering algorithm DBSCAN, so that an extraction result has logic uniqueness and local integrity of a salient target, a local feature map is extracted by using a shallow convolutional network, target texture and edge information is effectively captured, bounding box optimization is realized by rotating an IoU loss function, and the robustness of the target is improved. And the fitting precision of the boundary of the salient region is improved.

The invention provides a concrete pumping pressure loss prediction method and system based on machine learning, and belongs to the technical field of machine learning. According to the scheme, the method comprises the steps that data samples are collected, and a high-dimensional feature data set containing concrete raw material characteristic parameters and pumping structure parameters is constructed; carrying out unsupervised hierarchical processing on the data samples by adopting a density-based clustering algorithm, taking a cluster number to which each sample belongs in an unsupervised clustering result as a new feature, and supplementing the new feature into the high-dimensional feature data set to obtain an expanded high-dimensional feature data set; and based on a machine learning algorithm, establishing a concrete pumping pressure loss machine learning prediction model, training the concrete pumping pressure loss machine learning prediction model, and performing pumping pressure loss prediction on the to-be-predicted data by using the trained pumping pressure loss prediction model to obtain a pumping pressure loss prediction result. Therefore, the nonlinear and non-uniform density distribution sample structure is identified through the density-based clustering algorithm, and the pumping pressure loss prediction precision is improved.

A method may comprise receiving airspace surveillance data from a plurality of sensors, the airspace surveillance data comprising tracks associated with one or more targets, aggregating the airspace surveillance data to obtain aggregated data, performing density-based clustering of the aggregated data to obtain a plurality of clusters, determining one or more candidate associations between the tracks and the clusters, associating each of the tracks with one of the one or more targets based on the candidate associations, and estimating a track for each of the one or more targets based on the associations between the tracks and the one or more targets.

The present invention relates to a data processing method and device based on a distributed computing model, belonging to the field of data processing technology. The method comprises the following steps: using a hash algorithm to preliminarily partition data, identifying and adjusting abnormal partitions, and refining abnormal partitions through fine-grained partitioning; identifying nodes with the same time window and generating an access policy set, selecting the optimal access set for task allocation; selecting core objects, expanding cluster clusters through a density-based clustering algorithm, and merging the clustering results of all Map tasks. The present invention improves the uniformity of the amount of data processed by each Reducer through a two-stage partitioning strategy, and then uses a cluster analysis method to find the optimal access set of the current distributed processing server node for task allocation, thereby improving data processing efficiency and resource utilization. Finally, a density-based clustering algorithm is used to remove noise points, thereby improving the accuracy of the clustering results.

The application discloses an environmental monitoring data analysis method and related device, relates to the technical field of environmental monitoring, obtains the standard sample data consistent with the attribute of the to-be-analyzed environmental monitoring data, calls a density-based clustering algorithm to process the two kinds of data, obtains a clustering result, and in the case that a turning point in a K-distance graph of the to-be-analyzed environmental monitoring data does not satisfy a preset turning point condition, determines a reference turning point by using a sliding window algorithm, so that the clustering result is more accurate; when the clustering result satisfies a condition, multi-dimensional quality analysis is performed on the to-be-analyzed environmental monitoring data, and the efficiency and accuracy of environmental monitoring data analysis are further improved; when the to-be-analyzed environmental monitoring data is subjected to multi-dimensional quality analysis, at least data stability and correlation of the to-be-analyzed environmental monitoring data are analyzed, so that the accuracy of an analysis result obtained by multi-dimensional analysis is greatly improved, and the accuracy of application of the analysis result to other scenes, such as data quality evaluation, is further improved.

The invention discloses a synchronous incremental modeling method based on slice scanning. The method comprises the following steps: step 1, obtaining object edge information according to hierarchical scanning data; step 2, using a density-based clustering algorithm to perform clustering analysis on the single-layer contour points so as to distinguish inner contours and outer contours of different areas, the clustering result including a plurality of sub-contours, and performing subsequent data matching and grid generation by taking each sub-contour as a unit; step 3, performing operation by using two adjacent layers of data, and generating a grid between the two layers in a mode of matching a nearest point; 4, optimizing the efficiency of a density-based clustering algorithm by using a parallel operation framework, and ensuring the synchronism with a data acquisition process; and 5, performing grid simplification and smoothing operation on the grid model. The method supports synchronous modeling of hierarchical data and adapts to anisotropic sampling, so that the modeling efficiency and the structure reduction precision are effectively improved.

The present subject matter relates to a system (100) and a method (400) for demand-based optimization of airline ticket pricing. The disclosed system (100) includes is a user interface (101) that facilitates the input of flight information, subsequently processed by an integrated memory (203) and processor (201). The machine learning-driven price optimization module (204) involves fetching price range and contextual information, extracting features, and further utilizing a classification model (205) for identifying demand cluster probabilities, and calculating a demand score. The system (100) further refines this demand score to account for market fluctuations. Notably, it employs advanced techniques like density-based clustering for demand segmentation and ML explainability through the Airline Experience Quotient (AEQ). This ensures transparency and enhances the user experience. Additionally, the system's capability extends to efficient model deployment, leveraging MLOps, and presenting the optimal ticket price to users for informed decision-making.

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.

The present application belongs to the technical field of trajectory smoothing method, and particularly relates to a personnel trajectory smoothing method, comprising the following steps: acquiring satellite positioning data; and performing deviation correction treatment on the drift points in the data; adopting a density-based clustering analysis algorithm to cluster the knotted points in the trajectory line due to trajectory return into one point, while ensuring the time sequence of the trajectory point set. The present application performs deviation correction treatment on the drift points in the satellite positioning data by applying Kalman filtering algorithm; the present application adopts the density-based clustering algorithm to cluster the knotted points in the trajectory line, eliminates the problem of trajectory line confusion caused by knotting, and makes the presented trajectory more clear and smooth. The present application ensures the time sequence of the trajectory points in the set after executing the clustering algorithm.

A kind of federal learning model attack defense method based on neural network feature extraction, before carrying out federal learning global aggregation in each round, neural network feature extraction model is constructed in advance in safe environment, and it is trained using public data set, trust of local parameter is guided by server itself;In online stage, the local parameters uploaded by each client are extracted and output to the server using the trained neural network feature extraction model, and the server uses the density-based clustering method (DBSCAN) with noise to classify the feature vectors and eliminate the corresponding malicious local parameters, to realize safe aggregation.The present application trains neural network in safe environment before each iteration, so that it can resist various model poisoning attacks in online stage, thereby effectively preventing the damage of malicious client to global model.

This invention discloses a method and system for identifying cross-connection issues in low-voltage distribution areas. It acquires voltage and current time-series data of users in the low-voltage distribution area, cleans the data using a mask interpolation strategy, and adaptively extracts voltage drop and current surge features based on the median absolute deviation algorithm. It constructs a voltage trend similarity model based on local regularization kernels and a mutation co-occurrence similarity model based on event level weights, employing a dual-track strategy combining daily rigorous assessment and feature revival to generate suspected voltage correlation pairs. A density-based clustering algorithm is used to generate an initial population, and a voltage similarity-weighted centroid screening mechanism is introduced to remove loose outliers in the feature space. A pairwise cross-validation model is constructed based on electrical causality, and the population is topologically refined using relative voltage response criteria and a maximum fully connected clique extraction algorithm to determine the final cross-connection population. This invention significantly improves the accuracy and robustness of cross-connection identification.

Monitoring device for anomaly detection from sensor data (102, z) determined in time succession of a technical system (100), comprising: an input interface (201) configured to receive a new sensor data point (102, z) of the technical system (100); an analysis unit (202) configured to determine, for the new sensor data point (102, z), a minimum reachable distance (minRD) with respect to a selection of neighboring training data points (x1, x2, x3) and to determine, for the new sensor data point (102, z), a minimum position (xi) in a sequence of training data points in a reachable graph (312) created by means of a density-based clustering function using predetermined training data points as input values, to insert the new sensor data point (102, z) in the reachable graph after the minimum position (xi) and before a training data point having a greater reachable distance than the determined minimum reachable distance (minRD), to assign the new sensor data point (102, z) to a determined cluster according to its position in the supplemented reachable graph and as a normal or abnormal condition according to the minimum reachable distance (minRD); and an output interface (203) configured to output the determined assignment for the new sensor data point (102, z) as a result of anomaly detection.

The application discloses a Tor network hidden malicious organization mining method based on multi-dimensional fusion detection. The method comprises the following steps: crawling the network state consensus published by Tor Metric in the latest half year, sorting the online relay list and mining the relay related information; performing multi-dimensional security evaluation based on the relay list and the characteristics, and screening out suspicious relays with abnormal scores higher than a threshold; comprehensively using the relay server descriptor file and the network public asset mapping platform information to obtain the internal attribute behavior and external intelligence features of the relay node, and modeling the similarity of relay pairs; using a random forest algorithm to calculate the correlation degree of relay pairs; based on the correlation degree of relay pairs, using an OPTICS clustering algorithm to perform density-based clustering, and dividing the suspicious relays into organizations. The application combines the internal attributes and external intelligence features of the relay node, respectively uses the similarity modeling and clustering algorithm to calculate the correlation degree of relay pairs and divide the organizations, and realizes efficient identification of the Tor network hidden malicious organizations.

This invention discloses a programmable data plane high-intensity traffic response method and system based on feature distribution, belonging to the field of network traffic classification technology. The programmable data plane high-intensity traffic response method based on feature distribution includes: extracting features from each flow in the data plane and determining whether the features are matched by the current rule; if matched, processing according to the corresponding rule; if not matched, processing as an outlier and recording it; periodically sampling processed flows, triggering rule updates when outliers reach a preset threshold; the control plane uses a density-based clustering algorithm to cluster sampled points to obtain the current traffic feature distribution shape, extracting the boundaries of each cluster to form a high-dimensional rectangle as a new rule, and formulating corresponding processing measures based on spatial features and meta-features, and issuing them to the data plane. This invention achieves line-rate processing and dynamic adaptation on resource-constrained programmable hardware, effectively addressing feature drift under high-intensity traffic while ensuring interpretability.

The present disclosure provides a road generation method, device, equipment and storage medium, relates to the field of data processing, in particular to the field of electronic map, cloud computing and big data technology. The specific implementation scheme is: obtaining position data to be processed; performing clustering analysis on the position data by using a density-based clustering algorithm to obtain data clusters; obtaining longitude and latitude coordinates of boundary points in the data clusters to obtain a boundary point set; connecting the longitude and latitude coordinates in the boundary point set in sequence by using a boundary point discovery algorithm, and drawing a road graph according to the connected boundary points. Through the above scheme, the development and operation cost is saved, and the flexibility and applicability of road generation are ensured.

The invention discloses a crack three-dimensional accurate quantification method and system based on semantic point clouds, which are used for solving the problems of skeleton extraction distortion, tail end missing, noise sensitivity and insufficient multi-scale fusion in the existing crack quantification method, and the method comprises the steps: obtaining three-dimensional point cloud data with crack semantic tags, and carrying out the denoising and smoothing preprocessing; segmenting independent crack individuals through a density-based clustering algorithm; dynamically extracting skeleton points by adopting a curvature weighted L1 median algorithm, and adaptively adjusting the point density according to the curvature; skeleton end points are complemented from the original point cloud through main direction projection; calculating crack length, width, direction and type parameters based on the complete skeleton point set; the system correspondingly comprises a point cloud preprocessing module, a segmentation module, a skeleton extraction optimization module, a parameter calculation module and the like. According to the method, high-precision and automatic three-dimensional quantification of a crack structure with complex bending and strong noise interference is realized, and the accuracy, the integrity and the anti-noise capability of skeleton extraction are remarkably improved.

This invention discloses a programming pattern mining method and system based on a large language model, belonging to the fields of software engineering and code analysis technology. The method includes: scanning and filtering target code repositories to select valid source files; constructing an abstract syntax tree using a static parser and extracting candidate code fragments at three granularities: function level, statement level, and interval level; vectorizing the code fragments using a code embedding model, assembling the AST structure and semantic vectors into meta-information; identifying high-frequency code patterns with semantic similarity using a density-based clustering algorithm; abstracting and generalizing variable elements through sliding window consistency analysis to generate generalized code templates; collaboratively determining the programming patterns of candidates using a multi-agent system, ultimately outputting a code template library; and constructing the code template library into a RAG retrieval knowledge base for use in software engineering tasks such as unit test generation, programming standard recognition, and code completion.

The present invention discloses a security-enhanced Internet of Vehicles (IoV) trust assessment method combined with transfer learning, which relates to the field of IoV security. The method comprises the following steps: constructing an IoV model for urban roads based on urban road conditions; designing an interactive authentication module based on the elliptic curve Diffie-Hellman protocol and the elliptic curve digital signature algorithm based on the IoV model for urban roads; introducing a true and false message identification method based on a three-factor trust model improved by a transfer learning method based on the interactive authentication module, designing a transfer learning model TranferMegaCRN in speed expectation sub-factor analysis to obtain the predicted speed of the message sender; designing a vehicle trust value calculation method based on the transfer learning model TranferMegaCRN using an improved Bayesian method and a density-based clustering screening algorithm, and constructing an IoV trust management model; and completing vehicle trust assessment based on the IoV trust management model.

The invention relates to the field of power grid operation and maintenance, and discloses a power grid abnormity positioning method and device and a medium, and the method comprises the steps: obtaining the new energy generating capacity according to the obtained meteorological data, and obtaining the parameters of a generator set; obtaining power grid simulation time sequence data according to the new energy generating capacity and the generator set parameters; clustering the power grid simulation time series data by adopting a density-based clustering algorithm; adopting an isolated forest algorithm for the clustered power grid simulation time series data to obtain an isolated forest model; and according to the isolated forest model and the acquired power grid time sequence real-time data, positioning an anomaly in the power grid time sequence real-time data. According to the method, the abnormal region can be quickly and accurately positioned in a large amount of power grid operation data, and the complexity of the operation state of a novel power system is effectively dealt with.

The invention relates to a method for expanding an object detected in a parking space of motor vehicles (2), wherein at least a partial area of ​​the parking space is detected by at least one environmental sensor (7, 9, 18) of at least one motor vehicle (2), and first sensor data (19) are generated in the process, wherein on the basis of the first sensor data (19) a plurality of first data points (pk) and a number of second data points (pk) are generated in a digital map (30), each of which corresponds to a detection of an object at a specific location in the parking space, wherein a first object cluster (CL1) is generated from the first data points (pk) by a density-based clustering method, wherein at least one first scatter parameter (s 1,xx / yy , s 1,jk) of the first data points (pk) of the first object cluster (CL1) is calculated, and wherein the number of second data points (pk) is added to the first object cluster (CL1) to form an extended object cluster (CLE), and based on the first scatter parameter (s 1,xx / yy , s 1,jk ) and based on the number of second data points (pk) without directly using the first data points, at least one extended scatter parameter (s 12,jk ) of the extended object cluster (CLE) is calculated.

Aspects includes a system, method and computer-implemented method that provides identification of surgical workflow outliers, including identification of an outlier includes determining distances between workflows using a density-based clustering algorithm.

The present application belongs to the technical field of big data mining, and specifically relates to a social robot detection algorithm based on user semantics, attributes and neighborhood information. The algorithm comprises: encoding the text content through a BERT model, modeling the semantic representation of the user, and combining the user attributes and neighborhood features to build a user relationship network with the user as a node and the forwarding relationship as an edge. The constructed graph data is subjected to self-supervised training using an improved graph attention network model to learn the social user representation. The model is subjected to parallelized calculation in the form of subgraph sampling, and auxiliary tasks are set using multi-task learning. In order to solve the data imbalance problem, a conditional adversarial generative network is used for data augmentation, and the final user vector representation is subjected to density-based clustering to obtain the discrimination result of whether it is a social robot. The algorithm adopts a self-supervised technology and introduces an adversarial idea, and is strong in generalizability and in line with the future technology development trend.

To provide a machine-learning program, a machine-learning device, and an information-processing system that enable stable AI operation.SOLUTION: An inference result obtained by inputting operation data to a learned machine learning model trained based on learning data is collected, density-based clustering is executed on the collected inference result to generate a cluster, an estimated label corresponding to the cluster is estimated for each cluster from correct labels corresponding to all correct answers that can be the inference result, and the learned machine learning model is fine-tuned based on the operation data belonging to the cluster and the estimated label corresponding to the cluster.SELECTED DRAWING: Figure 2

The application provides a panel defect point aggregation detection method, system, device and storage medium, and relates to the technical field of defect detection. The method comprises the following steps: acquiring characteristic data of a panel, wherein the characteristic data comprises coordinate information of all defect points on the surface of the panel; performing clustering and division processing on the characteristic data of the panel by using a density-based clustering algorithm to obtain a plurality of clustering regions; performing region merging processing on the plurality of clustering regions to obtain at least one merged region; and comprehensively detecting the defect points of the panel based on the clustering regions and the merged region to obtain a defect point aggregation detection result. The density-based clustering algorithm is applied to the industrial panel defect detection, and the defect point aggregation condition can be more intuitively considered by introducing the defect point merging adjustment between clusters and the total number threshold judgment of the defect points in the cluster, so that the accuracy and reliability of the defect point aggregation detection are improved.

The invention belongs to the technical field of point cloud target detection, and discloses a power line point cloud extraction method and device, equipment and a storage medium. The method comprises the following steps: acquiring urban environment point cloud data; performing point cloud filtering on the urban environment point cloud data according to cloth filtering and horizontal linear feature constraints to obtain initial power line point cloud data; performing multi-scale voxel connectivity analysis and vertical continuity constraint point cloud filtering on the initial power line point cloud data to obtain reference power line point cloud data; performing point cloud extraction according to the density characteristic and the geometric characteristic of the reference power line point cloud data to obtain power line point cloud data; through multiple constraints such as linear features, vertical continuity, local geometric consistency and the like, various ground feature noises are greatly inhibited, the multi-scale voxel analysis and density-based clustering effectively prevent the power line point cloud from being excessively segmented or mistakenly deleted in the filtering process, the extracted power line is ensured to be continuous and complete, and the power line point cloud extraction efficiency is improved. And more accurate power line point cloud extraction is realized.

The application discloses a multi-person pose estimation method based on a millimeter wave radar, and specifically comprises the following steps: acquiring original multi-person point cloud data and real-time joint data, calibrating the two, so that they are in the same three-dimensional space coordinate system, obtaining three-dimensional coordinates of the real-time joint and taking the three-dimensional coordinates as label data; processing the original multi-person point cloud data by using a density-based clustering algorithm, removing noise points and separating the point cloud, obtaining separated multi-person point cloud data for constructing a data set; after preprocessing independent point cloud data to be processed, inputting the preprocessed independent point cloud data into a trained point cloud-based pose estimation neural network to obtain a three-dimensional coordinate prediction value of the real-time joint; the pose estimation neural network is sequentially cascaded by a space embedding layer, a first feature extraction layer, a second feature extraction layer, a global feature extraction layer and a multi-branch full connection layer, and the pose estimation neural network is trained on the data set.

The present invention discloses a method for tracking user positions in a millimeter wave MIMO system, comprising: S1: establishing a two-dimensional spatial coordinate system, determining the physical coordinates of the millimeter wave MIMO base station, and each base station transmits the received user signal back to the data center for joint processing; S2: discretizing the channel beam domain by angle, sparsely representing the millimeter wave channel, and listing the sparse Bayesian learning problems of channel estimation and arrival angle tracking; S3: estimating the channel gain vector by generalized approximate message passing, and tracking the arrival angle by utilizing the time-varying characteristics of the user position; S4: utilizing the density-based clustering algorithm DBSCAN to screen out base stations with a direct line of sight path for positioning; S5: estimating the user position by utilizing the weighted minimum mean square error (WLS) estimator. The present invention realizes sub-meter-level high-precision user position tracking in a millimeter wave MIMO system by tracking the angle of moving users. At the same time, the algorithm has low computational complexity, meeting the demand for rapid positioning.