301 results about "Data cluster" patented technology

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 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 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 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 relates to the technical field of anomaly detection, in particular to an industrial Internet of Things equipment behavior pattern anomaly detection method. Clustering the data points according to the time domain features and the frequency domain features of the data points in the local time period, and obtaining an outlier degree and a target data point according to the number features of the data points in the data cluster and the distance features between the data points and the maximum data cluster; obtaining a first confidence coefficient according to the data difference of the target data point in other data points in a preset neighborhood range in the clustering space, the number ratio of other data clusters to which the other data points belong, and the discrete feature of the target data point; obtaining a second confidence coefficient according to the data difference between the target data point and the same historical moment, the trend difference between the target data point and the same local time period corresponding to the same historical moment, and the data fluctuation in the local time period of the target data point; and obtaining the abnormal degree of the target data point according to the comprehensive confidence coefficient and the outlier degree, and detecting the behavior state of the equipment, thereby improving the accuracy of anomaly detection.

The invention relates to a consistent semantic enhanced single-step parameter-free multi-view clustering method, which comprises the following steps of: 1) automatically learning anchor points, and avoiding the problem that the quality of the anchor points is reduced due to the randomness of an anchor point selection strategy; (2) spectral clustering is converted into decoupling decomposition of a representation matrix, and single-step data processing is completed without depending on a subsequent clustering method; and 3) the method does not contain any parameter, so that the problem that the clustering quality depends on the parameter is eliminated. According to the method provided by the invention, the accuracy which can be achieved on a Dermatology data set is greatly improved on the same data set compared with the accuracy which can be achieved on the same data set through traditional anchor point-based multi-view subspace clustering. According to the method provided by the invention, the problem that parameters such as multi-view clustering anchor point selection are difficult to adjust can be effectively solved, the problem of optimizing flow splitting is solved, and the method does not need to depend on a subsequent clustering method, so that the multi-view data clustering precision is improved.

The invention belongs to the technical field of data mining and artificial intelligence, and discloses a high-dimensional data clustering and feature structure analysis method, which comprises the steps of 1, preprocessing high-dimensional data and outputting standardized data to obtain a preprocessed standardized data set, 2, constructing a Gaussian graph mixture model to perform unsupervised clustering, and 3, obtaining a feature structure of the Gaussian graph mixture model; the method comprises the following steps: step 1, carrying out classification on the data sub-groups, and outputting data sub-groups containing class labels, step 3, independently constructing a feature association network for each class of data sub-groups, and outputting a topological graph of the feature association network of each sub-group, and step 4, carrying out multi-dimensional graph theory index calculation and statistical test on the feature association network of each sub-group, and outputting a final analysis result. According to the method, the problem that high-dimensional complex manifold distribution data is difficult to process is solved, core features behind different categories and a dynamic interaction mechanism thereof can be visually displayed, spanning from sample division to mechanism revealing is realized, and the method can be widely applied to the fields of medical subtype discovery, financial risk conduction analysis, industrial fault diagnosis and the like.

The invention provides a data link multi-dimensional domain signal communication method, which comprises the steps of performing cross-domain association deconstruction on a data link space-time-frequency multi-dimensional signal and error feature data in an electromagnetic environment to obtain a space-time-frequency domain original feature set, and synchronously integrating signal propagation feature data and error source attribute data to generate a multi-source association data cluster, a space-time frequency multi-dimensional fusion feature topology is generated; a multi-dimensional domain collaborative error-resistant model is constructed based on space-time-frequency multi-dimensional fusion feature topology, dynamic weight distribution and error suppression strategy iterative optimization are performed on space-time-frequency domain error-resistant weight parameters and error suppression strategy parameters in combination with a space-time-frequency domain resource adaptation rule, and a multi-dimensional domain error-resistant regulation and control instruction is generated; and performing space-time-frequency collaborative optimization processing on a data chain transmission signal according to the multi-dimensional domain error-resistant regulation and control instruction to obtain an error-resistant optimization signal, synchronously acquiring transmission quality data of a link feedback signal, and performing reverse fine tuning on a multi-dimensional domain error-resistant regulation and control instruction parameter through a deviation between the transmission quality data and a preset quality threshold value.

The invention discloses a method and device for comprehensively optimizing test resources, electronic equipment and a storage medium, and belongs to the technical field of software test.The method comprises the steps that a test case set is classified through a high-dimensional data clustering algorithm, and a plurality of test case classes are obtained; selecting representative test cases from each test case class based on an improved greedy algorithm to form a target test case subset; performing priority ranking on the target test case subset by adopting a firefly group optimization algorithm to obtain a ranking result; and optimizing distribution and scheduling of test resources according to the sorting result so as to preferentially execute high-priority test cases and realize resource load balancing. By combining high-dimensional data clustering, an improved greedy algorithm and a firefly group optimization algorithm, the problem of large-scale test case management in the interconnection and interworking test of heterogeneous AC and AP equipment is solved, comprehensive optimization management of test resources is realized, and the test efficiency and the system quality are remarkably improved.

The invention provides a railway signal equipment fault prediction and diagnosis method, which belongs to the technical field of railway signal equipment, and comprises the following steps: acquiring multi-dimensional real-time data in the operation process of railway signal equipment, and storing the multi-dimensional real-time data according to a time sequence to form a super sparse historical data set; a hierarchical sparse data clustering index structure is established by adopting a multi-level partition clustering mode to carry out super-dilute clustering processing, an equipment fault feature vector library is established, feature vector similarity is calculated through a nearest neighbor search algorithm, and a corresponding fault prediction level is output. And carrying out correlation analysis by using the space-time alignment super-sparse network model to identify a fault propagation path between the equipment, establishing an equipment fault correlation graph, and generating a railway signal equipment fault prediction and diagnosis report according to a fault risk assessment value and an equipment fault correlation diagnosis result. The technical problem of low fault prediction accuracy of railway signal equipment in a super sparse historical data environment is solved.

A computer-implemented method for a digital security system receives unlabeled event data associated with a computing environment, clusters via an unsupervised machine learning model the unlabeled event data into clusters of unlabeled event data where unlabeled event data in one cluster are more similar to each other than to unlabeled event data in other clusters, selects a respective subset of unlabeled event data for each cluster of unlabeled event data, translates via a large language model artificial neural network each unlabeled event datum in each respective subset of unlabeled event data into a description for the unlabeled event datum, and applies a label via a labeling algorithm to at least one unlabeled event datum in a respective cluster responsive to and representative of the respective description for the unlabeled event datum in the respective subset, thereby transforming the at least one unlabeled event datum to a labeled event datum.

The present invention relates to a system and a method for measuring and analyzing minimal residual disease (MRD) in pediatric B-cell precursor acute lymphoblastic leukemia (B-ALL) using multiparameter flow cytometry (MPFC). The invention finds application in clinical diagnostics and hematology-oncology for quantifying MRD in B-ALL patients with high sensitivity and specificity, needed for risk stratification, monitoring treatment response, and informing therapeutic decisions. The system comprises interconnected subsystems including an acquisition subsystem with an MPFC instrument, a control and file generation subsystem, and an analytical subsystem. The analytical subsystem incorporates modules for sequential data reduction, automated data cleaning, automated unsupervised data clustering, and interactive cluster analysis. Key advantages include high MRD detection sensitivity (e.g., 10⁻⁵ or 0.001%) and high specificity, without reliance on reference samples or supervised machine learning models, making it applicable in laboratories with different measuring equipment and using different panels of antibodies for identification of leukemic cells.

The invention discloses an extra-high voltage equipment full-chain data trusted sharing system based on a block chain, and belongs to the technical field of power equipment data management. The system comprises a distributed account book data storage layer, wherein the distributed account book data storage layer comprises a data access and preprocessing module, a data hash and metadata uplink module, a security block generation module and a credible consensus verification module; the data processing and privacy protection layer comprises an intelligent data clustering analysis module and a privacy encryption and proxy re-encryption module; the data requesting and sharing management layer is used for receiving request information of a data requester; the server is used for providing unique identities for participants in all links, controlling data access authority and sending request information to a data owner for verification; and the data sending module is used for sending the data corresponding to the request information of the data requester to the data requester after verification is completed. According to the invention, the data of each link of the extra-high voltage equipment can be ensured not to be tampered.

The invention discloses a data collection method and system for energy-saving control of a central air conditioner and a storage medium, and the method comprises the steps: collecting multi-dimensional operation parameters to generate a time sequence data stream, screening and caching data by edge nodes, calling an air conditioner time sequence data clustering analysis model to generate a feature vector, and analyzing data based on a double-layer architecture of an edge-cloud collaborative model. And the acquisition frequency is dynamically adjusted, and the sensing device is controlled to acquire and compare data according to the new frequency to form a closed loop. The system comprises a multi-dimensional parameter sensing unit, an edge data processing unit, a clustering feature extraction unit, an edge-cloud cooperative processing unit, an acquisition strategy adjustment unit and a closed loop verification unit which work cooperatively. The storage medium is a computer readable medium, stores a corresponding program, and stores data in a classified manner by adopting a distributed architecture. Through dynamic collection, edge-cloud collaboration and clustering analysis, the data validity and processing efficiency are improved, the problem that traditional collection and collaboration are insufficient is solved, and efficient and energy-saving control over the central air conditioner is achieved.

The invention relates to the technical field of data cluster management, in particular to a multi-cluster message bus intelligent management method and system based on a unified control plane. The method comprises the following steps of: constructing a unified control plane architecture, and integrating a message proxy cluster, an authentication service, a message storage service, a delay message scheduling service, a management end and a client SDK (Software Development Kit); the authentication service receives an authentication request of the client SDK, executes fine-grained permission authentication, generates a unified permission token, and distributes message cluster connection information; communication is established based on the message cluster connection information, and message production and consumption track states are reported to a message storage service; carrying out persistent storage and classification processing on the reported message content and the full life cycle trajectory, and delivering the message to a target message agent cluster; and aggregating the data of each message cluster so as to realize configuration management, monitoring alarm and message replay operation of multiple clusters. According to the invention, unified management of heterogeneous message clusters can be supported.

The invention discloses an electric vehicle transportation risk scene construction method and system based on multi-source data clustering, and the method comprises the following steps: collecting a multi-source heterogeneous data source, and obtaining original data in the transportation process of an electric vehicle; preprocessing the original data to obtain a standardized data set; performing feature extraction on the standardized data set to form a feature vector set; performing clustering analysis on the feature vector set by using a clustering algorithm to obtain a plurality of data clusters; and constructing an electric vehicle transportation risk scene model based on the data clustering cluster. According to the invention, through multi-source data fusion and clustering analysis, a potential risk mode in electric vehicle transportation can be automatically identified, a fine-grained risk scene is constructed, and the accuracy and efficiency of risk management are improved. In the embodiment, data quality is ensured by data preprocessing, dynamic risk factors are captured by feature extraction, natural data grouping is realized by clustering analysis, and interpretable risk classification is provided by a risk scene model.

The invention relates to the technical field of text mining and data clustering, and discloses a state grid document collaborative clustering analysis method, system and device based on a graph model and a medium, comprising: constructing a joint graph model, the joint graph model comprising a document node subset and a word node subset, the edge weight between document nodes being determined based on the diversity between documents, and the word node subset being determined based on the difference between the document nodes; the edge weight between the word nodes is determined based on the diversity between the words, and the document nodes and the word nodes are not directly connected; on the basis of the joint graph model, constructing and solving a collaborative clustering target function to synchronously perform graph division on the document node subset and the word node subset so as to obtain a clustering result of the document and a clustering result of the word at the same time; wherein the collaborative clustering objective function is configured to maximize the sum of the cut value of the document sub-graph division and the cut value of the word sub-graph division. According to the method, the complex dissimilar relationship between the documents and between the words can be visually and effectively captured by utilizing the natural relationship representation capability of the graph model.

Methods for managing received alarm text data for an alarm, which can involve processing the alarm text data for clustering, thereby generating processed alarm text data; clustering the processed alarm text data into first clusters by performing a first clustering algorithm, the first clustering algorithm configured to be facilitated via accelerated processing; for the first clusters that do not satisfy a metric, performing a second clustering algorithm on the first clusters to form second clusters that represent a subset of the first clusters and have improved homogeneity, the second clustering algorithm different from the first clustering algorithm; performing a label annotation process on the second clusters and the first clusters that satisfy the metric to label annotate the second clusters and the first clusters that satisfy the metric into groups, thereby forming labeled cluster groups; and providing the labeled cluster groups for display on a user interface.

This invention discloses a method and system for invoking computational rules based on key indicators of preschool education, relating to the field of preschool education technology. It involves constructing a cloud-based big data center to collect multi-source data from preschool education, processing this data to obtain correlated data clusters, and building a preschool education AI model including a judgment model, a child development assessment and diagnosis model, a personalized gamified learning content recommendation model, and a home-school collaborative interaction support model. The judgment model analyzes the input to the preschool education AI model to determine which type of model to use for key indicator output. Simultaneously, a dynamic data pipeline is constructed to connect the cloud-based big data center and the preschool education AI, and a detection window is set to protect data security. This invention achieves more intelligent and precise preschool education intervention through the cloud-based big data center, the preschool education AI model, and the dynamic data pipeline.

The invention discloses a power consumption control method and device, equipment and a medium, and is applied to the technical field of servers, and the method comprises the steps: obtaining the power consumption data of a central processing unit in a recent preset time period, and obtaining first power consumption data; determining a cluster to which the first power consumption data belongs based on a preset clustering model to obtain a target cluster, the preset clustering model being a clustering model obtained based on historical central processing unit power consumption data clustering; determining a working mode corresponding to the target cluster to obtain a target working mode; determining power consumption data corresponding to a graphics processor based on basic power consumption corresponding to the target working mode, a proportionality coefficient and the first power consumption data to obtain second power consumption data; and performing power consumption control on the graphics processor based on the second power consumption data. In this way, GPU power consumption control can be accurately achieved, and the overall performance of the server is improved.

The application discloses a collaborative filtering recommendation method and system based on adaptive noise adding privacy protection, and the method comprises the following steps: suspected abnormal users are screened out by using a DBSCAN algorithm, and true abnormal users are determined by using a box chart, so that abnormal user data in a user data set is screened out; the similarity between each user is calculated, different sizes of noise are added according to the similarity value, and a user similarity noise matrix is constructed; an initial centroid is selected according to the user similarity noise matrix by using a k-means algorithm, and iterative updating is performed, so that user data clustering is realized; a recommendation list is obtained according to a neighbor set of a target user; and items are recommended to the target user according to the scores of the items in the recommendation list. Through the technical scheme of the application, the accuracy of the recommendation result is improved, the user privacy is protected, the problem that the clustering centroid deviates greatly is avoided, and the problem that noise is continuously accumulated in the iteration process to cause the final recommendation result to be inaccurate is avoided to a certain extent.

The application belongs to the technical field of network security, and particularly relates to a CPS attack path reconstruction method and system based on a kill chain model clustering. CPS information layer and physical layer operation data are collected, a feature vector is generated through time window fusion, a feature subspace is divided based on the four stages of the kill chain, candidate clusters are obtained by clustering the data of each stage, high-confidence clusters are screened in combination with time concentration and behavior intensity, a causal diagram is constructed according to time proximity and cross-layer correlation, and complete reconstruction of the attack path is realized; the application adapts to the cross-layer coupling characteristics of CPS, improves the accuracy of attack stage division and path reconstruction, and can provide reliable support for attack tracing and defense strategy formulation.

A conversational artificial intelligence (AI) agent learning method based on a generative language model includes a step of clustering learning conversation data with respect to a conversation context to generate k number of learning conversation data clusters, a step of learning each of the k learning conversation data clusters to generate k number of generative language models, a step of inputting each learning conversation data cluster to the k generative language models to generate k number of first responses for each learning conversation data cluster, and a step of classifying response preference between the k first responses generated for each learning conversation data cluster to generate (k-1) number of response preference data and automatically generating k×(k-1) number of response preference data corresponding to all of the k learning conversation data clusters without a separate labeling operation.

The present application belongs to the technical field of urban meteorological data processing and spatial information calculation, and particularly relates to a method and device for constructing urban meteorological gridded data based on spatial interpolation. The method comprises meteorological data clustering, partition optimization by using a target function during clustering, partition interpolation, spatial division and processing of regions according to the clustering results of meteorological data, statistics of the number of base stations for each region, determination of the type of a region based on the spatial distribution density of the base stations, determination of a global variation function and a regional variation function for each region, calculation of the estimated value of an interpolation point by using a corresponding Kriging interpolation method based on the variation function for different region types, interpolation of points near the boundary lines of different regions by using the meteorological data of the base stations of the regions involved, and weighting to obtain the final result. Finally, the construction of urban meteorological gridded data based on spatial interpolation is realized.

This invention relates to the field of wind power generation technology and discloses a method, device, equipment, and medium for fault assessment of wind turbine transmission systems. The method utilizes a data dimensionality reduction algorithm to reduce the dimensionality of multidimensional raw data, retaining core distinguishing features and simplifying calculations. Then, a data clustering algorithm is used to intelligently classify operating states, and a preliminary fault mode mapping is constructed by combining historical faults. Subsequently, the rationality of clustering is verified using signal source correlation coefficients, and long-term historical operating data is filtered through historical data matching rates to reduce the risk of misjudgment. Next, based on time series analysis and degradation path analysis, the coupling relationship between vibration trend slope, temperature accumulation offset, and torque decay cycle is obtained. Finally, core features are extracted through convolutional neural networks to accurately output the probability distribution and specific location of fault occurrence, thereby improving the accuracy of fault identification in the transmission system of offshore wind turbines and the ability to predict component performance degradation.

The invention relates to the technical field of big data processing, and relates to a data processing method and device, a computer program product and electronic equipment. The data processing method comprises the following steps: analyzing an obtained multi-source data file, and extracting numerical value information and semantic background information corresponding to the numerical value information from the multi-source data file; according to the numerical information and the corresponding semantic background information, performing clustering processing on the numerical information according to preset multi-dimensional clustering features to obtain a plurality of data clusters, and performing statistical analysis on data in each data cluster to generate a structured data template corresponding to each data cluster; and matching the to-be-processed file with the generated structured data template, determining a target data template according to a matching result, and processing the to-be-processed file based on the target data template to obtain processed data. The data processing efficiency and accuracy can be improved.

The invention discloses an object identification method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring object operation data from at least one data acquisition source; executing a target detection operation on the object operation data, and determining first operation data and second operation data associated with the target object; inputting a first object feature corresponding to the first operation data and a second object feature corresponding to the second operation data into a pre-trained object classification model to obtain a classification result; under the condition that the classification result meets the target classification condition, a group of data clusters are obtained, the object type of the target object is determined based on the position of the data cluster where the operation data corresponding to the target object is located, the group of data clusters comprise at least one data cluster, and different objects in one data cluster are associated. The technical problem that the recognition accuracy of the object with the abnormal operation is low is solved.

The invention relates to the technical field of data processing, and particularly discloses an intelligent management system and platform for car rental, and the method comprises an obtaining module which obtains rental car data, predetermined rental data, historical rental data and car rental behavior feature vectors; the calculation module is used for calculating a standardized behavior vector of the historical rental sub-data of which each car rental tag is completed in the historical rental data, and determining behavior clustering data of a plurality of clustering tags; the clustering module is used for calculating a clustering independent variable set of each clustering label and a significant weight, a clustering range or a dominant clustering set of each independent variable in the clustering independent variable set; the construction module is used for constructing a lease recommendation model; and the management module is used for realizing intelligent management of automobile leasing. The method can adapt to lease demands and performance capabilities of different user groups, realizes customized lease recommendation, optimizes guarantee deposit collection and vehicle deployment strategies, reduces resource idleness and performance disputes, and gives consideration to recommendation accuracy, risk controllability and operation efficiency.