8 results about "Density based clustering" patented technology

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.

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.

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 application relates to a photovoltaic module abnormality monitoring and fault diagnosis method based on a clustering algorithm; multi-dimensional running time series data of a target photovoltaic module in a preset historical time period is collected, and original data collected is preprocessed to form a regular time series data set; based on the preprocessed time series data set, a feature vector for clustering analysis is constructed, the feature vector is input into a clustering algorithm, the historical normal running state of the photovoltaic module is clustered, and one or more clustering models corresponding to different normal working conditions are established; firstly, the application uses a Gaussian mixture model to accurately depict the complex normal behavior mode of the photovoltaic module under multiple working conditions, and establishes a flexible and robust normal state benchmark; and then, an abnormal data exploratory analysis is conducted through a noise-based density clustering method, unknown fault modes are automatically found and distinguished.

The application discloses a kind of sand earthquake liquefaction discrimination method and system based on SSA-CNN-SVM model coupling, and the present application relates to the technical field of seismic safety evaluation, comprising the following steps: obtaining historical sand liquefaction sample data set, including key influence index and liquefaction state label, while collecting geographic location information, extract statistical characteristic parameters to calculate anti-liquefaction intensity index and vibration intensity index, and according to the weighted similarity measurement of geographic characteristic parameter, the sample area is divided into multiple geological regions by condensation hierarchical clustering algorithm;In each region, the Mahalanobis distance between sample regions is calculated, and a secondary clustering is carried out using a density-based clustering algorithm to identify similar liquefaction mechanism sample clusters, and a prediction model coupled with a deep learning model and an optimization algorithm is established for each cluster to assess liquefaction risk, significantly improving the accuracy and robustness of sand earthquake liquefaction discrimination.

This invention discloses a method and system for identifying seismic liquefaction of sandy soil based on a coupled SSA-CNN-SVM model. This invention relates to the field of seismic safety assessment technology and includes the following steps: acquiring a historical sandy soil liquefaction sample dataset, including key influencing indicators and liquefaction state labels; simultaneously collecting geographical location information; extracting statistical feature parameters to calculate the liquefaction resistance index and seismic intensity index; performing weighted similarity measurement based on geographical feature parameters; dividing the sample area into multiple geological regions using a hierarchical clustering algorithm; within each region, calculating the Mahalanobis distance between sample areas; performing secondary clustering using a density-based clustering algorithm to identify clusters of samples with similar liquefaction mechanisms; and establishing a prediction model coupled with a deep learning model and optimization algorithm for each cluster to achieve liquefaction hazard assessment, significantly improving the accuracy and robustness of sandy soil seismic liquefaction identification.

The present application belongs to the technical field of industrial energy digital management, and particularly relates to a kind of industrial equipment low carbonization reconstruction efficiency intelligent evaluation method and system, its method includes: collecting the multi-source heterogeneous operation data before and after industrial equipment reconstruction;Dynamic response hysteresis index representing system dynamic instability and hysteresis degree is constructed, and the search radius of density-based clustering algorithm is adaptively adjusted using dynamic response hysteresis index;Carbon sensitive weighted distance cost function is constructed, and the optimal warping path between the measured sequence after reconstruction and the ideal benchmark sequence is found using dynamic time warping algorithm based on cost function;A low-carbon reconstruction efficiency comprehensive scoring model is constructed, and the low-carbon reconstruction efficiency comprehensive score is calculated to evaluate the low-carbon reconstruction efficiency of industrial equipment. The present application can eliminate thermal inertia misjudgment and focus on core emission reduction conditions to achieve scientific and comprehensive performance evaluation.