17 results about "Nearest neighbor clustering" patented technology

The invention discloses a question and answer system based on a semantic vectorization knowledge graph and approximate nearest neighbor clustering, and the system comprises a data uptake and preprocessing module which is used for the input and preliminary processing of an unstructured text; the knowledge graph construction module is used for constructing a dynamic knowledge graph according to the primarily processed data; the knowledge graph enhancement module is used for expanding representation of entities in the knowledge graph by adopting an information enhancer and converting each entity node and relation node in the knowledge graph into a high-dimensional semantic vector by using an embedded model; during work, the general knowledge graph can convert structured and unstructured data into semantic vectors, the semantic vectors are stored locally in the form of the knowledge graph, and cross-domain knowledge accurate retrieval is supported in combination with a fine-grained tree pruning technology. The knowledge integration model is based on an approximate nearest neighbor clustering method, so that integrated output of a plurality of large language models can be realized, and the hit rate of answers with relatively high credibility is effectively improved.

The invention discloses an adaptive learning method and system for underground cable damage risk assessment, and relates to the field of cable damage risk assessment. The method comprises the following steps: constructing state evolution vectors of M cable damage events, carrying out neighbor clustering on the M state evolution vectors, generating K state evolution clusters, determining first similarities between the K state evolution clusters and real-time evolution vectors, constructing a similar cluster sequence according to the K first similarities, and in the similar cluster sequence, carrying out neighbor clustering on the M state evolution vectors to generate K state evolution clusters; calculating a variance of a plurality of relative time zone codes in the Q similar clusters, determining an adaptive learning cluster according to the variance of the plurality of relative time zone codes in the Q similar clusters, determining a second similarity in the adaptive learning cluster, and calculating a damage risk index based on the second similarity; according to the method, the damage risk index is generated based on the weighted relation between the L2 norm of each historical state evolution vector in the adaptive learning cluster and the second similarity, and visual quantification of damage risk assessment is realized.

The invention discloses a teaching intelligent service method, electronic equipment and a storage medium. The method comprises the following steps: acquiring a jump vector set of P target teaching objects; wherein the jump vector set comprises J jump vectors; performing neighbor clustering on J jump vectors in the jump vector set to generate K jump vector clusters; calculating Gini coefficients of J jump vectors in the K jump vector clusters; comparing the Gini coefficients of the K jump vector clusters with a coefficient threshold, and performing structural jump screening on the K jump vector clusters until S structural jump clusters are obtained; positioning jump knowledge points according to the S structural jump clusters; according to the method, the jump vectors of the target teaching object on the adjacent knowledge points are constructed, and all the jump vectors are classified by adopting a neighbor clustering mode, so that clear positioning of the jump knowledge points in the teaching path is realized, and support is provided for content feedback acquisition and knowledge point micro-reconstruction.

A collective data leakage attacks on a Retrieval-Augmented Generation (RAG) application for a generative artificial intelligence (GenAI) application is detected and prevented based on an analysis of incoming queries to distinguish normal and potentially malicious querying behavior. Similarity distances associated with each query are determined, such as the distances between the query vector embeddings and the nearest neighbors in the vector embedding space, distances between each query vector embedding and other query vector embeddings for other queries from the same user, or distances between each query vector embedding and the nearest neighbor cluster of vector embeddings in the vector space. A data leakage attack on the RAG application may be determined based on the similarity distances associated with each query. In response to the identification of a potential data leakage attack, the release of data from the RAG application may be halted.

The invention discloses a multi-step clustering point cloud denoising method for a disinfection robot in a water mist environment, and belongs to the field of disinfection robot radar point cloud processing. Firstly, primary noise separation is carried out based on point set density difference, and a density distribution model is constructed to realize initial point set division; a dynamic compensation mechanism is introduced on the basis, and accurate re-classification of misclassification point sets is realized through a shared nearest neighbor clustering algorithm; and finally, an improved I-DBSCAN algorithm fused with a local density adaptive parameter adjustment strategy is adopted to complete secondary filtering of residual noise. According to the method, noise and targets can be adaptively distinguished through a multi-stage cooperative processing mechanism, the integrity of effective point clouds is guaranteed while water mist interference and miscellaneous point shielding are efficiently removed, the influence of water mist generated when the disinfection robot works and complex environment data on correct navigation information is greatly weakened, and the navigation accuracy is improved. The limitation that laser radar cannot be used for accurate navigation when the robot sprays disinfectant fluid is solved.

The present invention discloses an unsupervised detection method for the acoustic fingerprint signal faults of power transformers, which is applied to the technical field of power transformer fault diagnosis. The method includes: acquiring the acoustic fingerprint signal data of each power transformer by using sensors to form a sample set, using the density peak algorithm to find the density center of the sample set and using the K-nearest neighbor clustering algorithm for clustering to obtain a training sample set, putting it into an autoencoder network to extract a low-dimensional sparse representation sequence sample set, performing preprocessing, using the first part of the sequence as the input of the gated recurrent unit (GRU) and the second part as the output for training; the acoustic fingerprint signal to be measured passes through the prediction sequence and the GRU target output in sequence to obtain an abnormal score set, and the 3-sigma rule is used to process the abnormal score set, so as to detect faults. The present invention effectively improves the accuracy of fault detection and obtains a lower false alarm rate and missed alarm rate.

The invention discloses a neighbor clustering method and device for complex manifold data, and relates to the technical field of data mining. The method comprises the following steps: firstly, obtaining lambda-nearest neighbors of each data object by using a natural neighbor search algorithm, and calculating the density; then determining a natural density peak, and dividing other objects into sub-clusters to which the natural density peak belongs according to representative information of the other objects; setting a density threshold value tau, determining a non-noise data object, and performing clustering by using a k-nearest neighbor of the non-noise data object; and finally, dividing the noise data object into the class cluster to which the natural density peak belongs, thereby finishing the clustering analysis of the whole data set. The invention discloses a neighbor clustering method for complex manifold data, which eliminates the interference of noise points in the clustering process, performs fast clustering by utilizing neighbor information, can accurately identify any shape class clusters in the complex manifold data, has excellent adaptability and noise resistance to high-noise data, and can be used for high-precision clustering of the complex manifold data. And obvious high efficiency and robustness are shown.

This invention discloses a spatiotemporal clustering method for small target detection based on sparse event points. The method involves preprocessing raw data captured by an event camera to obtain preprocessed event data; traversing all event points in the event data, assigning a weight value to each event point, and sorting all event points in descending order according to their weight values; using the average weight of the bottom M% of event points as a clustering threshold, and performing nearest neighbor clustering on the event data based on this threshold to obtain preliminary detection results; and finally, performing point cloud filtering on the preliminary detection results to obtain the final detection results. This invention can directly extract small target features from event data for detection. Under static backgrounds, it can effectively filter out event point clutter triggered by thermal noise and interference event points generated by the movement of large objects, achieving high detection accuracy.

The application discloses a kind of adaptive learning method and system for underground cable breakage risk assessment, it is related to cable breakage risk assessment field;The method comprises: constructing the state evolution vector of M cable breakage events, the first similarity of K state evolution clusters and real-time evolution vector is determined to the near neighbor clustering of M state evolution vectors, generates K state evolution cluster, according to K first similarity, constructs similar cluster sequence, in similar cluster sequence, the variance of several relative time area numbers in Q similar clusters is calculated, according to the variance of several relative time area numbers in Q similar clusters, determine adaptive learning cluster, determine the second similarity in adaptive learning cluster, and calculate breakage risk index based on second similarity;The application generates breakage risk index based on the weighted relationship of the L2 norm of each historical state evolution vector in adaptive learning cluster and second similarity, realizes the intuitive quantification of breakage risk assessment.

The present invention proposes a method and system for abnormal energy consumption monitoring of base stations based on wavelet decomposition and transfer discrimination, including: performing clustering and classification of base station energy consumption samples based on multi-dimensional high and low frequency features obtained from an energy consumption feature set; constructing an abnormal energy consumption discrimination model with the high and low frequency features, archive feature data, meteorological feature data, and holiday feature data of a certain clustering energy consumption historical sample data set as inputs; performing nearest neighbor class model parameter tuning transfer learning on the constructed abnormal energy consumption discrimination model, adaptively adjusting the model parameters through reinforcement learning, and outputting a nearest neighbor clustering abnormal energy consumption discrimination model. Sequentially perform nearest neighbor transfer learning and parameter tuning until the construction of abnormal energy consumption discrimination models for all clustering categories is completed, forming a set of energy consumption abnormal discrimination models with generalization; using the set of energy consumption abnormal discrimination models with generalization to perform real-time monitoring of base station energy consumption. Thereby further improving the effectiveness and generalization of abnormal energy consumption discrimination.

The invention provides a different-vehicle-type electric vehicle path planning method and device based on a multi-strategy fusion Jaya algorithm. The method comprises the following steps: acquiring vehicle information and node information; constructing a fitness function based on the total path cost function; generating an initial population by using a nearest neighbor clustering method according to the vehicle information and the node information; for the current population, a double-elitist adaptive reservation strategy, an updating strategy of a Jaya algorithm, a Monte Carlo acceptance criterion and a vehicle type adjustment strategy are comprehensively adopted to carry out iterative optimization, a greedy algorithm strategy is used to insert charging station nodes, and a reverse energy calculation strategy is used to optimize the positions of the charging station nodes; obtaining a new population Dpop through the iterative optimization process; and outputting a global optimal solution in the new population Dpop.

The application provides a big data feature portrait generation method based on multi-stage optimization, a medium and a system, and belongs to the technical field of feature engineering. According to the application, the information bottleneck compression model is used to evaluate feature importance, the Bayesian multiple imputation method is used to fill in missing values for high-value features, the robust Z-score standardization is performed on unified wide tables, and the wavelet multi-scale decomposition stabilization processing is performed on non-stationary time series features. The number of principal components is determined through progressive sampling and parallel analysis, a projection matrix is constructed to realize dimension reduction, the local adaptive Mahalanobis distance and shared nearest neighbor clustering are used in a low-dimensional subspace, the cluster feature portrait is extracted by backtracking to the original feature space, and when the density is uneven, hierarchical adaptive subspace division is performed to optimize clustering. The technical problem that the clustering result accuracy is insufficient due to uneven data quality in the feature portrait generation process of multi-source heterogeneous data in a big data scene is solved.

The embodiment of the invention discloses a vector data learning type indexing method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence and databases, the method comprises construction operation, retrieval operation and updating operation of an index structure, and the index structure is composed of a mapping relation table from cluster numbers to vector ID sets and a classification model. According to the construction operation, reference vectors in a vector data management system are divided, a mapping relation table from cluster numbers to vector ID sets is formed, and meanwhile, a classification model is trained to be used for predicting L-nearest neighbor clusters of query vectors. In the retrieval operation, candidate vectors are collected through one-time prediction of the classification model, and k designated candidate vectors with the closest distance are returned by calculating the preset distance between the query vector and the candidate vectors. The updating operation updates the index structure in real time according to addition, deletion, modification and change of the reference vector. The invention provides a construction method and a dynamic updating mechanism of a vector data learning type index structure, and more efficient approximate nearest neighbor search is realized.

A robustness evaluation method for film holes based on flow parameter dimensionality reduction is proposed. This method is used to evaluate the robustness of cooling performance under the influence of laser-drilled film hole defects. The method consists of two uncertainty analyses, namely, prior estimation and deviation correction. The basic film hole defect uncertainty analysis framework is composed of the laser-drilled film hole defect model, the non-invasive chaotic polynomial uncertainty quantification method, and the k-nearest neighbor clustering algorithm to evaluate the robustness of performance under consistent film hole defects. Deviation correction includes extraction of main flow features, probability distribution evaluation of single film hole flow parameters, probability evaluation of total flow parameters, and flow parameter correction uncertainty quantification analysis. This method is easy to use, widely applicable, flexible in evaluation and analysis, and has low computational cost. In addition, this method is compatible with data results from multiple sources, which is more in line with the needs of aircraft engine turbine design engineers.

The application provides a seismic attribute fusion method based on a thin-plate spline base function, comprising the following steps: step 1, calculating an attribute value set X of all seismic attributes participating in fusion; step 2, defining a thin-plate spline base function; step 3, establishing an initial network; step 4, calculating the distance of sample data to a cluster center; step 5, calculating the nearest neighbor cluster of the kth sample data x k ; step 6, forming an activation function of the thin-plate spline base function; step 7, establishing a network learning error judgment function; step 8, correcting a training weight coefficient, and completing seismic attribute fusion calculation according to the trained weight coefficient w i . The seismic attribute fusion method based on the thin-plate spline base function is more consistent with geological characteristics, meets the requirement of nonlinear seismic attribute fusion on a mathematical model, solves the nonlinear relationship fusion processing of seismic attribute data, and can enhance the accuracy of seismic attributes in reservoir prediction.

This invention discloses a method, system, and electronic device for biochar identification, relating to the field of biochar detection and pattern recognition technology. The method includes: inputting the physicochemical properties of a target individual into a biochar identification model to obtain the target biochar's identification information; wherein the process of determining the biochar identification model involves: performing outlier detection and standardization on the sample input variable data matrix, and constructing a feature data matrix based on the processed sample input variable data matrix; obtaining a random subspace nearest neighbor clustering ensemble learning classifier based on the feature data matrix, the sample identity multi-class label column vector, and a random subspace nearest neighbor clustering ensemble learning algorithm; the random subspace nearest neighbor clustering ensemble learning classifier is the biochar identification model. This invention can efficiently and accurately identify biochar identification information.

The application discloses a kind of based on nearest neighbor clustering forecast rolling mill machine hour output method, comprising: obtaining the real-time rolling data of each material;Based on rolling time, the interval time, rhythm time and machine hour output of each material are calculated, and machine hour output information table is established;Based on material steel grade, material weight and product specification setting classification condition, using nearest neighbor clustering algorithm based on classification condition real-time to material is classified, and rolling mill machine hour output grading table is established;Real-time update each kind of product specification rolling mill interval time, rhythm time and machine hour output data and establish rolling mill machine hour output gear information log table;Obtain the information of material to be rolled, and based on each table, unknown rolling mill machine hour output is predicted.The application method is according to the characteristics of many rolling mill rolling product specifications, using the method of nearest neighbor clustering to integrate and cluster within the range of difference allowed specification, facilitate production forecast and production arrangement etc.Work is carried out, effectively improve production efficiency.