14 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 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 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 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 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 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.

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 application discloses a cluster type orderly charging and discharging management system based on LoRa Internet of Things, relates to the technical field of power system management, and is used for solving the problem of how to realize accurate state perception and optimal scheduling of the cluster charging and discharging system under the condition that LoRa Internet of Things communication is limited. The key state under the condition of missing report and time delay is kept continuously available by establishing a belief state of consistency checking and time sequence tracking. The power instruction sequence is formed in the rolling time domain based on the state, and the information value density is obtained through the sensitivity of the target function, so that the reporting set and the time stamp arrangement are organized according to the priority, the key information in the limited air interface is reached first, and the instruction is aligned in the cycle. When the difference between observation and prediction increases, local correction is triggered to limit the adjustment range and quickly converge the error, and the record and index are written back to the next cycle, so that the estimation and the instruction are kept trackable and consistent in the cross-cycle execution.

The application provides a feature clustering method, device and equipment and a storage medium, and relates to the technical field of clustering. The method comprises the following steps: obtaining multi-dimensional feature data corresponding to a plurality of users respectively; clustering the plurality of users according to the multi-dimensional feature data and a preset clustering number to obtain an initial clustering graph; the initial clustering graph comprises a preset clustering number of clustering types and a clustering type corresponding to each user; calculating a position offset between an initial position of each user and a center position of a plurality of clustering types according to the multi-dimensional feature data of each user; determining a target position offset corresponding to each user according to the position offsets; and adjusting the initial position of each user on the initial clustering graph according to the target position offset to obtain a target clustering graph. Compared with the prior art, the method avoids clustering of multi-dimensional data and the problem that the clustering result is not visible.

The invention belongs to the technical field of intelligent manufacturing and industrial automation control, and particularly relates to a fan mold cluster type self-adaptive precise temperature control system. The system comprises a thermal field sensing and modeling module, a dynamic scheduling generation module, a collaborative optimization module, a fault prediction and reconstruction module and a data closed-loop management module. According to the system, a multi-mode-cavity thermal coupling topology model is constructed through a graph neural network, intelligent distribution of a cooling medium is realized in combination with an improved particle swarm algorithm, equipment faults are predicted based on LSTM to trigger redundancy switching, and finally safe and efficient data management is realized through an edge-cloud collaborative architecture; the temperature control precision and energy efficiency of the cluster mold can be improved, and the local temperature control stability and the global operation reliability are both considered.

The application discloses a cluster type temperature controller distributed collaborative control method and system, and relates to the technical field of temperature controller control. The method comprises the following steps: establishing temperature control consensus, constructing a temperature controller alliance chain, and achieving temperature control target consensus by using a practical Byzantine fault tolerance algorithm; sharing a prediction model, training a temperature prediction model locally based on a federal learning architecture, and uploading model parameter gradients to a cloud server for aggregation and updating; globally optimizing energy consumption, minimizing and optimizing global energy consumption by using a multi-agent deep deterministic policy gradient algorithm; and dynamically reorganizing a topology, supporting plug and play of equipment, and automatically reorganizing a cluster topology according to equipment states. The application solves the information island problem, realizes sharing and privacy protection of the temperature prediction model, and optimizes global energy consumption, so that the cluster temperature control consistency can be improved, energy utilization efficiency can be optimized, user comfort experience can be improved, hundreds of equipment extensions can be supported, and complex scene requirements can be met.