559 results about "Data cluster" patented technology

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.

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 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 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 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 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.

The apparatus employs adaptive machine learning for optimization using dynamically variable local parameters. It consists of a processor and memory. Initially, it access a first dataset corresponding to the first phenomenon and a second dataset corresponding to the second phenomenon. Then, it identifies a dependency relationship between at least one data cluster of the first phenomenon and at least one data cluster of the second phenomenon. Using the at least a processor, modify a processor, an attribute set, as a function of the second data cluster. Further, it optimize a target data cluster, as the function of the first phenomenon. Last, it modify using the at least a processor, the second data cluster as a function of the target data cluster.

The invention relates to the technical field of information management, in particular to a smart agriculture precision big data information management system, which comprises a growth index extraction module, a stage data clustering module, a nutrient fluctuation identification module, an agricultural material strategy generation module and a scheduling information scheduling module. According to the method, the acceleration criterion is constructed based on the continuous period leaf area index change sequence, transition node identification is completed in combination with the photosynthetically active radiation utilization rate and the nitrogen absorption rate change trend, accurate positioning of crop growth stage change time points is achieved, the stability of stage classification in a space region is enhanced, and the accuracy of crop growth stage classification is improved. The agricultural material putting grade is judged and the fertilization frequency and dosage are blended by analyzing a root zone soil nutrient concentration difference value and a rate fluctuation frequency detection mode in combination with moisture content and root activity conditions, and operation period adjustment is guided in a task schedule in cooperation with a meteorological element fluctuation rate. The precision of farmland crop stage identification and the timeliness of nutrient fluctuation response are integrally improved.

The invention discloses a multi-dimensional time series data dynamic evolution analysis method and device, a medium and electronic equipment. The method comprises the steps that time series data are obtained and subjected to structured preprocessing; performing multi-dimensional quantization and attribute endowing on each piece of time series data, and constructing a three-dimensional data node containing a space coordinate, an initial weight and a timestamp; based on the set of the three-dimensional data nodes in a specific time window, constructing a three-dimensional data density field capable of representing data hotspot distribution and intensity in the time window; in the three-dimensional data density field, identifying a density peak region as a core data cluster, calculating the cluster mass according to the density and the coverage range of the core data cluster, and calculating the association strength among different core data clusters; and serializing a plurality of continuous time window data field state snapshots to form a dynamic evolution view capable of showing data hot spot generation and extinguishing and data cluster fusion and splitting processes. According to the invention, abstract multi-dimensional data is converted into a visual three-dimensional density field.

The invention provides a production control method and system based on SPC alarm linkage process locking, and relates to the technical field of quality management and control, and the method comprises the steps: deploying an SPC sensing node used for collecting technological parameters at a cutting edge position where a machining tool of production equipment is in contact with a workpiece, or at a contact point where a probe of online measurement equipment is in contact with the surface of the workpiece; the method comprises the following steps: acquiring process parameter data of a key production process in real time through an SPC sensing node deployed at a cutting edge position or a contact point; inputting the process parameter data as real-time data or small sample expansion data into an SPC judgment engine; and performing statistical process control analysis on the real-time data or the small sample expansion data through an SPC judgment engine so as to model data clusters which are densely distributed and have stable parameters in the data into a virtual reference curved surface representing a normal state domain. According to the invention, the accuracy of abnormity determination can be improved, and timely management and control of quality abnormity and minimization of production loss can be realized.

The invention discloses a message-driven enterprise multi-service system centralized to-do processing method and system, and the method comprises the steps: S1, obtaining resource investment and task processing data, and obtaining a resource consumption and task state cluster through clustering; s2, using a regression algorithm to analyze correlation between the two, and determining a resource utilization rate change trend; s3, when the threshold value is exceeded, calculating a task completion rate statistical characteristic to obtain a resource optimization index; s4, constructing an interactive model by using a decision tree in combination with historical data, and judging an input strategy adjustment point; s5, rendering visual resource distribution; s6, abnormal tasks are screened, and an optimization area is determined; and S7, updating an instrument panel to obtain a decision view. Through data clustering, correlation analysis and dynamic adjustment, the resource and task matching degree is improved, and decision scientificity and efficiency are enhanced.

The invention relates to the technical field of data processing, and discloses a hydrocephalus correlation analysis method and system based on multi-dimensional data. The method comprises the steps that a feature matrix is generated by obtaining and standardizing hydrocephalus multi-dimensional data, after the matrix is discretized, an association rule mining algorithm is adopted to extract an association mode, a rule set is obtained in combination with knowledge graph verification, data clusters are divided based on mahalanobis distance clustering, mixed effect time sequence model fitting parameters are established for all the clusters, and a mixed effect time sequence model is obtained. And calculating a target sample attribution cluster and predicting a symptom improvement trajectory and a confidence interval. According to the method, automatic integration, credible association rule extraction, precise subtype division and individualized symptom trajectory prediction of the multi-dimensional data of the hydrocephalus patient are realized.

The invention relates to the technical field of fan early warning, in particular to a fan intelligent early warning method and system based on a self-adaptive scheme and computer equipment. Multi-frequency-band heterogeneous data are collected in real time through multiple sensors, and four-dimensional tensor data are constructed. And carrying out single-factor evaluation on the environmental risk and the fan state risk. And fusing the two types of risks by using a double-layer fusion mechanism to obtain a fusion risk value. And setting different risk probability early warning thresholds according to fan operation requirements, comparing the fused risk value with the different risk probability early warning thresholds, and judging the state of the fan. And once the risk value exceeds the threshold value, identifying the high-risk data cluster, associating the fault mode library to determine a potential risk mode, and triggering a corresponding early warning signal so as to timely process and ensure the safe operation of the fan. The real-time and accurate early warning of the potential risk of the fan is realized, and the safety and reliability of the operation of the fan are improved.

The invention relates to the technical field of equipment fault prediction, in particular to a compressor mixed fault prediction method and system based on LSH and LSTM, and the method comprises the steps: data collection, preprocessing, LSTM model construction and training, incremental learning and fault prediction. Data preprocessing is combined with median filtering and wavelet denoising, and similar data clusters are formed through LSH processing; constructing an attention layer-containing LSTM network, and highlighting key features through attention scores; when the state of the compressor changes, new data are matched with corresponding data clusters through LSH, and only correlation model parameters are finely adjusted; the system comprises a data acquisition module, a preprocessing module, a model training module, an incremental learning module and a fault prediction module. According to the invention, data processing efficiency and model adaptability are improved, real-time performance and accuracy of fault prediction are enhanced, and equipment maintenance cost is reduced.

The invention relates to the technical field of big data, and provides a data verification method and device in big data cluster migration, equipment and a medium, and the method comprises the steps: obtaining a source data cluster and a target data cluster; dividing the source data cluster and the target data cluster into a plurality of data layers according to a standard data warehouse layering strategy, and binding different data comparison strategies for each data layer; establishing a fingerprint baseline table and constructing a sampling model feature library; performing data fragmentation processing on each data layer, and controlling each data layer to execute a data comparison task in parallel; and if any data layer of the target data cluster and any data layer of the source data cluster are detected to have difference, recording difference data and performing data restoration. According to the method, the source data cluster and the target data cluster are subjected to hierarchical parallel fragmentation comparison, a plurality of fragments of each layer are subjected to data comparison tasks in parallel at the same time, different comparison strategies are bound to the data layers, the comparison process can be achieved, and the overall data processing efficiency is improved.

The invention belongs to the technical field of urban meteorological data processing and spatial information calculation, and particularly relates to an urban meteorological gridding data construction method and device based on spatial interpolation. Comprising the following steps: clustering meteorological data: carrying out partition optimization by utilizing an objective function during clustering; partition interpolation: according to a meteorological data clustering result, carrying out spatial division and processing on regions, carrying out statistics on the number of base stations for each region, and determining region types based on the spatial distribution density of the base stations; determining a global variation function and a region variation function for each region, and calculating an estimated value of a to-be-interpolated point by adopting a corresponding Kriging interpolation method for different region types based on the variation functions; region junction point interpolation: for points near different region junctions, interpolating by using the meteorological data of the related region base stations, and weighting to obtain a final result; finally, urban meteorological gridding data construction based on spatial interpolation is realized.

The invention relates to the technical field of medicine management, and discloses a medicine informatization whole-process management system based on big data analysis. According to the system, a drug full-life-cycle multi-dimensional data stream is captured through a distributed sensing node network, and a drug state tensor is generated after noise filtering and missing value compensation are carried out through a real-time stream processing engine. The risk identification module scans and locates an abnormal data cluster through a multi-scale mode and constructs a drug quality hypersurface model; and the data aggregation module establishes a mapping relation between a state tensor and a quality model by using a space-time diagram attention network and calculates a global consistency metric value. The intelligent decision-making module generates an optimal drug management action sequence based on the measurement value under multiple constraint conditions, the closed-loop control module executes the action sequence and monitors system state deviation in real time, data acquisition parameters are dynamically calibrated, and autonomous optimization and closed-loop management of the whole process of drugs are achieved.

The invention discloses a power system network abnormal data clustering vulnerability detection technology, and belongs to the field of power system network security. Aiming at the problems of insufficient high-dimensional data processing capability, poor dynamic adaptability, high false alarm rate and the like of a traditional anomaly detection method, a detection technology based on an improved FCM algorithm is provided. According to the technology, through a dynamic feature weight matrix design module, a time adaptive membership updating module, a physical constraint regularization module, an incremental density peak initialization module and the like, an intelligent clustering framework fusing spatial-temporal features and physical constraints is constructed. And extracting multi-dimensional features by using GCN and Transform, dynamically adjusting the weight in combination with an attention mechanism, and introducing a time decay factor and a fuzzy logic constraint to realize accurate clustering. Experiments show that when the technology is applied to a provincial power grid, the anomaly detection accuracy is improved to 94.3%, the decision generation time is shortened to 14.8 ms, the constraint violation rate is reduced to 2.1%, and the real-time performance and reliability of power grid safety protection are remarkably improved.

The present application belongs to the technical field of power transmission line engineering cableway design and construction, and particularly relates to an engineering cableway stockyard point spatial data clustering method and system based on road feature enhancement. The method comprises the following steps: S1, road feature extraction: extracting road center line feature data, road average width feature data, and road unit direction feature data; S2, road feature enhancement processing: calculating boundary minimum distance feature data, transverse offset feature data based on cross product, and longitudinal projection position feature data; S3, spatial data clustering: based on the enhanced road features, using the K-means clustering algorithm, the stockyard points are automatically divided into left stockyard point set and right stockyard point set; S4, spatial consistency optimization: based on the principle of geographical spatial continuity, the outlier point label is corrected through the neighbor point voting mechanism. The accuracy and generalization of the spatial data clustering of the stockyard points of the power transmission line engineering cableway are improved.

The invention discloses a self-adaptive K-Means clustering method based on a learning enhancement framework, and belongs to the technical field of search engine clustering. The problem that the query performance obtained based on an existing method is poor due to the fact that the data access frequency is not considered in the existing method is solved. According to the method, initial classification is carried out based on an external predictor, the initialization quality can be improved, the access frequency of data in a search engine serves as a weight, a guiding effect can be provided for candidate centroid selection and dimension sampling, it is ensured that hot spot samples get full attention in initial classification, the clustering effect is ensured, and the clustering efficiency is improved. And searching is performed according to the clustering result, so that the query performance can be improved. In addition, redundancy calculation can be reduced through the design of the jump window, unnecessary data points are skipped, and therefore the clustering speed is increased. According to the method, rapid and accurate K-Means clustering can be realized on each search engine data set, and the method can adapt to various query load modes. The method can be applied to data clustering in a search engine.

The invention relates to the field of automobile part data processing, in particular to a material data processing method and system. The method comprises the following steps: acquiring material data of a plurality of automobile parts; and clustering the material data to obtain a final cluster, and managing the material data of the automobile parts according to the final cluster. According to the technical scheme, the accuracy of material data clustering of the automobile parts can be improved, and the precision of a data processing result is improved.

The invention discloses a slope anchor cable prestress measuring method based on data clustering. The slope anchor cable prestress measuring method comprises the steps that a multi-dimensional database containing environment characteristics, echo energy and counter-pulling method prestress values is constructed; a mixed distance clustering algorithm is designed, numerical features and classification features are jointly processed, and automatic data classification is achieved by iteratively updating a cluster center; the cluster quality is dynamically evaluated based on the contour coefficient, and only when the sample size is gt; 3 and an average contour coefficient gt; constructing an exclusive regression model in the cluster when the contour coefficient is a threshold value; and finally predicting the anchor cable prestress in real time through echo energy. According to the method, the prediction precision under the complex working condition is remarkably improved, the use frequency of the reverse drawing method is reduced, and safe and efficient prestress nondestructive testing is achieved.

Systems and methods to identify a data cluster for an input data record based on a spatial distance to a representative subset of data records corresponding to the data cluster. The representative subset of data records may be identified based on a spatial distance to other records in the cluster.

The embodiment of the invention provides a business rule classification method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining business rule feature data, and constructing a knowledge graph ontology model based on the business rule feature data subjected to preprocessing operation; through a knowledge graph ontology model, entity information and a relation triple of the service rules are extracted from the preprocessed service rule feature data, and the similarity between different service rules is determined by adopting the entity information and the relation triple; performing clustering operation on the service rules based on the similarity between different service rules to obtain a service rule data clustering result for the service rules; the business rules are classified based on the business rule data clustering result, and the classification result for expressing the association relationship among different business rules is generated, so that comprehensive, accurate and rapid understanding, association analysis and classification of complex business rules are realized, and the correctness and the working efficiency of data processing are improved.

The invention provides a virtual power plant optimization scheduling method, device and equipment based on multi-objective optimization. The method comprises the following steps: acquiring historical power consumption data of each user in a target virtual power plant; clustering the historical power consumption data to obtain a plurality of power consumption data clusters and a clustering center corresponding to each power consumption data cluster; based on the plurality of power consumption data clusters and the clustering center corresponding to each power consumption data cluster, determining the weight of each power consumption data cluster and the power consumption demand of the target virtual power plant at the future moment; obtaining a scheduling target value based on the power consumption demand, the power storage quantity of distributed energy storage of the target virtual power plant and the weight of each power consumption data cluster; and determining a scheduling scheme of the target virtual power plant at the future moment by taking the lowest cost of the target virtual power plant and the minimum influence of the target virtual power plant on the power grid as targets and taking the scheduling target value as a constraint. The optimization scheduling scheme can be obtained more accurately and quickly, the dependence of the target virtual power plant on the power grid is reduced, and the stability of the power grid is maintained.

The invention relates to a lung cancer drug resistance risk assessment system based on multi-modal data and a lightweight network, and relates to the technical field of lung cancer drug resistance risk assessment, and the system comprises a data clustering division module which is used for obtaining sample multi-modal data and sample drug resistance data of historical patients, extracting a response distribution mode for clustering division, and obtaining a plurality of response subgroups; the network library construction module is used for training a plurality of specific evaluation networks to form a specific evaluation network library; the mode matching retrieval module is used for acquiring a target response distribution mode of the target patient and performing network matching; and the drug resistance risk assessment module is used for acquiring target multi-modal data of the target patient, performing risk assessment in combination with the network matching result, and generating a drug resistance risk assessment result. The problems that a traditional evaluation method mainly depends on single-mode data, information is single, evaluation accuracy is poor, and individual differences of patients are difficult to adapt are solved.

The invention belongs to the field of submarine pipeline risk early warning, and relates to a submarine oil and gas pipeline burying state identification method based on unsupervised machine learning and 3D Hough transformation. The method comprises three parts of pipeline data acquisition, pipeline point cloud data clustering, and pipeline state detection and identification, wherein the pipeline point cloud data clustering part comprises the following steps: firstly, removing most noise points in point cloud data by using cloth filtering and statistical filtering, and then clustering the point cloud data by using a DBSCAN algorithm of a self-adaptive threshold value; the higher the density is, the higher the class interestingness is. According to the pipeline state detection and identification part, 3D Hough transformation is used for carrying out cylinder and circular truncated cone detection on interest classes, and the exposed state of the pipeline is determined according to different detected results.

The invention provides a rapid calculation and prediction method, device, equipment and medium for a soft melting zone of a blast furnace, and the method comprises the following steps: collecting blast furnace operation data, carrying out standardization processing, dynamically establishing a geometric model, constructing simulation models of grids with different resolutions, and carrying out hierarchical solving based on a historical sample matching condition to realize efficient prediction. When the matching degree is insufficient, the first resolution grid is adopted to quickly calculate and generate first interpolation data, when the matching degree is between basic matching and complete matching, second interpolation data is acquired in real time through the matching sample, the first or second interpolation data is mapped to the second resolution grid to be initialized, accurate calculation is carried out, and the first interpolation data and the second interpolation data are obtained. Fast and accurate calculation adopts a gas-solid single-core interaction parallel partitioning strategy to accelerate solution, a matching working condition result is directly reused when the matching degree meets complete matching, a calculation result and a standardized parameter are synchronously stored in a historical sample library, and data clustering matching and visual output are realized; the method effectively supports blast furnace smelting state monitoring, operation optimization and stable and smooth operation.

The invention discloses a structural entropy guided esophageal cancer multi-modal data discriminative representation learning method, and belongs to the technical field of medical image engineering and the field of semantic understanding. The method comprises the following steps: collecting esophageal cancer multi-modal data and executing preprocessing operation; constructing a regularization framework based on the structure entropy; inputting the preprocessed esophageal cancer multi-modal number into the constructed regularization framework based on the structure entropy, and carrying out single-modal task classification clustering based on the structure entropy; and constructing an unsupervised multi-modal clustering framework guided by the structural entropy, and inputting the preprocessed esophageal cancer multi-modal number into the unsupervised multi-modal clustering framework guided by the structural entropy to perform multi-modal data clustering so as to complete esophageal cancer multi-modal data discriminative representation learning. According to the method, a structure entropy theory is introduced, a three-layer coding tree is constructed, the problem that esophagus cancer data structure information is not sufficiently utilized in a traditional method is solved, and the collapse representation problem in an unsupervised or semi-supervised scene is solved by designing different strategies for different tasks.