29 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 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 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 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 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 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.

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 present application relates to a network system on chip, in particular to a kind of multi-level cluster type three-dimensional network system on chip for neuron network simulation.A kind of multi-level cluster type three-dimensional network system on chip for neuron network simulation includes multiple functional nodes, functional node includes vertically stacked routing chip and multiple computing chips, routing chip is arranged at the bottom of multiple computing chips;Computing chip includes processing core, first-level router, second-level router and higher level router, routing chip includes second-level router and higher level router, and the highest level router of computing chip is the same level with the highest level router of routing chip.The present application adopts multi-level cluster type three-dimensional network structure design on chip, and the connection distance between chip is shortened by three-dimensional stack in functional node interior, so as to reduce inter-chip data transmission delay, improve the computing efficiency of system.

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 invention relates to an unmanned platform state updating method for large-scale cluster simulation, belongs to the technical field of unmanned platform cluster simulation, and solves the problems of large calculation amount and low speed when the existing unmanned platform cluster simulation is used for updating the state of an unmanned platform. Comprising the following steps: obtaining the type of each unmanned platform in an unmanned platform cluster and a control instruction of each unmanned platform; dividing the unmanned platform cluster into a rotor wing cluster and a fixed wing cluster based on the type of each unmanned platform, and initializing the state of each cluster; the cluster state comprises a cluster linear state and a cluster attitude state; and based on the cluster type, each cluster state at the current simulation moment and the control instruction of each unmanned platform, performing state updating of each cluster, taking each updated cluster state as each cluster state at the next simulation moment, and repeatedly executing the step to perform state updating until the simulation is finished. And completing state updating of the unmanned platform state at each simulation moment.

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

The invention discloses a high-speed radio frequency hopping sequence reverse analysis device which comprises an Ethernet communication interface physical driving module, a central processing module, an ASIC hardware IO array trigger aggregation processing module, a CANFD subnet communication interface physical driving module, an ASIC hardware FIFO cluster array cache module and a cluster type radio frequency front end scanning module. The Ethernet communication interface physical driving module is connected with the central processing module, and the central processing module is respectively connected with the ASIC hardware IO array trigger aggregation processing module, the CANFD subnet communication interface physical driving module, the ASIC hardware FIFO cluster array cache module and the cluster type radio frequency front end scanning module. The cluster type radio frequency front end scanning module is respectively connected with the ASIC hardware IO array trigger aggregation processing module, the CANFD subnet communication interface physical driving module and the ASIC hardware FIFO cluster array cache module. The technical problems that an existing communication device is single in function, and wireless frequency hopping communication function detection cannot be achieved are solved.

The invention relates to the technical field of data processing, and particularly provides a heterogeneous computing task scheduling method, system and device and a storage medium, and the method comprises the steps: recognizing the task type of a to-be-allocated task, and extracting the feature data of the task; inputting the feature data into a prediction model corresponding to the task type, and outputting a target computing resource type which belongs to an edge device hardware type or a cloud cluster type; obtaining the available resource quantity of a plurality of candidate computing resources belonging to the target computing resource type; and screening out the target computing resource with the maximum available resource quantity from the candidate target computing resources, and allocating the task to the target computing resource. According to the method, the task execution efficiency can be greatly improved, the response delay is reduced, and the stability and throughput capacity of the whole heterogeneous computing system are enhanced.

The present application relates to the technical field of computer, and discloses a test parameter determination method and device, electronic equipment and storage medium, since the storage cluster type and cluster state value are acquired first, the first storage pool is determined, and the first test parameter set is determined according to the type, attribute information and cluster state value, and then the first test parameter value is determined, then the second storage pool is determined according to the storage cluster type and the first storage pool type, the second test pool parameter set is determined according to the type, attribute information and corresponding cluster state value, and then the second test parameter value is determined, finally, the target test parameter value is determined according to the first test parameter value and the second test parameter value, a variety of complex test scenarios are covered through different storage cluster types and cluster state values, and then the target test parameter value meeting the actual test requirements is determined through the first test parameter value and the second test parameter value meeting the constraint conditions, so that the accuracy and authenticity of the test are improved.

The invention provides a cluster type magnetic modulator sharing demodulation, and relates to the technical field of electrical measurement and signal processing. The device consists of a plurality of closed annular magnetic modulators, and is sleeved on a monitoring line to realize non-contact measurement; the voltage excitation unit provides square wave excitation with different fundamental wave frequencies for each magnetic modulator; the signal output unit combines multiple paths of analog outputs, converts the analog outputs into digital signals through the acquisition module, extracts amplitudes at corresponding double frequencies through windowing Fourier analysis, and converts the amplitudes into measurement results of all channels in combination with proportionality coefficients, so that centralized demodulation of multiple channels is realized, the number of hardware is reduced, the cost and the wiring complexity are reduced, and the measurement accuracy is improved. The precision and the sensitivity are ensured; and the system integration degree and the adaptability are improved.

The invention discloses an intelligent attendance system and method applying cluster type multi-person face dynamic recognition, and relates to the technical field of big data analysis. A sign-in time curve graph is constructed by selecting a unique identity label of a sampling target, and the sign-in time is predicted in combination with a machine learning algorithm; weighted fusion calculation is carried out according to sign-in time feature marks, individual sign-in time is accurately predicted, the difference between historical rules and personnel sign-in habits is mined, and a reliable time basis is provided. The method comprises the following steps: extracting sampling target historical face recognition time consumption and times to calculate average time consumption duration, creating a pre-cache region in combination with pre-sign-in time sorting, pre-estimating a sign-in sequence in advance, pre-loading data, relieving high-concurrency pressure, and ensuring stable operation of a system. According to the method, positive and lazy sign-in feature intervals are divided and marked, basic values are selected according to different features, pre-sign-in time is determined in combination with face recognition time consumption, quantitative classified management of sign-in behaviors is achieved, and a pre-caching strategy is optimized.

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.