86 results about "Cluster group" patented technology

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.

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 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 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 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.

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 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.

The invention discloses a cluster memory allocation method and device for a service system, equipment, a medium and a product. The method comprises the following steps: acquiring each target service command, a database table, a cluster number of each cluster in a cluster group and a total number of clusters; calculating according to each target service command and the total cluster number to obtain a target module value matched with each service command; according to each cluster number and the target module value of each target service command, sending each target service command to a target cluster matched with the target service command, and obtaining target data matched with the target cluster based on a database table; and processing the target data respectively matched with each target cluster group according to the target service command respectively matched with each target cluster group through each target cluster group so as to execute each target service command. By means of the technical scheme, cluster memory allocation of the service system can be achieved, the utilization rate of the cluster memory is increased, and then the operation efficiency of the service system is improved.

The embodiment of the invention discloses a recommendation strategy making method and device, electronic equipment and a storage medium, and relates to the field of financial science and technology, and the method comprises the steps: determining an original clustering result corresponding to an intentional customer set based on a clustering parameter set corresponding to the intentional customer set; carrying out resampling with replacement on the intention customer set to obtain a plurality of resampling customer sets, and determining reclustering results corresponding to the resampling customer sets based on the same clustering parameter set; performing stability verification on the original clustering result according to the similarity between the original clustering result and each re-clustering result; and when the original clustering result does not pass the stability verification, regenerating the original clustering result corresponding to the intention customer set, taking the original clustering result passing the stability verification as a target clustering result, and formulating a target product recommendation strategy corresponding to each clustering group in the target clustering result. And the credibility of differentiated recommendation strategies formulated for various intentional customers is improved.

The application discloses a kind of based on adaptive high-dimensional data local differential privacy protection method, using rough set theory and mutual information, the data of different relevance and sensitivity are clustered grouping, different grouping is used different random response probability, permanent random response and temporary random response are carried out.Local differential privacy processing data, using SNE method for dimension reduction and adaptive sampling gradient optimization in server end, with low-dimensional data joint probability distribution approximates the joint distribution of all attributes, according to the sampling strategy and the approximate joint distribution constitutes release dataset.The application solves the problem of high-dimensional data with dimension disaster and local differential privacy combination and maximum degree maintaining data relevance, and more reasonably allocates privacy budget according to the different data sensitivity to some extent, improves data availability and reduces communication cost.

The invention discloses a load collaborative management method, system and device based on multi-dimensional perception, and the method comprises the steps: obtaining multi-dimensional data which can affect a load through a deployed edge node; extracting features of the multi-dimensional data in different dimensions to form an initial load feature vector; inputting the initial load feature vector into a pre-trained model to obtain a load feature vector output by the model; clustering the load nodes based on the load feature vectors, and correspondingly forming group-level units by clustering groups obtained by clustering; and selecting a representative node of each group-level unit, receiving a scheduling instruction through the representative nodes, and coordinating the allocation of resources in the group-level units. Due to the fact that the multi-dimensional data including the environment, the user behavior, the equipment state and the like influencing the load are collected, the high-quality data are extracted from the multi-dimensional data, the load feature vector is formed and used for determining the load management decision, and the accuracy of the load management decision is improved.

The application discloses a single-stage multi-cluster fracturing method for ultra-deep shale gas, an electronic device and a medium. The method can comprise the following steps: calculating the formation fracture pressure of a horizontal section, and then calculating the inter-cluster interference stress; determining the perforation cluster position; calculating the bottom hole flowing pressure increase value, and controlling the bottom hole flowing pressure; according to the perforation cluster grouping condition, performing intra-fracture temporary plugging design and inter-fracture temporary plugging design on each group of perforation clusters in the fracturing section; and repeating the above steps to complete the reconstruction of all fracturing sections of the horizontal well. The application can increase the fracturing reconstruction volume of the ultra-deep shale gas by differentiating the perforation cluster position, batch starting and multi-cluster fracturing and the temporary plugging method, reduce the fracturing reconstruction cost, and help the ultra-deep shale gas to realize benefit development.

The application provides a multimedia data security measurement method based on multi-dimension correlation. For multimedia data, a plurality of features of a single image in the multimedia data are divided into a plurality of clustering groups based on spatial similarity by a spectral clustering algorithm. Whether the multimedia data is safe is judged by combining content security score, privacy information security score, semantic security score and formal security score, so as to realize security evaluation and optimization of calculation efficiency.

The invention is suitable for the field of computer artificial intelligence, and provides a clustering method and a clustering device based on a hybrid clustering algorithm. The method comprises the following steps: acquiring to-be-clustered data; according to the clustering number, clustering the to-be-clustered data by using a divided clustering algorithm to obtain a first clustering cluster group, the first clustering cluster group comprising first clustering clusters; and clustering the first clustering cluster by using a hierarchical density clustering algorithm to obtain a second clustering cluster group, the second clustering cluster group comprising a second clustering cluster, and the size of the second clustering cluster is smaller than or equal to the size of the first clustering cluster. The method is helpful for improving the accuracy of the clustering result.

A method includes receiving files. Deployed models analyze the files to create results with corresponding data. The results identify suspicious files. The system calculates ranked results by ranking the results according to a confidence level, and clustered groups by clustering the results based on similar data. The system calculates classes by correlating the ranked results to the clustered groups. The classes indicate accurate results and inaccurate results. The system retrains the deployed models having the class indicating inaccurate results with new training sets to create retrained models. The system validates the retrained models when the retrained models identify one or more suspicious files from the receiving. The system compares the classes of the deployed models to new classes of the retrained models. The computerized system deploys the retrained models when the new classes of the retrained models indicate accurate results greater than the classes of the deployed models.

The application relates to a data compression method, device and equipment of a regular expression HNFA model. The method comprises the following steps: obtaining HNFA models corresponding to a plurality of regular expressions, extracting an effective character set group of each model, clustering the models based on the similarity between the effective character sets, forming a plurality of clustering groups, constructing a unified character mapping table for each group, mapping characters in the group to continuous indexes, compressing original character activation module data into a dense matrix according to the mapping table, converting input characters through the mapping table during matching, and enabling an engine to correctly read a state activation vector from the compressed data. The method significantly reduces storage overhead and transmission delay while maintaining hardware compatibility, and improves regular matching efficiency.

The invention discloses a business travel information processing method and device and electronic equipment, and relates to the technical field of artificial intelligence. The method comprises the steps of receiving a first instruction; the first instruction comprises first input content representing current travel information and travel preference information of at least one travel object; under the condition that the at least one travel object is a plurality of travel objects, inputting the first input content into a first model, and modeling the multi-dimensional association features among the travel objects through the first model to obtain a modeling result; carrying out clustering analysis on the modeling result, screening out a business travel object cluster of which the comprehensive association degree meets a preset screening condition, and determining a target matching result based on the business travel object cluster; wherein the target matching result comprises at least one of a first matching result representing the travel objects of the same travel in the travel objects and a second matching result representing the travel objects of the same room in the travel objects.

The invention provides a firewall intercepted information clustering identification method and system based on network security, and relates to the technical field of firewall intercepted information clustering identification based on network security, and the method comprises the steps: collecting payload feature data of a real-time intercepted data packet of a firewall to form an original intercepted data set; the data set is preprocessed, and normalized payload data is obtained; performing fuzzy clustering on the normalized data by adopting a fuzzy C-means algorithm to obtain a clustering group and a clustering center feature set; and based on the feature set, performing depth feature extraction on each clustering group through a convolutional neural network, fusing the obtained depth abstract features and the corresponding clustering center feature set into a feature vector, and finally generating a clustering recognition result of the firewall interception information. Accurate mining and automatic identification of the potential threat mode in the firewall intercepted data are realized.