781 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 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 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 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 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 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 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.

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 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 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.

The invention provides a clustering method and system for transcriptome sequencing data. The method is applied to the technical field of data processing, and comprises the following steps: collecting cervical adenocarcinoma and para-carcinoma tissue specimens of a plurality of patients, and carrying out data preprocessing to obtain a standardized gene expression matrix; performing clustering operation on the standardized gene expression matrix based on a clustering analysis method combining a potential category model and sub-alliance division to obtain a clustered gene set; performing gene function annotation on the clustered gene set, and performing correlation analysis on a clustering result after function annotation and clinical characteristics of cervical adenocarcinoma to obtain correlation between gene expression and the clinical characteristics; and on the basis of clustering analysis and correlation analysis results, constructing a deep clustering prediction model based on a variational auto-encoder and a Gamma hybrid model so as to predict the prognosis risk or chemotherapy sensitivity of the patient. The problems that high-dimensional transcriptome data is poor in clustering stability and low in prognosis prediction precision are solved.

The information processing device includes an acquisition means, a cluster generation means and a straight line generation means. The acquisition means acquires measurement data which is a set of data representing a plurality of measured points of a berthing place measured by a measurement device provided on a ship. The cluster generation means generates one or more clusters obtained by dividing the plurality of measured points by clustering. The straight line generation means configured to generate a straight line along the berthing place of the ship, based on the one or more clusters.

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 relates to a training sample screening method and device and a computer readable storage medium, and relates to the field of artificial intelligence. The training sample screening method comprises the steps of obtaining multiple groups of prediction results of each training sample in a training sample set under multiple groups of parameters of an artificial intelligence model; dividing the plurality of training samples in the training sample set into a plurality of data clusters according to the plurality of groups of prediction results of each training sample in the training sample set; calculating the contribution degree of each data cluster of the plurality of data clusters to artificial intelligence model training; and selecting a target training sample from the plurality of training samples according to the contribution degrees of the plurality of data clusters. According to the method and the device, the training samples can be efficiently screened, and the efficiency of training the artificial intelligence model is 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 lithium battery overcharge early warning system based on big data, and the system comprises a data collection module, a data processing module, a dynamic threshold generation module and a risk analysis module, and relates to the technical field of data processing. A local mean value and a standard deviation are calculated by using a sliding window method, a similar data cluster is constructed by using a dynamic time warping algorithm, and overcharge voltage and temperature thresholds are calculated in combination with a preset risk ratio, so that the limitation of a single Gaussian mixture model is avoided, and the accuracy of the thresholds is improved. Historical data are also acquired, a prediction model is constructed by using an autoregression integral moving average model, a slope is calculated to obtain a correction factor, an aging model constructed by machine learning is combined to update a threshold value, dynamic change and aging of the battery are combined, battery state change is adapted, and early warning reliability and timeliness are improved.

The invention belongs to the technical field of transformer monitoring, discloses a method, a system and equipment for cleaning and checking transformer oil chromatography online monitoring data and a medium, and aims to solve the problem of recognition accuracy. The method comprises the following steps: acquiring the concentration of the dissolved gas in the transformer oil to construct gas concentration time sequence data, identifying and eliminating missing values and / or zero values in the gas concentration time sequence data, filling by using a linear interpolation method, detecting other values by using a Z-Score method to determine an obviously abnormal value E1, and calculating the concentration of the dissolved gas in the transformer oil. Preprocessed gas concentration data are obtained from E1 data in gas concentration time sequence data, specific value characteristic data are constructed according to an IEC three-specific-value method and added, then points which do not belong to data clusters obtained through clustering are determined as outliers by applying a DBSCAN clustering algorithm, and the outliers are determined as outliers. And identifying the clustered data clusters with fuzzy boundaries, finding out points near the boundaries, marking the points as boundary points, summarizing to form an abnormal data set E < total > to be checked, approving the abnormal data set E < total > by adopting a Rogers ratio method, and judging whether the abnormal data set E < total > is abnormal or not.

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 discloses a data deduplication method and system based on incremental calculation and feature clustering and electronic equipment, and the method comprises the steps: receiving multi-source heterogeneous text, image, audio and video data streams in real time, adding a timestamp to each data unit, and generating an input data set with a time attribute; performing multi-modal feature extraction on the input data set, and dynamically adjusting feature weights of the extracted multi-modal features through a sliding window model and a time decay factor to obtain an increment feature vector set with the weights; executing two-stage clustering based on the feature vector set to generate a plurality of fine-grained data clusters; and comparing the intra-cluster data of each fine-grained data cluster in pairs by adopting a composite similarity model to determine intra-cluster repeated data, and performing data deduplication on the input data set according to the intra-cluster repeated data to obtain a deduplicated input data set. According to the method, the processing efficiency of the real-time data is improved, and the storage and calculation cost is remarkably reduced.

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.