176 results about "Cluster group" patented technology

The invention discloses an aerospace network hierarchical cooperation routing optimization method and system, and relates to the field of aerospace information network communication, and the method comprises the steps: constructing a system model comprising an unmanned plane, satellite nodes and a communication link, defining link attributes, building a routing optimization objective function, introducing a reputation value-based multi-agent cooperation mechanism, and carrying out the routing optimization of the unmanned plane, the satellite nodes and the communication link. A hierarchical reinforcement learning framework is designed, inter-cluster decision and intra-cluster decision are divided, an upper layer selects an inter-cluster path according to global information, a lower layer plans an unmanned aerial vehicle path and defines a state, an action space and a reward function according to local information, an intelligent agent interacts with an environment to collect empirical data, and optimal routing communication is selected in combination with a real-time reputation value after training. Adjustment can be carried out according to network changes; according to the invention, the cooperative reliability is improved through a reputation value mechanism, the decision complexity is reduced through hierarchical learning, the routing performance is optimized, the network robustness, expandability and adaptability are enhanced, and the cooperative communication efficiency of the unmanned aerial vehicle and the satellite and the system stability are improved.

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.

The invention relates to an energy storage power station SOC automatic calibration and inter-cluster dynamic balance cooperative control method. The method comprises the following steps: acquiring charge state parameters of a sodium ion supercapacitor cluster group preset in an energy storage power station; calculating a corresponding charge change rate based on the charge state parameters to judge whether calibration is abnormal or not; when the calibration is abnormal, the working modes of the cluster group are divided according to the charge state parameters, and a calibration period is set for each working mode; a local equilibrium buffer is additionally arranged on a preset management end of the energy storage power station, a previous calibration value is allowed to be returned within a preset time period, and the returning frequency of the previous calibration value is recorded; and when the return frequency of the previous calibration value is greater than the preset frequency, sending a calibration refresh request to the management end of the energy storage power station, shortening the calibration period, forcibly obtaining the latest SOC value, filling the missing estimated value, optimizing the operation efficiency of the energy storage power station through an adaptive calibration algorithm and hierarchical equalization, reducing redundant operations, and improving the reliability of the energy storage power station. Missing data is compensated through historical data trends, and the safety and reliability of the system are improved.

The invention discloses an intelligent campus student activity trajectory tracking method and system based on front and rear end fusion learning, and relates to the technical field of intelligent campus security. An embedded front end collects videos of a plurality of monitoring cameras of a campus, carries out target detection and extracts primary features of student targets; uploading the extracted primary features and the video to a back-end server; and the back-end server extracts advanced features of human body postures and gaits from the video by using a human body posture estimation algorithm and a gait recognition algorithm, fuses the advanced features and the primary features to obtain fused features, and clusters all detected student targets by using the fused features, thereby realizing cross-camera identity unification of the same student under different cameras, and improving the identification accuracy of the student. Face detection and recognition are carried out in each clustering group, a student information base is matched to determine student IDs, and the student IDs, video timestamps and monitoring camera position information are stored; and the client retrieves target information according to the input student ID or photo, and automatically clips and generates a time-space continuous complete cross-camera trajectory tracking video. According to the invention, efficient and intelligent analysis of campus monitoring data is realized, and targets can be accurately associated and tracked in a cross-camera scene.

The invention discloses an alarm association rule generation method and device based on clustering, equipment and a medium. The method comprises the following steps: collecting alarm logs in response to an alarm log information collection instruction, and sorting the alarm logs according to a timestamp sequence; performing clustering processing on the sorted alarm logs based on an elbow rule to obtain a plurality of alarm clustering groups; dividing each alarm cluster group into at least one transaction according to a time window, and generating an item set; and performing frequent item set mining on each item set by adopting an association rule learning algorithm, generating an alarm association rule for describing an attribution relationship among the fault description information, and storing the alarm association rule in the graph database. Different from the prior art which only depends on timestamp sorting, the embodiment of the invention creatively puts forward that alarm logs in a time window are grouped by utilizing an elbow rule to form a high-cohesion alarm clustering group, so that subsequent association rule mining focuses on an alarm combination with strong space-time association, and the rule confidence is remarkably improved.

The invention relates to the technical field of cluster communication, in particular to a hundred thousand card cluster communication acceleration system based on intelligent topology awareness, which is characterized in that a distributed resource detection module, an execution analysis module, a label setting module, an awareness module and a topology optimization module are arranged for cooperative work; basic communication information of sub-clusters is obtained based on a distributed resource detection module, an execution analysis module determines a sub-cluster association relationship and constructs an association sub-cluster group, cluster interference characteristics are analyzed, and then a label setting module calculates a cluster interference characterization coefficient; the sensing module selects a sub-cluster needing interference analysis based on the cluster interference characterization coefficient, finally determines a change fluctuation value according to basic communication characteristics monitored by the topological optimization module on the sub-cluster, judges whether fluctuation interference abnormity exists in the sub-cluster or not, and calls the remaining sub-clusters for task allocation when the fluctuation interference abnormity exists. Therefore, the cluster performance and the operation efficiency are ensured, and the availability and the reliability during cluster cooperative work are ensured.

A control circuit accesses a digitized image of a particular item and then processes that digitized image to thereby assign various aspects of the digitized image to various dimensions in N-dimensional Euclidean space to thereby provide an N-dimensional representation. The control circuit then accesses a database containing a plurality of various clustered groups of N-dimensional representations. At least some of the clustered groups have a corresponding identifying label. Conducting a nearest neighbor search serves to identify a clustered group to which the particular item most likely belongs to thereby provide an identified cluster group. When the identified cluster group does not have a corresponding identifying label, the control circuit can then further determine whether a predetermined condition has also been met. When true, the control circuit alerts a user via a user interface that the identified cluster group should be assigned an identifying label.

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 an e-commerce platform marketing strategy generation method based on a generative adversarial network. The method comprises the following steps: S1, collecting user behavior data to construct a dynamic behavior data sequence; s2, performing semi-supervised hierarchical clustering on the dynamic behavior data sequence by adopting an R3GAN model; s3, constructing a staged progressive R3GAN strategy generation model according to the clustering group; s4, constructing a double-layer flying fox optimization algorithm to optimize strategy parameters; s5, deploying a marketing strategy and monitoring user response in real time; s6, constructing a strategy self-sensing adaptation mechanism, and dynamically adjusting a model parameter space; and S7, setting a strategy performance threshold value, automatically decommissioning and redeploying. According to the invention, the dynamic adaptability and accuracy of the marketing strategy are improved.

The invention relates to the technical field of file backup, and discloses a file backup method, device and equipment for a new technology file system (NTFS) and a medium, file metadata of a plurality of files to be backed up and cluster numbers of all occupied clusters in an NTFS partition can be obtained based on a master file table of the NTFS, and the file to be backed up can be backed up based on the cluster numbers of all the occupied clusters. The method comprises the following steps: determining each continuous cluster group and each discontinuous cluster in an NTFS partition, reading file data in the NTFS partition in a manner of reading the file data in the continuous cluster groups or the discontinuous clusters at one time, writing the file data into a backup storage space, and generating a bit chart of the backup storage space at the same time for recovering a file when needed. According to the method, traversing and backup of the files to be backed up one by one can be avoided, the reading efficiency and the writing efficiency of different file data in the NTFS are improved, and therefore the backup efficiency of the different files to be backed up is improved.

The invention provides a positioning deviation correction method and system based on an unmanned aerial vehicle, and the method comprises the steps: determining a deviation vector of each telegraph pole through obtaining a first historical mark position and an actual position of a first telegraph pole; performing clustering processing according to the first historical mark position to obtain each first clustering center and a corresponding first cluster; respectively evaluating the offset vector of the corresponding telegraph pole in each first cluster to obtain a corresponding comprehensive offset vector; clustering a second historical position of a second telegraph pole needing to be corrected subsequently and the first historical mark position to obtain a second cluster, matching the first cluster with the second cluster, and determining a first cluster where the second telegraph pole planned to be repaired subsequently is located; and according to the comprehensive offset vector corresponding to the first cluster group where the second telegraph pole is located, the second historical position is corrected, so that other maintainers can accurately find the position of the telegraph pole during subsequent telegraph pole maintenance.

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.

The embodiments of the present invention relate to the field of information and communication technology, and disclose a layout method, device, server and storage medium for a message middleware. In the present invention, a coupling set of topics is obtained; wherein, the topics in each coupling set have a coupling relationship; all topics are decoupled and grouped according to the coupling set of topics to obtain multiple topic sets; the multiple topic sets are respectively deployed in each message middleware container cluster, wherein one topic set is deployed in one message middleware container cluster. Through the above technical means, the topics of the message middleware are decoupled and grouped, and the message middleware is distributedly deployed in a cluster group composed of a message middleware cluster, so that the service pressure of multiple services accessing the message middleware is dispersed, the coupling between multiple services accessing the message middleware is reduced, and the working stability of the message middleware is improved, while also simplifying the deployment process of the message middleware.

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.

This application discloses a dynamic task allocation method and device based on dispatching and grabbing orders, relating to the field of unmanned aerial vehicle technology. The method comprises: determining the network communication delay of the target node within the cluster based on the task type; determining the task allocation method based on the task type and network communication delay; wherein the task allocation method includes dispatching and grabbing; determining the number of nodes required to execute the task, and reporting to the command node to redefine the task allocation method when the number of nodes allocated within the cluster group does not meet the task requirement. This method solves the problem of ensuring that cluster tasks can still be executed efficiently in actual flight environments with poor network communication quality.

A company that develops software applications for operating and managing autonomous vehicles has an array of developer teams working on many different projects. Successful continuous delivery of software applications for the company can depend on reliability of the cluster infrastructure. Platform owners may be most knowledgeable when it comes to setting up cluster group that spans multiple regions. A developer working on a project can select, on a project management system, a cluster group for deployment of the project. A cluster group can specify a name for the cluster group, a plurality of environment-stack channels, and one or more clusters in deployment infrastructure associated with each environment-stack channel. The project management system can seamlessly provision resources for the project according to the selected cluster group.

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 present application relates to a power grid dispatching method, apparatus, computer equipment, and storage medium. The method comprises: obtaining electric vehicle charging demand data; obtaining a clustering object difference dataset of electric vehicles based on the electric vehicle charging demand data, obtaining a dynamically adjustable parameter based on the clustering object difference dataset, obtaining a fused object difference item for electric vehicles at each charging station based on the clustering object difference dataset and the dynamically adjustable parameter, and forming an electric vehicle data distance matrix from the fused object difference items; normalizing the distance matrix corresponding to each charging station and performing clustering processing to generate electric vehicle charging cluster groups corresponding to each charging station; obtaining aggregated information based on the electric vehicle charging cluster groups and an electric vehicle dispatching potential model constructed based on charging demand, and generating an electric vehicle charging dispatching plan based on the aggregated information. The present method can effectively reduce the peak-to-valley difference in load at power grid nodes and save power grid operating costs.

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 present invention provides a method and system for detecting rail surface defects, relating to the technical field of rail defect detection. The method includes: performing validity analysis on an initial rail surface image to obtain rail surface data, including the rail surface image and the corresponding defect classification label; extracting image node features, constructing an original graph adjacency matrix and an original graph weight matrix, dividing cluster groups and reconstructing the internal structure of the cluster groups, and constructing a cluster group adjacency matrix and a cluster group weight matrix; inputting them into a hypergraph convolutional model, and performing topology through a persistent homology mechanism to obtain the original graph topological features and the cluster group graph topological features respectively, fusing them, and outputting the current defect detection data of the rail surface image; comparing the current defect detection data with the numerical values of the defect classification labels, calculating the prediction error, and iteratively optimizing to obtain the defect detection data. The present invention can solve the technical problems of low accuracy and efficiency in the traditional manual inspection and non-destructive detection of rail surface defects in the prior art.