1002 results about "Data cluster" patented technology

The invention provides a cold and hot data object life cycle feature extraction method based on deep learning, and the method comprises the steps: carrying out the clustering of feature vectors through employing a clustering algorithm, dividing the data into different viscosity level data clusters according to a clustering result, and enabling the data in each viscosity level data cluster to have similar access modes and life cycle features; for each viscosity level data cluster, analyzing the access time distribution and life cycle stage of the data cluster, and judging the heat type to which the data cluster belongs, including continuous heat, periodic heat, temporary heat and cold; according to the current popularity type and the historical access mode of the viscosity level data cluster, predicting the popularity change trend of the viscosity level data cluster in a future period of time, and obtaining a popularity change curve of each cluster; by monitoring the access condition and the heat change of the data in real time, the migration and replication modes of the data between different storage layers are dynamically adjusted, and when the actual access mode of the data is deviated from the predicted mode, data re-clustering and storage optimization are triggered.

The invention provides a novel power system multi-scale network construction energy storage planning optimization method based on source network load clustering. The method comprises the following steps: firstly, processing source network load data by adopting a multi-dimensional adaptive feature extraction and fusion method, and realizing accurate processing of the data through interval adaptive probability density estimation, adaptive kernel tensor decomposition and marginal information entropy maximization; then, a multi-level data clustering method is adopted, and accurate clustering is achieved through hierarchical distance measurement and boundary self-adaptive adjustment; then, a multi-scale energy storage modeling method is adopted, and accurate modeling of the energy storage system is achieved through the dynamic characteristic matrix and self-adaptive topology embedding; and finally, a multi-objective optimization solution method is adopted, and efficient solution of an optimization problem is realized through adaptive hierarchical decomposition and hybrid evolutionary optimization. According to the invention, the accuracy and reliability of energy storage planning can be obviously improved, and the system operation efficiency is improved.

The invention belongs to the technical field of computers, and particularly relates to an information integration management method and system supporting real-time updating, and the method comprises the steps: accessing a heterogeneous data source through a distributed gateway, carrying out the adaptive analysis of a protocol, and standardizing a structure; adding a millisecond timestamp and a unique identifier, and generating a semantic data cluster through streaming engine sliding window association analysis; after multi-dimensional consistency verification is executed, writing and storing are carried out by adopting a double-buffering and snapshot isolation mechanism; and dynamically assembling the customized view according to the user role and the context and safely returning the customized view. The system correspondingly comprises a protocol analysis module, a time sequence marking module, a streaming association module, a semantic verification module, a double-buffer control module, an index updating module, a view assembling module and a secure transmission module. According to the invention, millisecond-level real-time convergence, conflict-free fusion and on-demand view output are realized, and the data access flexibility, the quality guarantee capability and the service adaptation flexibility are comprehensively improved.

A method for diagnosing diseases in human patients can include providing a set of global X-ray diffraction (XRD) data to a global data center (GDC) and processing the set of global XRD data and categorizing it into data clusters, where each data cluster corresponds to a diagnostic indicator for assessment of a physiological or pathological condition. The method can further include communicating local XRD data and local patient data from a local autonomous cell (LAC) to the GDC, where the local XRD data includes information about a tissue sample measured using a tissue diffractometer of the LAC, and wherein the tissue sample includes skin. The method can further include processing the local XRD data, comparing it with the data clusters to determine a local diagnostic indicator for the local patient, and communicating the local diagnostic indicator to the LAC.

The present application provides a data clustering method and apparatus, a system and a computer-readable storage medium. The method comprises: acquiring a node feature vector of each node and a node relationship network, the node feature vectors indicating attribute features of entity objects, and the node relationship network indicating a real interaction relationship between the entity objects; and in a hyperbolic space, generating node representations according to the node relationship network and the node feature vectors, and outputting a clustering result on the basis of the node representations. Using the method can fully capture contained low-order and high-order topological structure information in the hyperbolic space, thus achieving a better clustering effect.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine heterogeneous data visualization processing method combined with artificial intelligence, and the method comprises the steps: collecting multi-source heterogeneous data above and under a coal mine to construct a real-time heterogeneous data matrix; feature categories are divided based on data dynamic relevance, and stable data areas are screened through a dynamic analysis window to generate an optimization matrix; performing pattern recognition on the unstable region to extract an abnormal data cluster, and calculating an abnormal feature factor of a data point; analyzing the feature distribution difference to generate a stable feature index, and calculating an abnormal risk value in combination with the distance, the abnormal feature factor and the stable feature index; and mapping the center coordinate of the stable region to a three-dimensional space of the coal mine, and positioning a key information region in combination with the abnormal risk value. An apparatus includes a memory, a processor, and a computer program. The coal mine heterogeneous data processing efficiency and the safety monitoring accuracy are improved, intelligent positioning of the key information area is achieved, and powerful support is provided for coal mine safety production.

The invention relates to the technical field of intelligent power supplies, in particular to an intelligent power supply for an unattended station. Comprising a multi-source power supply scheduling unit; an intelligent energy storage management unit; a double-link communication unit; the double-link communication unit is used for constructing a multi-redundancy communication link of wired, wireless and power line carriers, and the double-link communication unit is based on link quality data, adopts an automatic switching strategy to guarantee data transmission continuity, preprocesses monitoring data by using an edge side data clustering algorithm, and realizes offline data caching and transmission flow optimization. Through a multi-source energy dynamic scheduling strategy, according to photovoltaic real-time output, commercial power time-of-use electricity price and load electricity demand, power supply paths of photovoltaic, commercial power and a storage battery can be dynamically optimized, the consumption proportion of clean energy is improved, the dependency degree of high-cost commercial power is reduced, a complex energy structure of photovoltaic, commercial power and storage battery multi-energy complementation is effectively adapted, and the energy utilization rate is improved. And power supply flexibility is enhanced.

The invention relates to the technical field of construction safety management, and discloses a construction engineering site construction monitoring management method and system, and the method comprises the steps: obtaining an original data set, carrying out the data preprocessing according to the original data set, obtaining a preprocessed feature data set, carrying out the sequential optimization of the preprocessed feature data set, and obtaining a feature data set; the method comprises the steps of optimizing a data uploading sequence to obtain an optimized data uploading sequence, performing time synchronization on the optimized data uploading sequence to obtain a unified time reference data set, performing spatial calibration on the unified time reference data set to obtain a spatially calibrated data set, and performing data clustering analysis on the spatially calibrated data set to obtain a data clustering result. And obtaining a region-level data deviation analysis result, and performing abnormal region identification according to the region-level data deviation analysis result to obtain an abnormal region report. According to the method, the abnormal region can be dynamically identified.

The invention discloses a comprehensive online monitoring system for a high-voltage cable of an electrified railway, and particularly relates to the technical field of cable monitoring. The clustering analysis module divides similar groups based on factory and use environment data; the prediction mode selection module evaluates the stability and nonlinearity degree of the environmental data, and selects an optimal prediction method to predict the environmental data; the threshold value judgment module selects a fixed or dynamic threshold value to judge a fault through comparison with similar group data; the dynamic adjustment module optimizes a fault threshold based on historical data and improves the detection precision; the alarm module triggers early warning when the monitoring data exceed a threshold value; according to the method, the operation mode of the cable is accurately matched, the monitoring precision is improved, misjudgment caused by a single data source is avoided, it is ensured that the optimal prediction method is always used under different environmental conditions, the prediction precision of future environmental data is improved, and reliable support is provided for fault judgment.

The invention relates to the technical field of data processing and analysis, and discloses a cost consultation data arrangement and analysis system based on a cloud platform, and the system comprises a data collection module which is used for obtaining and standardizing original cost data; the data clustering module is used for clustering data according to specific dimensions and extracting key feature vectors; the strategy generation module is used for constructing candidate analysis strategies; the strategy optimization module is used for screening an optimization strategy; and the execution engine module processes the data according to the optimization strategy to generate an analysis result. In addition, a data verification module is arranged to verify results, a dynamic adjustment module is arranged to monitor data changes, and a knowledge graph module is arranged to construct an entity relation graph. The system can solve the problems of data source heterogeneity, single analysis strategy and the like in cost data processing, realizes efficient data processing and accurate analysis, ensures that the result is accurate and reliable, and improves the data processing and analysis level of the cost consultation industry.

A method for using machine learning models to automatically cluster data into meaningful populations and groups and to identify hidden patterns and structures in such data is provided. The method includes: receiving first information that relates to a group of entities; analyzing the first information with respect to a predetermined set of parameters and a predetermined set of data types; selecting, based on a result of the analysis, a first machine learning (ML) model from among a predetermined set of ML models; and using the selected first ML model to generate a report that includes second information that relates to at least one data cluster identified by the first ML model from the first information.

The invention discloses a high-dimensional time series data cluster structure prediction method based on dynamic hierarchical clustering and LSTM fusion. The method comprises the following steps: receiving a high-dimensional heterogeneous time series data set; performing clustering processing on the high-dimensional heterogeneous time series data set to obtain a cluster structure and a centroid matrix at the current moment; based on the cluster structure and the centroid matrix, time sequence features are extracted from three dimensions of a centroid track, a topological structure and scale dynamic, and a multi-channel time sequence feature tensor is generated through coding; and inputting the multi-channel feature tensor into the LSTM, and outputting a prediction result of the centroid offset and the topology change probability at the next moment by applying an attention gating mechanism. According to the method, the system and the equipment provided by the invention, efficient clustering and accurate prediction of high-dimensional heterogeneous time series data are realized through deep fusion of dynamic hierarchical clustering and LSTM, and the timeliness, the accuracy and the calculation efficiency of time series data processing are remarkably improved.

The invention discloses a drainage basin ecological anomaly monitoring method based on clustering processing. The method comprises the steps of data acquisition, drainage basin ecological mapping, radius optimization, initial clustering center selection, drainage basin ecological data clustering processing and drainage basin ecological anomaly monitoring. The invention belongs to the field of ecological monitoring, and particularly relates to a clustering processing-based drainage basin ecological anomaly monitoring method, which comprises the following steps of: introducing a time weight coefficient to construct a time fusion feature vector, and improving the sensitivity to an emergency; the sampling density is automatically adapted by iteratively adjusting the radius, and the monitoring accuracy is improved; introducing a time change factor, constructing a composite index, selecting a core reference point, and objectively emphasizing the most representative core position; the sample weight is introduced to strengthen anomaly monitoring, the attention degree on different indexes is continuously adjusted through core reference point guidance, and the influence of important ecological variables is improved; the weight is updated based on the index stability, the contribution degrees of different indexes to anomaly monitoring are distinguished, and then the anomaly monitoring effect is improved.

The invention relates to the technical field of spatial transcriptome data clustering analysis, and discloses a spatial transcriptome data clustering method based on progressive learning and multi-modal fusion. According to the method, a high-order structure relationship among sample points is captured by using a multilayer graph convolutional network, and various noises of gene expression data are removed to the greatest extent through comparative learning and a complementary mask mechanism; meanwhile, gene expression data, spatial position information and histological image information are ingeniously fused to the maximum degree through two times of cross attention, and then fusion feature latent representation Latent is obtained. Compared with a mainstream spatial transcriptomics clustering analysis method, the method has the advantages that clustering based on the fusion feature latent representation Latent shows higher capability and excellent clustering performance, and has remarkable robustness.

The invention relates to the technical field of enterprise data analysis, and discloses an enterprise data clustering processing method and system based on NLP and machine learning, and the method comprises the steps: S1, obtaining structured data and unstructured text data of an enterprise; s2, carrying out standardization processing on the structured data, extracting key business indexes, and forming structured feature vectors; the system comprises a data acquisition and preprocessing module, a multi-modal feature construction module, a causal semantic alignment module, a tensor decomposition module, a graph modeling module, a clustering module and an anti-fact reasoning module. The comprehensiveness and accuracy of enterprise behavior analysis are improved through multi-modal data fusion, a graph neural network and a causal reasoning technology; modeling by utilizing a dynamic graph and a causal relationship, and deeply mining an enterprise transaction relationship; and more accurate enterprise risk early warning is realized through anti-fact analysis and a risk scoring mechanism.

The invention provides an scRNA-seq data clustering method, system and device based on ZINB distribution and graph attention. The scRNA-seq data clustering system mainly comprises three core modules: a ZINB auto-encoder, which is used for modeling scRNA-seq data based on zero-expansion negative binomial distribution, generating robust potential representation through a denoising auto-encoder, and accurately capturing sparsity, excessive discreteness and shedding events of gene expression; the residual image attention auto-encoder is used for constructing a cell relation graph by using a Pearson correlation coefficient, dynamically learning a neighborhood weight in combination with a multi-head attention mechanism, retaining original features through residual connection, and relieving the excessive smoothness problem of image convolution; and a deep clustering model is self-optimized: target distribution and soft label distribution are minimized through KL divergence, and end-to-end joint optimization of embedded learning and clustering is realized.

The invention relates to an intelligent industrial data clustering and grouping method, system and device and a medium, and the method comprises the steps: carrying out the standardization preprocessing of collected original industrial data, obtaining standard industrial data, and constructing a high-dimensional feature space according to the standard industrial data; performing industrial feature extraction and dimension reduction on the high-dimensional feature space to obtain a low-dimensional feature vector; performing preliminary grouping on the low-dimensional feature vectors according to a preset clustering algorithm to obtain a first clustering result; performing secondary adjustment on the first clustering result to obtain a second clustering result; and performing industrial scene simulation verification on the second clustering result to obtain a verification result, and performing optimization adjustment on the second clustering result according to the verification result to obtain a target clustering combination. According to the method, a more reasonable clustering result can be obtained, and the accuracy and applicability of the clustering effect are improved.

The invention belongs to the technical field of financial risk control systems, and particularly relates to an isolated forest abnormal data detection method and system based on clustering enhancement and a medium. S2, a K-Means clustering algorithm is adopted to screen out data clusters needing to be further detected; s3, constructing an isolated forest model according to the screened data clusters, and performing model training through a training set; s4, predicting the model through the test set, and calculating an initial abnormal score; the class density is calculated to serve as a weight to weight the initial abnormal score, and a final abnormal score is obtained; s5, judging the final abnormal score according to a preset threshold value, and outputting an abnormal data detection result; and S6, evaluating the isolated forest model, adjusting model parameters according to an evaluation result, and continuously optimizing the model until a preset requirement is met. The problems that abnormal data detection is complex in calculation and low in efficiency are solved.

The invention relates to the technical field of single cell data processing, in particular to a database visualization method based on single cells. The method comprises the following steps: acquiring a single cell data set; standardizing the single cell data set to obtain standardized single cell data; performing high-dimensional feature extraction on the standardized single-cell data to obtain single-cell high-dimensional feature data; and performing nonlinear dimension reduction processing on the single-cell high-dimensional feature data to obtain single-cell dimension reduction data. Performing data clustering on the single cell dimension reduction data to obtain single cell clustering data; performing gene differential expression extraction on the single cell clustering data to obtain differential expression gene data; multi-modal data fusion analysis is carried out on single cell data through a data processing technology, a feature mapping technology and a visualization technology; and dynamic weight division is carried out on the single cell database, so that the effect of visualization of the single cell database is enhanced.

The invention discloses a sewage treatment supervision method and system based on artificial intelligence control. The method comprises the steps of data acquisition, water quality prediction, water quality data clustering treatment and sewage treatment supervision. The invention belongs to the field of data processing, and particularly relates to a sewage treatment supervision method and system based on artificial intelligence control. According to the scheme, water quality deviation measurement and time decay factors are introduced for sampling point optimization, sampling points and time are finely adjusted, outdated data influences are avoided, then high-sensitivity sampling points are selected, and the accuracy of sewage treatment supervision is improved. The sewage treatment supervision accuracy is improved; according to the method, adaptive distance measurement is introduced, and by designing feature weights of environmental adaptability and timeliness response, the weights of different features can be adaptively adjusted according to environmental changes, so that an analysis result is more biased to real-time data, early warning and decision making in the water quality treatment process are more timely, the problem of relying on outdated data is avoided, and the water quality treatment efficiency is improved. And the sewage treatment supervision effect is improved.

The invention discloses a BIM-based construction engineering visual management method and system, and relates to construction engineering management, and the method comprises the steps: collecting a data set containing geometric information, attribute information and associated information; dividing the data set into a plurality of hierarchical subsets according to the hierarchical relationship of the building engineering BIM model; an improved DBSCAN clustering algorithm is adopted to perform clustering analysis on each hierarchy subset; classifying and archiving the data set according to the data cluster, and generating a semantic association data collaboration array A according to a classifying and archiving result; carrying out dimension reduction processing on the semantically associated data collaboration array A; constructing a spatial index of the dimension reduction data array by adopting a density-based spatial index method; and dividing the building engineering BIM model into a plurality of partitions by using a distributed computing framework, and performing distributed matching on the partitions and the spatial index to obtain a data matching point set. For the problem that the BIM-based building engineering high-dimensional data clustering precision is low in the prior art, the analysis precision is improved.

Apparatuses, systems, and techniques generating one or more cluster performance evaluation metrics that allow for evaluation of the performance of an unsupervised natural language processing clustering algorithms to be used with unlabeled data. At least one embodiment pertains to methods of generating one or more cluster performance evaluation metrics based, at least in part, on one or more vectors generated by one or more neural networks to indicate a relationship among members of one or more clusters of data generated using the one or more data clustering algorithms, according to various novel techniques described herein.

The controller 13 of the information processing device 1A functions as the acquisition unit, the cluster generation unit, and the ship direction estimation unit. The acquisition unit is configured to acquire measurement data which is a set of data representing a plurality of measured points measured by a measurement device. The cluster generation unit is configured to generate, based on normal lines of the measured points of a ship, one or more clusters of the measured points of the ship. The ship direction estimation unit is configured to estimate a direction of the ship based on a first cluster which is a cluster having a largest number of the measured points of the ship.

The present invention relates to a system and a method for forecasting charging time of electric vehicles. Charging event data is filtered to discard at least one piece of the charging event data or disable use of at least one piece of the charging event data. The remaining charging event data is processed for generating a data collection comprising sample data. A data cluster representing sample data associated with a selected electric vehicle cluster is obtained and a charging time forecasting model is generated on basis of the obtained data cluster using machine learning. A plurality of charging time forecasting models are trained based on a first portion of the data cluster. The best performing charging time forecasting model is selected on basis of a second portion of the data cluster, and the selected charging time forecasting model is tested based on a third portion of the data cluster. The charging time forecasting model is applied on a charging event of an electric vehicle comprised in the selected electric vehicle cluster.

The invention discloses a data dynamic partition storage method and system based on adaptive clustering, and relates to the field of data processing. The method comprises the following steps: S1, extracting multi-scale geometric features of a high-dimensional data set, calculating a local curvature and generating a curvature feature matrix; s2, constructing a feature distance matrix and a similar matrix based on the curvature feature matrix, and generating a low-dimensional embedding matrix; s3, executing self-optimization clustering according to the low-dimensional embedded matrix, determining a cluster number through singular value distribution, and generating an initial cluster; s4, calculating a stability factor of each cluster, and triggering cluster splitting or merging operation according to a threshold value to form updated cluster division; and S5, performing incremental processing on newly added data points, obtaining a new point local curvature through local curvature gradient correction, mapping the new point local curvature to a low-dimensional space, dynamically deciding affiliation based on a cluster radius, and updating partitions. Through multi-scale feature extraction, self-optimization clustering and incremental updating mechanisms, high-dimensional data clustering precision, robustness and calculation efficiency are improved.

The present disclosure relates to a partial discharge monitoring system and a partial discharge monitoring method that are capable of monitoring and determining a defect generated in a high-voltage power device in real time by classifying signals generated from the high-voltage power device with a machine learning algorithm being applied, and capable of easily forming feature dot data clusters in both a high-density area and a low-density area of two-dimensional feature dot data by performing a process of clustering feature dot points generated from the signals of the power device on the basis of density and distance in parallel, respectively, thereby generating PRPD data for each cluster without missing a signal to improve partial discharge determination accuracy.

Systems and methods improve performance of a classifier, which comprises a neural network and is trained through machine learning. First and second scores are computed, by the classifier, for each a multiple data examples from a generator. The first score is indicative of whether the data example belongs to a first data cluster and the second score is indicative of whether the data example belongs to a second data cluster. The generator is trained with an objective such that, for each data example generated by the generator, the first and second scores computed by the classifier are equal. Partial derivatives from the classifier are back-propagated for multiple data examples generated by the generator, to obtain a vector, for each data example, that is orthogonal to a decision surface for the classifier. A problem with the classifier is detected based on changes in directions of the vectors. Upon detecting a problem, the classifier is adjusted to reduce errors by the classifier caused by overfitting training data.

The invention belongs to the technical field of intelligent retrieval, and discloses an archive data intelligent retrieval method and system based on a multi-modal large model. The method comprises the following steps that a cloud data center builds an archive data intelligent retrieval platform, deploys a block chain network and builds an archive data intelligent retrieval engine; the cloud data center is used for carrying out data processing on the real-time multi-modal file big data; the cloud data center is used for generating a knowledge graph according to a plurality of real-time retrieval themes; the cloud data center is used for carrying out data clustering; the cloud data center uses a block chain network to carry out distributed storage; the user terminal is used for uploading real-time query data; the cloud data center is used for performing intelligent retrieval by using an archive data intelligent retrieval engine; and the cloud data center visually displays the target archive data. According to the method, the problems of low retrieval efficiency, insufficient accuracy, weak multi-modal data processing capability and insufficient security in the prior art are solved.

The invention discloses an intelligent cleaning processing method and system for multi-source heterogeneous data, and belongs to the technical field of data cleaning processing methods, industrial sensor time sequence data, long text log data and other high-dimensional industrial data are obtained, the obtained data are subjected to cleaning, missing value processing, standardization, text processing and timestamp alignment, and the obtained data are subjected to data cleaning, missing value processing, standardization, text processing and timestamp alignment. The method comprises the following steps: preprocessing time sequence data of an industrial sensor, inputting the preprocessed time sequence data of the industrial sensor into a Transform auto-encoder, and in a low-dimensional feature space output by the Transform, applying a density-sensitive clustering method, setting a neighborhood radius and a minimum point number, and identifying a data cluster formed in a data dense region and noise points in a sparse region.

The invention relates to the field of task queue scheduling of automobile chips, in particular to a test case dynamic priority scheduling method for automobile electronic chips. Comprising the following steps: analyzing standard test data of the automobile electronic chip, and organizing a structured data set associated with the physical layout of the chip; identifying failure clusters and noise points on the structured data set by using a data clustering algorithm; extracting geometric and position features of the failure cluster, classifying the failure cluster into a known failure mode by using a classifier, performing correlation analysis on the failure mode, an upstream key process step and an equipment log, and establishing a causal relationship; calculating a function security risk score for each chip; and when the function security risk score is higher than a preset risk threshold value, generating and executing a dynamic task rearrangement instruction, and dynamically improving the execution priority of the test case. According to the method, the priority is dynamically selected according to the function security risk score through the dynamic task rearrangement instruction, so that the test time is saved, and the coverage depth and quality of the test are improved.