12 results about "Pattern clustering" patented technology

This invention relates to an intelligent deformation discrimination method based on InSAR temporal deformation patterns and building semantic information, comprising: acquiring InSAR temporal monitoring data and building vector data of the target monitoring area; performing temporal deformation pattern clustering on discrete deformation scattering points to determine the deformation pattern category label of each discrete deformation scattering point; identifying discrete deformation scattering points falling within the polygonal area of ​​the building outline as the internal point set of the building, and identifying discrete deformation scattering points falling within the surrounding foundation ring zone as the surrounding foundation point set; extracting a building-level comprehensive feature vector, including basic statistical feature components, temporal pattern distribution features, and internal and external difference features, using a single building as a unit; inputting the building-level comprehensive feature vector into an unsupervised anomaly detection model to filter out anomaly building sets; inputting the features of the anomaly building sets into an anomaly attribution clustering model, and determining the anomaly cause category of each anomaly building based on the clustering results.

This invention discloses a method and system for battery aging assessment of battery swapping cabinets based on charging pattern clustering, belonging to the field of battery testing technology for battery swapping cabinets. The method includes: analyzing battery charging cycles according to the charging cycle of the swapping cabinet to construct multi-dimensional charging features; performing clustering learning based on these features to construct multiple charging pattern clusters; performing health analysis on the battery based on these clusters to obtain battery health status parameters; retrieving real-time charging data and matching the charging pattern clusters with the health status parameters to determine the target charging pattern cluster; and performing battery aging assessment based on the target charging pattern cluster to generate aging assessment results. This invention solves the technical problems of low assessment accuracy and poor real-time performance caused by the diversity of charging patterns in existing battery aging assessment methods. It achieves refined analysis and dynamic matching of battery health status based on charging pattern clustering, improving the accuracy, scenario adaptability, and real-time responsiveness of battery aging assessment results.

Systems and methods for simultaneously analyzing time-series and dominant frequency data from both a process domain and an electrical domain of a facility, such as an industrial plant, to detect instances of deviation from optimal, normal, or other predefined process conditions or electrical trips. Detecting when changes in frequencies occur in the time-series data creates a time-series of the changes in dominant frequencies. A data analysis server detects instances of deviation from the predefined process conditions or electrical trips by detecting one or more of correlations, patterns, clusters, and the like in the rates of change. The data analysis server employs one or more of statistical analyses, data mining, machine learning, deep neural networks, parallel coordinate analyses, etc. to identify the deviations and predict or detect onset of an undesired event such as a process perturbation or electrical trip.

This invention discloses a liver fibrosis grading system based on serum index data. The invention relates to the field of liver fibrosis grading, and includes a standardized time-series acquisition module for liver fibrosis-related serum physiological signals, a gold-standard paired liver fibrosis grading pattern library construction and pattern clustering module, a subject physiological signal pattern matching and grading module, and a diagnostic result verification and pattern library dynamic optimization module. This liver fibrosis grading system based on serum index data utilizes a completely non-invasive serum detection method. Through standardized process design and intelligent module linkage, it achieves full automation from signal acquisition, preprocessing, feature extraction to judgment output, significantly shortening the diagnostic cycle and reducing reliance on the professional skills of clinical operators. It is suitable for large-scale population screening, dynamic follow-up, and promotion in primary healthcare institutions, possessing extremely high clinical application value and socio-economic benefits.

This invention provides a method, apparatus, electronic device, medium, and product for constructing a target knowledge base, relating to the field of knowledge base construction technology. The method includes: obtaining a set of symbolic expressions; the set of symbolic expressions includes at least two symbolic expressions; converting the format of each symbolic expression in the set of symbolic expressions to determine a set of symbolic tree structures; the set of symbolic tree structures includes the symbolic tree structure corresponding to each symbolic expression, and each symbolic tree structure includes at least one symbolic subtree; calculating the subtree similarity between any two symbolic subtrees, and determining the symbolic expression pattern clustering result based on the subtree similarity; constructing a set of candidate decomposition patterns for symbolic expressions based on the symbolic expression pattern clustering result; and determining the target knowledge base based on the symbolic expression pattern clustering result and the set of candidate decomposition patterns for symbolic expressions. The technical solution of this invention improves the accuracy and efficiency of downstream symbolic regression by using a target knowledge base determined through hierarchical analysis.

This application discloses a method, apparatus, device, medium, and program for mining traffic behavior patterns, relating to the field of traffic data processing technology. The method constructs traffic scene samples by acquiring multi-dimensional traffic features of target objects, extracts and integrates target trajectories, maps, and interaction features to form a unified scene vector, and generates behavior reference labels by combining the real and predicted trajectories of future time windows after initial pattern clustering. It judges the consistency of cluster-level behavior and detects intra-cluster heterogeneity, and finally uses semantic auxiliary information to subclassify the initial pattern clusters to obtain more refined traffic behavior patterns. This solves the problems of intra-class mixing and insufficient semantic consistency in clustering results in the prior art, improves the purity and interpretability of pattern units, and is applicable to scenarios such as intelligent traffic analysis and autonomous driving assistance.

This invention belongs to the field of data management technology, specifically relating to a method and system for managing geographic information mapping data based on multi-source spatiotemporal features. The method includes: acquiring mapping data containing spatiotemporal coordinate attributes and generating time-granularity codes; selecting the optimal child node based on comprehensive cost when inserting data; when a node is overloaded, evaluating the spatiotemporal variability cost of candidate splitting strategies to select the optimal strategy; for newly generated leaf nodes, determining the reference object and differential accuracy level based on the spatiotemporal distribution of internal data objects, and storing the differential values ​​of the data relative to the reference object; extracting query feature vectors and matching historical query patterns to cluster centroids for data prefetching; and asynchronously updating the centroids and adjacency pointer transition probabilities in batches after the query. This invention improves the retrieval response performance of mapping data while effectively compressing data storage space by constructing an index structure and combining differential storage with predictive prefetching.

This invention discloses a method and system for clustering analysis of test data patterns in smart terminals, belonging to the field of data analysis technology. The method includes: acquiring operational data of smart terminal components; identifying micro-deviation events based on the operational data and preset characteristic fluctuation patterns, wherein the preset characteristic fluctuation patterns reflect the fluctuation of system load under historical fault interference; constructing a directed acyclic graph (DAG) based on the micro-deviation events; extracting structural features from the DAG, wherein the structural features reflect the internal logical relationships of the DAG; and clustering the structural features using a preset clustering algorithm to identify fault patterns, wherein the fault patterns reflect the complete event chain of fault occurrence. This invention can combine micro-deviation events and structural features to identify fault patterns, thereby achieving data pattern clustering analysis and improving accuracy and reliability.

The application relates to the technical field of assisted reproductive health management, and discloses an assisted reproductive management method and system. The method comprises the following steps: receiving and standardizing physiological data of a plurality of monitoring devices, performing time sequence alignment and feature fusion to form a multi-dimensional feature matrix; utilizing pattern clustering and a sliding window technology to identify the dynamic evolution track of a key physiological pattern; performing state deduction on the pattern sequence based on an individualized model to predict a future physiological state; generating a regulation strategy sequence according to the difference between the prediction and a target, and executing an instruction; comparing real-time physiological response data with an expected model to dynamically adjust a subsequent strategy, thereby forming a closed-loop optimization; and finally evaluating the effectiveness of a path according to complete cycle data and updating model parameters. The application realizes continuous and accurate prediction of a reproductive physiological state and individualized adaptive regulation, and improves the accuracy and efficiency of management.

The application discloses a workpiece defect detection method and system based on machine vision, collects workpiece surface image sequence data for preprocessing to form a preprocessed image set, performs multi-scale feature extraction through deep separable convolution and executes attention enhancement processing to construct a saliency region to generate a defect response parameter, performs spatial pyramid pooling on a feature map and uses fused features to regress a bounding box to locate a defect section to form a feature matrix, performs mode clustering on the feature matrix to determine a class distribution and extract a weak response region to generate an early warning mark, generates a defect grade parameter according to the class distribution and the early warning mark to construct a defect feature atlas, extracts feature sensitivity weights through multi-scale loss constraints to construct a weighted discrimination rule to output a visual detection result. The application realizes integrated detection of accurate defect positioning, type identification, grade evaluation and early warning.

This invention discloses an edge computing-based fault detection and management system and method for electromagnetic vibration tables, relating to the field of electromagnetic vibration table condition detection technology. The system includes a transmission and acquisition module, a fault analysis module, and a management decision module. Its key technical points are: sending a target excitation sequence to the electromagnetic vibration table and acquiring multiple response sequences output by the electromagnetic vibration table; wherein the electromagnetic vibration table is associated with multiple test task types; extracting features from multiple response sequences through edge nodes, identifying coupling deviations of each feature coefficient, updating evaluation weights using the MapReduce framework, and employing ART neural network execution mode clustering to determine the health index of the electromagnetic vibration table; when acquiring test tasks to be executed, introducing a rule engine, and triggering a graded fault management response in conjunction with the health index; this invention improves the timeliness of fault identification for different test task types and enhances management efficiency.