133 results about "Cluster group" patented technology

The invention relates to the technical field of data processing, and discloses an Internet of Things gateway data grading processing method and system based on edge computing, and the method comprises the steps: collecting original operation data and a data traffic value received by an Internet of Things gateway; performing feature extraction and clustering grouping according to the original operation data, calculating an average influence coefficient of samples in a group in combination with a preset service weight, and determining an importance level corresponding to the group; important data is screened out for distribution immediate evaluation; performing aging characteristic analysis according to the important data, and predicting a data processing priority; analyzing resource demand allocation according to the priority sequence and the data flow value, and constructing a resource allocation proportion; according to a resource allocation proportion and an allocation proportion lower limit, identifying nodes with insufficient proportions and optimizing the priority sequence to obtain a final scheduling scheme; according to the final scheduling scheme, the processing sequence is applied to the original operation data, and data grading output is obtained. According to the method, efficient and accurate data processing is realized.

The invention relates to a supplier behavior real-time sensing and monitoring method and device and electronic equipment. The method comprises the steps of collecting and preprocessing supplier multi-source heterogeneous data in real time, dynamically capturing a Binlog of a MySQL database through a Flink CDC framework, obtaining service behavior data in real time, collecting text log data through a Flume framework, and transmitting network behavior data in real time. Integrating the structured data, the unstructured data and the semi-structured data, and constructing a unified data transmission channel; constructing a multi-dimensional user portrait; comparing the semantic similarity between the bidding file and the bidding requirement, and triggering compliance early warning when the matching degree is lower than a threshold value; analyzing a supplier cooperation network, and marking a cluster group with abnormal transaction frequency and irrelevant business fields as a cross-bidding risk; and analyzing the access behavior sequence, and detecting a malicious access pattern to generate a malicious access alarm. According to the supplier behavior monitoring system and method, real-time performance, accuracy and multidimensional performance of supplier behavior monitoring are achieved through the architecture of multi-source heterogeneous data real-time integration, multi-dimensional portrait construction and intelligent risk identification.

In a method for cross-cluster service access, a tenant configures a created cluster group, and a gateway sends configuration information to each application gateway such that when receiving a service access request of a client in a first cluster, a first application gateway may directly forward, based on the configuration information, the service access request to an application gateway corresponding to another cluster. In this way, the tenant can access a service in the other cluster, to implement automatic access between services in a plurality of clusters, thereby facilitating management.

A method performed by a mobile network node operating in a network is disclosed. The method includes collecting user equipment, UE, parameter data from each of a plurality of UE devices operating in the network; utilizing the collected UE parameter data to cluster the plurality of UE devices into one or more cluster groups, which are subsequently converted to a respective one or more tensor networks; determining the future trajectory positions for each of the UE devices included in a cluster group assigned to the mobile network node using the one or more tensor networks; determining a most energy-efficient movement for the mobile network node to support the future trajectory positions of the plurality of UE devices; and establishing a new position for the mobile network node based on the most energy-efficient movement and an optimal data rate prediction achievable by the mobile network node.

The invention discloses a day-ahead solar irradiance data correction method, and the method comprises the steps: screening historical numerical weather forecast data of the same weather type based on the weather type of day-ahead weather forecast; calculating a Spearman correlation coefficient and a variance of daily solar irradiance, dividing historical days into different clustering groups through a Kmeans clustering algorithm, and obtaining a clustering center; determining a cluster group to which the day-ahead numerical forecast belongs, and selecting historical data with matched weather types and groups to construct a training set; training a COSTLSTM model capable of decomposing an input sequence trend and a seasonal component to obtain a correction model; and inputting the numerical weather data of the day-ahead weather forecast into the trained solar irradiance correction model, and outputting to obtain the corrected day-ahead solar irradiance. Compared with the prior art, the method can effectively cope with complex mixed modes and seasonal periodic differences in the solar irradiance, and realizes accurate data correction of the day-ahead solar irradiance in complex weather.

The invention discloses a method and a device for automatically determining a single-phase grounding trigger reason of a power distribution network. The method comprises the following steps: acquiring original transient recording data corresponding to a historical power distribution network single-phase grounding event; processing the original transient recording data to obtain a standardized waveform data set; the standardized waveform data set is input into a feature encoder in a trained deep learning model, a feature vector data set is determined, and the trained deep learning model is obtained through training in an unsupervised mode based on the standardized waveform data set; clustering the feature vector data set to determine a plurality of clusters; receiving triggering reasons which respectively correspond to the plurality of clusters and are used for marking the original transient recording data, and determining a reason label system; and determining a trigger reason corresponding to the original transient recording data based on the reason label system. According to the method, the technical problems of low construction efficiency and poor quality of the sample library caused by dependence on manual annotation and manual feature extraction and weak traditional clustering capability are solved.

The invention discloses an environmental pollution detection method and system based on a wireless sensor network, and relates to the technical field of environmental pollution monitoring, and the method comprises the steps: deploying a sensing node set in a to-be-monitored region, collecting and establishing node collection data, and analyzing and establishing self-calibration observation data through an edge calculation unit; configuring adjacent node communication to construct a wireless sensor network and form a temporary cluster; and according to the cluster reconstruction communication topology and the sampling frequency, executing data reconstruction acquisition, inputting a result and self-calibration observation data into a causal backtracking channel for verification, and outputting an environmental pollution detection result. The technical problems that a traditional air pollution detection method is difficult to adapt to a dynamically changing monitoring environment, monitoring coverage is incomplete, data collection precision is insufficient, and pollution sources cannot be traced are solved, air pollution detection coverage is more comprehensive, data are more reliable, the pollution sources can be traced, a complete process from detection to management is formed, and the detection efficiency is improved. And the technical effects of accurate evaluation and effective management and control are achieved.

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.

The application provides a team user identification method and device, electronic equipment, medium and program product, which can be used in the field of data processing. The method comprises the following steps: obtaining first behavior data and first portrait data of a plurality of first users; converting the first behavior data of the first users into first risk image data corresponding to the first users; determining first user feature vectors of the first users according to the first risk image data corresponding to the first users and the first portrait data; clustering the first user feature vectors corresponding to each of the first users to obtain a cluster group, and determining the first users corresponding to the first user feature vectors in the cluster group as users belonging to the same risk team. In the application, the determination accuracy of the team users is improved.

The invention provides an optical cable fault intelligent analysis and positioning method based on big data and AI, and the method comprises the steps: extracting a spatial density feature value from a latitude and longitude coordinate set, judging the coordinate as a high-density region coordinate if the spatial density feature value exceeds a preset threshold value, and obtaining a preliminary spatial distribution point set; performing grouping processing on the preliminary spatial distribution point set by adopting a clustering algorithm, fusing Euclidean distance measurement in the longitude and latitude coordinate set, determining a cluster of fault points, and obtaining a distribution mode cluster group; and performing prediction processing on a time sequence of the high-incidence area identification set through a time sequence analysis algorithm, fusing trend data of the time evolution sequence segment, determining a prediction value of a future evolution rule, and obtaining a prediction rule value set.

The application discloses a kind of based on risk scene's electric power market parallel clearing method and system, it is characterized by: S1, multi-dimensional risk scene synchronization generation;S2, scene space-time feature extraction;S3, scene clustering group;S4, distributed parallel clearing calculation;S5. Risk index aggregation analysis.The application improves risk coverage by scene coupling generation, solves the problem that scene and clearing calculation are separated and cannot capture multiple risk coupling effects;Through distributed architecture for clearing calculation, improve the efficiency of calculation;Through risk index, the cross-market chain reaction effect can be quantified.The application improves risk coverage by scene coupling generation, solves the problem that scene and clearing calculation are separated and cannot capture multiple risk coupling effects;Through distributed architecture for clearing calculation, improve the efficiency of calculation;Through risk index, the cross-market chain reaction effect can be quantified.

The embodiment of the invention relates to a domain name resolution method and device based on a container arrangement engine, equipment and a medium, and is applied to a resolution cluster group, the resolution cluster group comprises a first resolution cluster and a second resolution cluster, a main resolution cluster is the first resolution cluster, and a standby resolution cluster is the second resolution cluster. The method comprises the following steps: a first resolution cluster deletes first domain name resolution rule information corresponding to the first resolution cluster in response to a degradation instruction; the second resolution cluster generates second domain name resolution rule information corresponding to the second resolution cluster according to a preset domain name address relationship in response to the upgrading instruction; wherein the domain name address relationship is used for recording the corresponding relationship between the entry domain name and the domain name resolution address of the member cluster; and the second analysis cluster sends an analysis cluster switching instruction to a member cluster corresponding to the analysis cluster group, so that the member cluster updates the main analysis cluster from the first analysis cluster to the second analysis cluster. Therefore, the duration of cross-cluster analysis abnormal interruption in the cluster federation is reduced.

The application discloses an adaptive clustering federated learning modeling method for precision medicine, and relates to the technical field of precision medicine, and comprises the following steps: data collection and preprocessing and model construction and training; the application accurately determines the optimal number of the global model through adaptive clustering, and divides clustering groups according to the similarity of the client model parameters, realizes independent training of the groups, effectively improves the adaptability of the model to different client data characteristics, samples and label distribution differences, avoids the problem that a single model has poor performance in some clients, and significantly enhances the ability of the model to capture complex medical patterns; the model generalization is optimized through grouped training, so that the model can better cope with new data distribution, under the premise of ensuring the privacy and security of medical data, the accuracy and reliability of the model in precision medical scenes such as disease diagnosis and prognosis prediction are greatly improved.

The present application relates to the technical field of cluster communication, and proposes an intelligent fusion cluster group call method, steps of which include: configuring group members and member types thereof, wherein the member types at least include a cluster terminal, a dispatch station, and a non-cluster terminal, wherein the non-cluster terminal includes at least one of a traditional fixed phone, a mobile phone and a smart camera; a cluster core network performs corresponding media adaptation operation and right adaptation operation on different types of group members in a group call establishment process and a group call process according to the pre-configured member types, so that the non-cluster terminal can access and participate in the cluster group call service. The present application enables the public network terminal and the intelligent device originally not supporting the group call and the right operation to be integrated into the cluster communication process, expands the terminal compatibility and the service coverage range of the cluster system, and meanwhile maintains the necessary media control and right management characteristics in the cluster dispatch.

The application provides a 3D object recognition system based on deep Hough voting and a recognition method thereof. The recognition system comprises a votenet detection module, which is used for inputting N point clouds, learning point cloud features based on a pointnet++ backbone network, and generating M votes from seed points; an RGB module connected with the seed points, which is used for object detection on a 2D image and point cloud feature extraction on a point cloud, and then 2D image voting and semantic and texture prompts are lifted to 3D seed points; a PPC module connected with the seed points, which is used for analyzing feature information carried by the seed point cloud; an OOC module, which is used for forming a cluster group after the point cloud is subjected to initial processing, Hough voting and sampling clustering; and a GSC module connected with the seed points and the cluster group, which is used for applying an MLP layer to further aggregate global information, and outputting a feature map which is then expanded and combined with an output feature map of the OOC module. Compared with the prior art, the application can directly process raw data without relying on any 2D detector.

The application discloses a kind of based on group head adaptation degree and adaptive competition radius UAV cluster grouping method, each UAV node periodically interacts node information, and according to the node information obtained, the group head adaptation degree value of UAV node is calculated, and then the candidate group head is determined by FloodMax and FloodMin distributed flooding election, and the candidate group head is adaptively determined competition radius according to the number of local effective neighbors, and competition timer is set according to the adaptation degree value, and the final group head is determined by competition mechanism, and the ordinary node sends the request of entering group according to the group head declaration, and forms the grouping structure.The method of the application adaptively determines the competition radius according to the number of effective neighbors in the local range of candidate group head, and determines the final group head in combination with the competition radius constraint, so as to reduce the redundant group head of overlapping coverage range in low-density area, enable multiple group heads to exist in high-density area to share access load, optimize the spatial distribution of group head, and improve the access balance and group structure stability of UAV cluster grouping.

The invention belongs to the technical field of artificial intelligence, and relates to a data exception monitoring method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining compensation data in a preset time period; constructing a graph model based on the claim data; performing feature extraction on the graph model to obtain graph features, and merging the graph features with the compensation data to obtain merged data; splitting the merged data based on the time sequence to obtain target data; performing clustering analysis on the target data based on a target clustering algorithm to obtain a plurality of clustering groups; performing index analysis on all the clustering groups based on the target index, and judging whether an abnormal claim group exists in all the clustering groups or not; and if yes, executing alarm processing corresponding to the abnormal claim group. In addition, the invention also relates to a block chain technology, and the target clustering algorithm can be stored in a block chain. The method can be applied to case abnormity monitoring scenes in the fields of financial science and technology and medical health insurance, and the recognition accuracy of repeated claim is effectively improved.

The invention provides a multimedia data security measurement method based on multi-dimensional association, and the method comprises the steps: dividing a plurality of features of a single image in multimedia data into a plurality of clustering groups based on spatial similarity through a spectral clustering algorithm for the multimedia data; and judging whether the multimedia data is safe or not by combining the content safety score, the privacy information safety score, the semantic safety score and the formalized safety score, thereby realizing optimization of safety evaluation and calculation efficiency.

The invention discloses a papermaking drying process energy efficiency monitoring identification method based on a comprehensive production state, and the method comprises the steps: carrying out the clustering and grouping of input data, and carrying out the preprocessing of the grouped data; a stacking algorithm is introduced to fuse a plurality of base models to form a powerful regression prediction model, the advantages of a plurality of different algorithms are combined to improve the performance of the whole model, and the steam flow prediction precision is improved; performing energy efficiency grade evaluation and identification on the data based on the relationship between the papermaking yield and the steam flow of the papermaking machine; a stacking algorithm is adopted to fuse a plurality of base model classifiers to form a powerful classification prediction model, and the advantages of a plurality of different algorithms are combined to improve the performance of the whole model so as to obtain an optimal papermaking drying process multistage energy efficiency prediction result. According to the method, the overall performance, the recognition accuracy, the energy efficiency grade precision and the environmental adaptability of the model are remarkably improved. The system ensures efficient, accurate and stable operation of the whole process from data input to energy efficiency level output.

The invention discloses a simulation scheduling method, device and equipment for cluster resources, a medium and a product. The method comprises the steps that a node resource configuration table of a target cluster group is acquired, the target cluster group is composed of at least one target cluster, and each target cluster is composed of at least one node and at least one working load; injecting node scheduling configuration matched with each node into a pre-configured simulator instance, and executing simulation scheduling operation on the target cluster group according to the simulator instance and each node scheduling configuration to obtain a scheduling result file; and generating a simulation scheduling report matched with the target cluster group according to the scheduling result file. Through the technical scheme of the invention, the simulation scheduling of the cluster resources can be realized, the acquisition efficiency and the result accuracy of the simulation scheduling result of the cluster resources are improved, and the efficiency of the scheduling work of the cluster resources and the service operation efficiency in the cluster environment are further improved.

The present application relates to a kind of double-fed wind farm grouping method based on index dimension reduction and weighted fuzzy C means clustering, belong to power system control technical field.The method includes: S1: the model of double-fed wind farm series compensation is incorporated into weak grid system is built, and the leading variable of sub-synchronous oscillation is selected;S2: considering the dynamic, transient process analysis under the condition of adapting to multiple disturbance scene, the leading variable data of each selected time point before, during the series compensation is incorporated into weak grid, during the series compensation is incorporated into weak grid, during the addition fault and after the removal of fault is used as the initial grouping index of double-fed wind turbine;S3: the initial grouping index is reduced using principal component analysis method;S4: the index after dimension reduction is used as clustering grouping index, and the clustering of wind turbine is realized using weighted fuzzy C means clustering algorithm.The present application improves the rational effectiveness of clustering grouping method in double-fed wind farm series compensation is incorporated into weak grid system.

The invention discloses a building industry industrial internet platform established based on data elements and a clustering algorithm, and relates to the technical field of building industry industrial chain supplier management. A multi-dimensional clustering module adaptively selects a clustering algorithm through multi-dimensional evaluation based on a spatial-temporal characteristic matrix; a clustering parameter configuration set is generated in combination with an elbow rule and a contour coefficient method, clustering parameters are optimized to eliminate empirical preset deviation, and supplier classification accuracy and service adaptability are remarkably improved. The risk early warning module generates a composite risk rule set integrating static and dynamic thresholds based on a supplier grading strategy instruction set and a cluster feature quantification report, monitors indexes in real time through a threshold comparison mechanism, automatically marks high-risk suppliers when the thresholds exceed the thresholds, generates an early warning list, breaks through fixed threshold response delay, and improves the early warning efficiency. And accurate and active prevention and control of supply chain risks are realized.

The application discloses a kind of based on information redundancy's call chain data compression method and system, method includes: for the call chain data collected, similar data is extracted using KMeans algorithm, the data point in the call chain data is grouped, and each cluster group is obtained;Wherein, the data similarity in the same cluster group is high, and the data similarity between different cluster groups is low;Using the offline tail sampling strategy based on hierarchical clustering, the cluster group is sampled and handled, and a structured file is output;The structured file is losslessly compressed using a lossless compression method, and the compression of the call chain data is completed.The application is based on the characteristics of call chain data, combined with the sampling idea of lossy compression, greatly reduces the size and storage burden of the compressed file on the basis of maintaining data analysis value, improves efficiency and real-time performance, can guarantee data integrity and strengthen generalization ability, and can be widely applied in computer technology field.

Systems and methods for determining how topics evolve in a messaging system extract at least one N-gram from data content (e.g., caption of messages) in the messaging system and detect anomalous behavior in N-gram frequencies over time. The anomalous behavior is used to select candidate N-grams for a determination of whether a topic of a candidate N-gram is evolving or fading. The candidate N-grams are clustered into cluster groups that are used to train at least one time series forecasting model to predict N-gram frequencies in a future time window. A time series of the N-gram frequency is divided into old and recent partitions and pattern recognition is applied to the predicted N-gram frequencies to identify an evolving or fading topic when the difference between a frequency of each anomaly and an average rolling median for each partition is greater for the most recent partition.

This invention discloses a satellite trunking terminal user information publishing and comment / like interaction system. After a satellite terminal forms a cluster social circle through online cluster grouping and adding friends, in addition to supporting one-call / group calling and real-time SMS services, it will also support value-added services such as terminal users publishing information to their group user circle and friend user circle, and receiving comments and likes. The terminal APP adds two communication sections, group activity and friend activity. Clicking on these sections displays information published by associated users, which can be in the form of SMS messages, data, or status emoticons. Users can comment or like these messages. Users can also actively publish information and receive comments and likes from other users. Associated information is exchanged bidirectionally between the satellite terminal and the ground station core network. The satellite trunking air interface data transmission path ensures the integrity and reliability of the interactive data, allowing users to publish information and comment / like simply by clicking to send to the satellite, adding new application functions to trunking communication.

The invention discloses a fashion design element automatic extraction and recombination method and system based on an intelligent algorithm, and relates to the field of design element extraction.The fashion design element automatic extraction and recombination method based on the intelligent algorithm comprises the following steps that S1, sample data and evaluation parameters are obtained, and data preprocessing is carried out; s2, multi-modal feature extraction is carried out, and a design element feature parameter set is generated; s3, time sequence modeling is carried out, and design fading parameters are extracted; s4, performing clustering processing to generate a design element clustering group, and calculating a feature ratio; s5, fusing and recombining the design element characteristic parameters, generating fashion product design element recombining parameters, and generating design effect evaluation parameters; and S6, generating design element labels, and carrying out automatic classification and labeling. According to the invention, through combination of the deep neural network and multi-modal feature extraction, the automation level of fashion product design is improved.

The invention relates to the technical field of power systems and renewable energy scheduling, and discloses a small hydropower station cluster planning and grouping method and device and a medium, and the method comprises the steps: obtaining multi-source data of each small hydropower station, and constructing a small hydropower station cluster feature space model; constructing a mathematical effectiveness index and a field rationality index, and establishing a k-means clustering algorithm multi-dimensional evaluation index system; executing an improved k-means + + clustering algorithm, and generating clustering schemes and evaluation results under different K values; constructing a derivative evaluation index based on an error sum of squares, distributing an index weight, calculating a comprehensive score corresponding to each K value, and determining an optimal clustering k value; and outputting a small hydropower station cluster grouping scheme corresponding to the optimal clustering number. According to the method, adaptive grouping of small hydropower station clusters is realized through a clustering algorithm driven by a small amount of data, and a small hydropower station cluster division scheme with reasonable spatial distribution, unified basin attributes and coordinated operation characteristics is provided for regional power grid dispatching.

The invention relates to the technologies of the Internet of Vehicles, block chains, consensus algorithms and the like, and discloses a dynamic clustering reputation collaborative hierarchical consensus architecture for the Internet of Vehicles. In the method, an architecture of'cluster grouping optimization topology + reputation dynamic management and control + hierarchical consensus efficiency improvement 'is constructed: grouping is carried out according to a communication distance between a vehicle and a clustering center, equipment computing power and a reputation value by using an enhanced K-Medoids algorithm, and a low-delay topology foundation is laid; based on an improved EigenTrust algorithm, node reputation is evaluated in combination with data authenticity, consensus participation degree, resource contribution degree and historical impact factors, and node permission is dynamically controlled; layered efficiency improvement is performed according to the number of vehicle nodes, a multi-layer architecture is established, local efficient processing is adapted, global consensus is realized, malicious interference and communication consumption are reduced, and the security of a consensus network is improved.

This invention discloses an industrial network security risk assessment method, belonging to the field of network security assessment technology. The method includes: acquiring standard traffic data and preset threat label data of an industrial wireless network; extracting traffic feature sets; obtaining feature cluster grouping datasets through distributed clustering across multiple working servers; selecting key feature datasets by combining threat labels; obtaining the optimal feature dataset through dimensionality reduction using an autoencoder and inputting it into a traffic detection model to obtain detection results; conducting risk assessment based on the results to obtain quantitative values; and determining the optimal combination of defense strategies accordingly. By using distributed clustering to process traffic features, combining threat labels to filter key information, and optimizing through dimensionality reduction using an autoencoder, the method dynamically quantifies risks and matches the optimal defense strategy based on anomaly detection results, thereby achieving accurate risk assessment and adaptive defense of industrial wireless networks and improving the operational security of industrial wireless networks.