43 results about "Cluster type" patented technology

The invention provides a Docker Registry management optimization mode. The Docker Registry management optimization mode comprises the steps that based on Docker Registry management requirements, a Docker Registry native management mode corresponding to the management requirements is determined; based on the management requirements, a corresponding optimization management mode is determined; optimization is conducted on the native management mode through the optimization management mode, and corresponding Docker Registry management operation is conducted through an optimized operational order. According to the embodiment, the problems that the native Docker Registry control operational mode is unfriendly, management operation is complex, and the network operation and maintenance cost is too high are solved. For example, storage catalogs of mirrored documents are changed to cluster-type share mirrored storage equipment from a local storage area, namely, single-machine mirrored storage is changed to cluster-type mirrored storage, and on the condition of large-scale cluster-type machinery equipment, the mirrored document maintenance cost can be greatly lowered. For another example, by means of the mode that multiple Registry service operation is simplified, the operating difficulty of Docker Registry management is lowered, and the network maintenance operational experience is improved.

The invention relates to the technical field of invoice data processing, and discloses an electronic invoice batch processing and storage method and system based on data encryption, and the method comprises the steps: obtaining original electronic invoice data; performing calculation on the original electronic invoice data through a data encryption algorithm to obtain encrypted electronic invoice data; inputting the encrypted electronic invoice data into a clustering model to obtain a clustering result, wherein the clustering result comprises a class cluster center, a class cluster type and a class cluster density; according to the class cluster center, a class cluster distance between each class cluster is calculated through a distance formula; and adopting different storage schemes for the original electronic invoice data and the encrypted electronic invoice data according to the class cluster distance and a preset distance threshold. The method has the following effects that the data security is enhanced through the data encryption algorithm, the data distribution is automatically analyzed in combination with the clustering technology, the electronic invoice data is classified and stored based on the clustering result, the data storage space is reduced, and the data query efficiency is improved.

The invention relates to the technical field of information acquisition, and particularly discloses a cluster type textile machine information acquisition method and system based on a robot, and the method comprises the steps: receiving a time-containing signal uploaded by each module, and generating a signal matrix containing an identification label and a time label; identifying the signal matrix containing the identification label and the time label, and determining the anomaly degree of each textile machine; determining an information acquisition path of the robot based on the anomaly degree of each textile machine, and sending the information acquisition path to the robot; when the robot returns, reading collected data of the robot; the robot is additionally arranged between the data total end and the textile machine, the robot moves to the position close to the textile machine to carry out high-frequency data collection, the data total end carries out low-frequency collection on data of all the textile machines, and then the data collection process of the robot is adjusted, in this architecture, the collection frequency of the data total end can be very low, and the collection efficiency is high. And the resource consumption is greatly reduced.

The invention discloses a distributed cooperative control method and system for cluster temperature controllers, and relates to the technical field of temperature controller control. Comprising the following steps: establishing a temperature control consensus: establishing a temperature controller alliance chain, and adopting a practical Byzantine fault-tolerant algorithm to achieve a temperature control target consensus; sharing a prediction model: based on a federated learning architecture, each node locally trains a temperature prediction model and uploads a model parameter gradient to a cloud server for aggregation updating; global energy consumption optimization: adopting a multi-agent depth deterministic strategy gradient algorithm to carry out minimization optimization on the global energy consumption; and dynamic topology recombination: supporting plug and play of the equipment and automatically recombining the cluster topology according to the state of the equipment. According to the method, the information island problem is solved, sharing and privacy protection of the temperature prediction model are realized, global energy consumption is optimized, cluster temperature control consistency can be improved, energy utilization efficiency can be optimized, user comfort experience can be improved, hundred-level equipment expansion is supported, and complex scene requirements are met.

The invention provides a source-load collaborative load prediction method and system based on dynamic clustering and trust management, and belongs to the technical field of power distribution network optimization operation, and the method comprises the steps: carrying out the seasonal scene division based on the historical load data of each distribution transformer device in a region, carrying out the clustering of the devices based on a dynamic time warping distance algorithm, and carrying out the clustering of the devices; outputting an equipment cluster type and a corresponding standardized load form curve; according to the cluster type to which the equipment belongs, combining with the real-time output data of the new energy, dynamically correcting the equipment demand coefficient and generating an equipment-level load prediction value; overlapping the load prediction values of the same cluster equipment to generate a cluster-level load value, and aggregating all clusters to obtain a regional total load prediction value; calculating a source load matching degree quantitative index and a new energy output prediction volatility, fusing the two to generate a time-varying trust value, dynamically adjusting a new energy output prediction weight based on the time-varying trust value, and outputting a regional net load prediction value; and according to the regional net load prediction error closed-loop optimization demand coefficient correction parameter.

The invention discloses a cluster type data management system and a node management method, the cluster type data management system manages each data node through a registration center, including filter deployment, capacity expansion and contraction and the like, that is to say, cluster management can be carried out on Bloom filters deployed on the data nodes through the registration center; bloom filters on all data nodes can be added, modified or deleted in a one-stop mode, service requirements can be quickly responded, and mass data can be processed more efficiently compared with a Bloom filter carried by a single machine; according to the node management method, each data node is mapped on a Hash ring by constructing the Hash ring and corresponding Hash calculation, and node management and Bloom filter management are performed on the basis, so that uniform storage of data can be realized, more storage resources are saved, and the problem of data synchronization among the nodes is avoided.

The invention provides a control method of a cluster type hot standby redundant system and a train, and can be applied to the technical field of vehicle control and the field of communication. The priority of a target system which the target device belongs to in the cluster type hot standby redundant system or the instruction running state of the target device is determined, the target system comprises a plurality of devices, the cluster type hot standby redundant system comprises a plurality of systems, the systems are classified into a main system and a hot standby system, the main system is used for managing communication data of a train, and the hot standby system is used for managing communication data of the train. The hot standby system is used for backing up communication data; and according to the priority or the instruction operation state, performing main-standby switching on the target system.

The invention discloses a job scheduling method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining a to-be-processed calculation job from a first scheduling queue in a plurality of scheduling queues; determining a target computing cluster from a plurality of computing clusters based on the metadata of the to-be-processed computing job, wherein the plurality of computing clusters comprise heterogeneous computing power clusters of different cluster types; converting the to-be-processed computing job into a target computing job conforming to the job submission mode of the target computing cluster, and distributing the target computing job to the target computing cluster; and in response to resource state update triggered based on release of the heterogeneous computing power resources, performing scheduling state evaluation on the computing jobs in the second scheduling queue, and when a scheduling state evaluation result indicates that the computing jobs can be scheduled, transferring the computing jobs to the first scheduling queue. According to the method and the device, the job processing efficiency is improved while the combined scheduling of the computing clusters of different cluster types is realized.

The invention discloses a test system and a test method for action reliability of a cluster-type intelligent load switch. The cluster type intelligent load switch action reliability test system is provided with a background monitoring main station, a data acquisition control card, a molded case circuit breaker, a contactor cluster, an adjustable load, a temperature measurement sensor, a voltage transformer, a current transformer, a temperature measurement sensor, a scram button, an indicating lamp, a buzzer and a fan. According to the test system and the test method for the action reliability of the cluster type intelligent load switch, the action reliability of a plurality of tested intelligent load switches can be automatically tested and monitored in real time, the test data is displayed and stored in real time, the test result is judged, and the test report is automatically generated through one-time wiring.

The invention discloses a cluster type breeze power generation terminal maintenance plan management system, and relates to the technical field of intelligent operation and maintenance. Comprising a data fusion module for fusing SCADA data, laser radar wake flow scanning and power grid PMU data to obtain a health state vector; the influence evaluation module is used for obtaining fan adjustment cost factors of the fans according to the health state vectors, and determining a composite influence value of each fan through an LSTM network model; and the coordinated maintenance module is used for determining the credit score of each fan according to the composite influence value and sorting the maintenance sequence of each fan. According to the method, after the fans are preliminarily graded according to the credit scores, the fans are secondarily divided according to the maintenance scores in each grade, so that the fans with high influence and low credit can be preferentially processed, and the marginal benefits of maintenance resource investment are improved.

The invention discloses a computing power leasing scheme generation method and device and a storage medium, and relates to the technical field of data processing, and the disclosed computing power leasing scheme generation method comprises the steps: determining computing power levels corresponding to all tasks of a target business scene, and determining cluster types and cluster configuration information corresponding to all the computing power levels; constructing a computing power cluster corresponding to each computing power level according to the cluster type and the cluster configuration information corresponding to each computing power level; scheduling each task to the corresponding computing power cluster for execution to obtain total execution overhead; and if the total execution overhead meets the expected execution overhead, according to the cluster type and the cluster configuration information corresponding to each computing power level, generating a computing power leasing scheme adaptive to the target business scene, thereby solving the problem of high computing power leasing cost of a current computing power leasing scheme generated based on a specific business scene, and reducing the computing power leasing cost.

The invention discloses a data search method and device, computer equipment and a medium, and relates to the technical field of data search, and the method comprises the steps: obtaining a target data set, and generating an initial cluster based on the target data set; performing sub-cluster division on the initial cluster to obtain a target cluster index; wherein the farthest data points of the data points in the same sub-cluster are the same; a target data point closest to the target query point in the target cluster index is searched based on the cluster type of sub-clusters in the target cluster index, and the cluster type comprises non-leaf nodes and leaf nodes. According to the method, in order to avoid large difference of different clustering scales, a large-scale cluster is further divided into sub-clusters until all the clustering scales are similar, so that a target clustering index is obtained, and hierarchy of the index is ensured. Searching is carried out based on the types of the sub-clusters, the situation that the searching cost is too high due to the fact that the subordinate sub-clusters of the factor clusters are too many can be avoided, and the searching accuracy and efficiency are improved.

The invention belongs to the field of electric vehicle charging systems. According to the cluster type charging system, a plurality of charging devices (including charging piles and charging terminals) are connected together through a direct-current line and communicate with one another, and charging power is allocated to one another. Therefore, any charging device can realize high-power charging and over-charging functions by calling idle charging capabilities of other charging devices in the system.

The application discloses a job scheduling method and device, electronic equipment and storage medium. The method comprises the following steps: obtaining a to-be-processed computing job from a first scheduling queue in a plurality of scheduling queues; determining a target computing cluster from a plurality of computing clusters based on the metadata of the to-be-processed computing job, wherein the plurality of computing clusters comprise heterogeneous computing power clusters of different cluster types; converting the to-be-processed computing job into a target computing job consistent with the job submission mode of the target computing cluster and distributing the target computing job to the target computing cluster; in response to a resource state update triggered based on the release of the heterogeneous computing power resource, performing scheduling state evaluation on a computing job in a second scheduling queue, and when the result of the scheduling state evaluation indicates that the computing job can be scheduled, transferring the computing job to the first scheduling queue. The application improves the job processing efficiency while realizing the joint scheduling of computing clusters of different cluster types.

The application discloses a cloud hard disk-based database capacity expansion method and device, and relates to the technical field of cloud computing. A specific implementation mode of the method comprises the following steps: determining the cluster type of a database to be expanded; determining the capacity expansion strategy of the cluster instance of the database to be expanded according to the cluster type of the database to be expanded and the residual resource quantity of the physical machine where the cluster instance of the database to be expanded is located; and expanding the corresponding cluster instance in the database to be expanded according to the determined capacity expansion strategy. The implementation mode can shorten the capacity expansion time, improve the success rate of the data replication process, make the user variable allocation time more flexible, reduce the temporary resource usage, and avoid the problems of resource shortage and scheduling imbalance.

The invention discloses a data processing method, computer equipment, a storage medium and a program product. The first cluster and the second cluster with different cluster types are determined based on the cluster selection instruction. And determining target tasks of the first cluster and the second cluster based on the task selection instruction. And then determining to-be-processed data according to the target task and the corresponding target task parameter. And finally, performing data processing corresponding to the target task on the first cluster and the second cluster based on the to-be-processed data. Therefore, personalized selection of the cluster type and the to-be-processed data can be realized through the self-defined cluster selection instruction and the task selection instruction to perform data processing, so that the flexibility of cross-cluster data processing is improved.

The embodiment of the present application provides a chamber leakage detection method and a cluster type semiconductor process equipment, the cluster type semiconductor process equipment comprises a transmission platform and chambers located around the transmission platform, the chambers are connected with the transmission platform through valves, and each chamber is independent of each other, and the method comprises the following steps: determining a master chamber and slave chambers of the master chamber from the transmission platform and the chambers; and detecting the leakage of a vacuum cavity composed of the master chamber and the slave chambers. According to the embodiment of the present application, whether the chambers of the semiconductor process equipment are connected with the platform and have air leakage can be detected.

The application relates to a cluster type medical building rapid construction BIM model construction method and system, a thinking tree with multiple attributes is constructed based on design requirements, pruning is carried out in combination with a medical prototype library and compliance constraints, a DAG is generated according to leaf node tasks of the thinking tree after pruning, inter-cluster linkage is used to assign a mutual dependence key level to DAG edges, a weighted DAG is formed, a planning agent determines task priorities and resource allocation according to the weighted DAG, an execution sequence is generated, a BIM code generation agent processes tasks according to the sequence, and a component code is generated through a thinking chain process with phased confidence score evaluation; at the end of each stage, confidence is calculated according to node attributes and medical geometric rules; if the confidence is lower than a threshold value dynamically adjusted based on cluster unit complexity, the thinking chain is adjusted and restarted. When the confidence is continuously too low or a key task cluster is completed, a check and correction agent is intervened to check and correct the generated code; the corrected code is collected to construct a BIM model.

The invention discloses a cluster release batch generation method and device, a medium, equipment and a program product. The method comprises the following steps: grouping and sorting a plurality of target clusters based on cluster types of the target clusters and release priorities corresponding to the cluster types to obtain at least one cluster group and a release sequence of the cluster groups, the release sequence of the cluster groups being in positive correlation with the release priorities; for each cluster group, based on the number of nodes of each target cluster in the cluster group, determining a release sequence of each target cluster in the cluster group, the release sequence of each target cluster being negatively correlated with the number of nodes; and based on the publishing sequence of each cluster group and the publishing sequence of each target cluster in each cluster group, determining the publishing sequence of each target cluster in the plurality of target clusters, and based on the publishing sequence of each target cluster in the plurality of target clusters, generating at least one publishing batch. Mass clusters of edge scenes can be automatically sequenced and automatically divided and released in batches, and the batch generation efficiency is improved.

The present application discloses a method, device and storage medium for generating a computing power leasing plan, which relates to the field of data processing technology. The disclosed method for generating a computing power leasing plan includes: determining the computing power level corresponding to each task of the target business scenario, and determining the cluster type and cluster configuration information corresponding to each computing power level; building a computing power cluster corresponding to each computing power level according to the cluster type and cluster configuration information corresponding to each computing power level; scheduling each task to the corresponding computing power cluster for execution to obtain a total execution overhead; if the total execution overhead meets the expected execution overhead, generating a computing power leasing plan adapted to the target business scenario according to the cluster type and cluster configuration information corresponding to each computing power level, thereby solving the problem of high computing power leasing costs in the current computing power leasing plans generated based on specific business scenarios and reducing computing power leasing costs.

The invention discloses a log analysis method, device and equipment and a computer readable storage medium, relates to the technical field of information processing, and aims to solve the problem of low automation degree of a log analysis method. The method comprises the steps of obtaining feature information of each log in a to-be-analyzed log data set; constructing a hierarchical tree model corresponding to the feature information based on a minimum spanning tree; performing clustering processing on logs in the to-be-analyzed log data set based on a hierarchical tree model, and obtaining a tree structure corresponding to each clustering type according to a clustering result; according to the target clustering type and a target tree structure corresponding to the target clustering type, performing spanning tree processing on the logs of the target clustering type to obtain an analysis template spanning tree of the target clustering type; and analyzing the logs of the target cluster type according to the analysis template spanning tree to obtain a log analysis result. According to the embodiment of the invention, automation of the whole log analysis process can be realized.

The invention relates to the field of wiring busbars, in particular to a wiring busbar of a cluster type user line, which comprises a busbar body, a wiring terminal module and a heat dissipation shell, and is characterized in that a manufacturing method comprises the following steps: S1, preparing a copper-aluminum composite busbar base material by adopting a rolling composite method, and carrying out synchronous hot pressing bonding on multiple layers to form a layered structure; s2, conducting channels are etched on the surface of the busbar base material through laser, and multiple branch wiring areas are formed; s3, under the protection of inert gas, fixing the sectional wiring terminal module in the wiring area through high-energy beam welding; s4, performing surface treatment on the surface of the busbar to improve the protection property of the busbar; s5, adding a heat dissipation structure between the busbar body and the heat dissipation shell; s6, processing the wiring terminal module, and integrating a monitoring module in the wiring terminal module; and S7, jointing the heat dissipation shell with the sealing insulating cover, and performing waterproof treatment on the busbar. According to the invention, the wiring busbar is enabled to adapt to a vibration environment, and the problem of thermal expansion of a local area of the wiring busbar is solved.

The invention relates to a high-entropy Ni-Fe-Cr-based high-temperature alloy designed based on a cluster type method and a preparation method and application of the high-entropy Ni-Fe-Cr-based high-temperature alloy. The alloy is designed based on a 16-atomic component formula of a cluster and connecting atom model, the components of the alloy fall within the range of Ni7-6Fe5-6 (Cr, Mo, Si, Nb and Mn) 4-5 in a Ni-Fe-Cr-like pseudo ternary diagram, a single-phase face-centered cubic structure is formed, and NbC is dispersed and separated out. The Cr-like element content is increased through high-entropy alloying, stability and high strength (the tensile strength is larger than or equal to 460 MPa) are kept at the high temperature on the premise of not depending on gamma-phase strengthening, and meanwhile excellent corrosion resistance is achieved. The alloy is low in cost, simple in process and suitable for force-bearing structural parts under the high-corrosion working condition of 600 DEG C or below, and has good industrial application prospects.

The invention discloses a cluster type electrical potential safety hazard pre-judgment method, system and device and a storage medium. The method comprises the steps of obtaining a real-time data stream, performing data processing on the real-time data stream to obtain a first data stream, performing feature extraction by using an edge computing node, and screening abnormal features to obtain an abnormal feature set; analyzing the abnormal feature set by using a deep learning model, modeling time sequence features in the abnormal feature set through a neural network model to obtain a potential risk probability of electrical hidden dangers, and optimizing deep learning model parameters by using an update algorithm to obtain updated model parameters; extracting neural network model parameters related to the edge end computing node, returning the neural network model parameters to the edge end computing node to obtain an optimized edge model, and reasoning the real-time data stream to obtain a real-time hidden danger early warning result; and a real-time hidden danger early warning result is judged to obtain an electrical potential safety hazard early warning instruction, so that a powerful guarantee is provided for safe and stable operation of a power system.

The application relates to a cluster type safety computer platform, which is centrally deployed, can realize centralized maintenance and is favorable for improving system availability. The safety computer platform is deployed on a track side and does not need to bear the severe working environment of a vehicle-mounted device, is high in reliability and can adopt high-performance hardware, thus being favorable for realizing more intelligent functions. The safety computer units in the safety computer platform communicate based on a wired network, and compared with the wireless communication between a vehicle-mounted safety computer platform and a track-side safety computer platform in a traditional train control system, the wired communication is better in reliability. A master and standby database cluster is introduced, the statelessness of safety applications can be realized, data synchronization during master and standby switching of the safety applications is avoided, the switching speed is accelerated, and the consistency of data during the master and standby switching process is ensured. The large-scale safety computer cluster platform is realized, and the problems existing in real-time performance and determination of virtualization on the safety computer platform do not need to be faced.

The utility model discloses a safety remote monitoring system for a cluster type flywheel energy storage system, which belongs to the field of flywheel energy storage and comprises an upper computer, a control cabinet, a first unit terminal execution device and a second unit terminal execution device. The unit terminal execution equipment II comprises an energy storage motor system, an energy storage safety pit, a converter system, a video monitoring system and a sensor monitoring system; the sensor monitoring system obtains operation data of the energy storage motor system and the converter system and transmits the operation data to the control cabinet; the video monitoring system obtains video data of the energy storage safety pit and transmits the video data to the control cabinet through a video signal; the control cabinet processes the collected data and then transmits the data to the upper computer; and the upper computer sends out a corresponding control signal so as to finish remote monitoring. According to the utility model, the remote monitoring of the operation state can be realized, and the safety of personnel and equipment is ensured.

The application provides a cloud computing platform task scheduling method, device and equipment, and the scheme selects appropriate sorting parameters based on the cluster type of the scheduling task, so that when scheduling is performed based on the sorting result, compared with the list heuristic, the parallel tasks can be processed in clusters, and the execution time is reasonably reduced; compared with the clustering heuristic, the tasks can be pre-sorted, the execution order is more reasonable, and the waste of idle time resources is reduced.

The invention discloses a resource determination method and device, a medium and a program product, and relates to the technical field of computers, and the method comprises the steps: receiving a resource determination request sent by a user side, determining a target resource pool from a plurality of resource pools, and controlling the resource pools to create target resources, the target resources belonging to the target resource pool, therefore, the specific target resource is determined for the user, the target resource pool can be intelligently determined, then the target resource is determined, manual intervention is reduced, and efficiency is improved; in addition, the resource pool belongs to different storage cluster types, so that how to determine the target resource in the heterogeneous storage cluster is realized; in addition, due to the fact that the heterogeneous storage clusters are different in type, compatibility is poor, the access link in the preset unified format is generated for the access information, manual adaptation operation of the user due to different types can be reduced, and user experience is improved.

This invention provides a cluster type identification method, apparatus, electronic device, and storage medium. The method obtains the test features of the cluster to be tested. Since the test features include at least the importance parameters corresponding to each member of the cluster in each preset behavioral parameter category, the test features can characterize the behavioral characteristics between members in the cluster and the importance of each member in each preset behavioral parameter category within the cluster. Because the cluster type prediction model is obtained by training a neural network, it can obtain the cluster structure of the cluster to be tested from multiple angles and dimensions based on the test features. Furthermore, since the training objective of the cluster type prediction model is to minimize the length of the true vector and the difference vector, the cluster type prediction model can accurately predict the probability that the cluster to be tested belongs to any known cluster category.

The invention discloses a cluster type network file system state recovery method and system, and the method comprises the following steps: taking over the service of a target server after the target server fails and obtains a virtual IP of the target server, and entering a grace period to rebuild a file state; within the grace period, refusing to receive a new non-file state reconstruction operation request sent by the target client; reading an IP (Internet Protocol) of a target client associated with the target client and stored in the sharing recovery module, so as to send a recovery operation request to the target client information; receiving pre-stored file state operation request records sent by the target client, wherein the stored file state operation request records are all file state operation records sent by the target client to the target server; sequentially executing all the file operation requests according to the file operation request record; and ending the grace period, and receiving a new file operation request sent by the target client. According to the invention, the reliability of file state recovery can be ensured.