304 results about "Node clustering" patented technology

The invention relates to the technical field of computers, and discloses a computing power resource multi-dimensional scheduling method and system based on dynamic weight, and the method comprises a data perception step, a weight generation step, an intelligent decision-making step and a scheduling optimization step. The system corresponds to the method. The method comprises the following steps: a data sensing step: collecting multi-dimensional state parameters of computing power nodes and carrying out feature modeling to construct a global feature space; a weight generation step: dynamically adjusting the weight of each dimension based on a machine learning model and a rule engine; an intelligent decision-making step of screening candidate nodes in the global feature space and evaluating priorities, generating an optimal node cluster and performing resource dynamic slice distribution; and a scheduling optimization step: monitoring an execution effect and performing closed-loop feedback so as to iteratively optimize a weight strategy and decision logic. The problems that in the prior art, the sensing dimension is single, and decision-making weight is rigid are solved, and multi-dimensional accurate sensing, dynamic weight decision making and elastic resource allocation of computing power resources are achieved.

The invention relates to the technical field of power loads, in particular to a power load dynamic optimization prediction method based on reinforcement learning, which comprises the following steps: acquiring real-time voltage frequency power of nodes, tracking frequency difference direction change of adjacent nodes, marking reverse disturbance to generate a distribution map, and identifying a frequent disturbance area according to the distribution map; clustering node groups with consistent fluctuation trends to determine a target area; monitoring power fluctuation inversion to lock energy steering nodes; constructing a prediction input set; comparing prediction and actual trends to extract a deviation interval, adjusting stride and rate of a reinforcement learning model, updating a decision and smoothing a curve, and outputting a power load dynamic prediction curve. According to the method, disturbance space-time dynamic identification is realized through multi-layer correlation analysis, judgment precision is improved through frequency power joint calibration, load path consistency and area extension are reflected through node clustering, power transmission tracking is enhanced through energy node identification, a feedback closed loop is constructed through offset monitoring, and curve continuity and response rate are improved. Stable convergence is predicted to be consistent with the trend under multiple disturbances.

The invention discloses a power grid air-ground cooperative emergency control system and method based on unmanned aerial vehicle multi-agent reinforcement learning, and belongs to the technical field of power system emergency control and intelligent cooperation. Comprising the following steps: a central coordination unit obtains a voltage state and a load importance degree of a power grid node and a position, energy and a communication state of an unmanned aerial vehicle cluster in real time, an emergency priority node is identified based on voltage recovery deviation and communication link quality, dynamic clustering is carried out, and a comprehensive communication demand priority of each node cluster is calculated; according to the method, the strong coupling optimization problem of power grid voltage recovery and emergency communication guarantee in a disaster environment is solved, the power supply reliability, the communication connectivity and the emergency response efficiency of the system are improved, the energy utilization of the unmanned aerial vehicle is optimized at the same time, and the energy utilization rate of the unmanned aerial vehicle is improved. The method is suitable for rapid recovery and cooperative scheduling of key infrastructures in extreme scenes such as earthquakes and typhoons.

The invention relates to the technical field of computers, discloses a heterogeneous computing low-delay communication method and system, and aims to solve the problem of high delay caused by high communication protocol overhead, lack of dynamic scheduling collaboration, memory migration redundancy and non-uniform cross-node communication abstraction in existing heterogeneous computing. The method comprises the following steps: receiving a task scheduling request and analyzing a task dependency graph; tasks are dynamically allocated based on node loads and link states; rDMA, NVLink or PCIe straight-through protocols are adaptively selected according to node types to establish communication channels; zero-copy data exchange is realized through a shared memory mapping buffer area; hardware timestamps are utilized to synchronize feedback delays with PTP to optimize scheduling. The system comprises a heterogeneous computing node cluster, a unified communication scheduling controller, a low-delay communication protocol stack, a shared memory mapping buffer area and a communication delay sensing task distributor. According to the scheme, the communication delay is remarkably reduced, and the throughput and the task execution efficiency are improved.

The invention discloses an intelligent power grid load prediction and dynamic response coordinated scheduling method, and relates to the technical field of power system automation, and the method comprises the steps: accessing intelligent ammeters, distributed power controllers and other devices of Modbus, IEC61850 and DL / T645 protocols through a multi-protocol adaptive gateway, and achieving data standardization; time stamps are calibrated by means of Beidou time service and an IEEE1588PTP protocol, and it is ensured that multi-source data synchronization errors are controllable; deploying an edge computing node cluster, distributing high-priority tasks to low-load nodes through an edge coordinator in combination with a load fluctuation level and a greedy algorithm, and ensuring real-time processing efficiency; the edge nodes generate short-term load prediction, and the cloud platform outputs medium and long-term prediction based on a historical data training model; and finally, the coordinated scheduling decision module fuses the two types of prediction results and the real-time parameters of the power grid, and generates a dynamic instruction to control the output of the adjustable load and the distributed power supply.

The invention discloses a distributed RID receiving system based on edge multi-source fusion, which relates to the technical field of low-altitude supervision, and is characterized in that a distributed RID receiving node cluster adopts a fixed node and maneuvering node collaborative grid deployment scheme to capture and preliminarily analyze low-altitude aircraft RID signals in real time, and node data is output after the low-altitude aircraft RID signals are processed by a primary fusion algorithm; the edge multi-source fusion processing unit is deployed in each management and control partition, converges node data in the area, completes calibration, de-duplication and noise reduction through a secondary fusion algorithm, and outputs structured RID data; the cloud management and control platform realizes global data overall planning, situation visualization, intelligent decision making and instruction issuing through a three-level fusion algorithm, and is linked with the urban low-altitude scheduling system to form closed-loop management and control; the clock synchronization module provides nanosecond time reference for the whole system. According to the method, the problems of incomplete coverage and insufficient data processing precision of a traditional RID receiving scheme are solved, and global sensing, accurate data processing and closed-loop management and control of the low-altitude aircraft are realized.

The invention relates to the technical field of data processing and storage, in particular to an asynchronous verification and batch storage system and method for high-concurrency voucher data. Certificate verification is executed based on the memory snapshot, and verification time consumption is improved from the millisecond level of database IO to the microsecond level of memory calculation; a batch persistence mechanism enables the throughput of the system to be improved by dozens of times, and a concurrent storage scene of ten thousand-level TPS can be easily supported; the voucher storage response time is reduced from a hundred millisecond level to a millisecond level; the response is immediately returned to the user after the verification is passed, the user almost does not need to wait, and the operation experience is smooth; the introduction of the message queue enables the system to have peak clipping and valley filling capabilities, and can deal with burst traffic leisurely; the multi-node cluster deployment supports horizontal capacity expansion, the fault of a single node does not influence the overall service availability, and the system reliability is improved.

The invention provides a remote sensing data processing task thermal replacement method based on a cloud architecture. The method comprises the following steps: constructing a comprehensive state model of nodes; carrying out structure standardization processing on the received original remote sensing data so as to carry out unified management on data sources; sensing the comprehensive state of each node in the data source and the node cluster in real time; extracting a graph structure of a current to-be-scheduled task, automatically selecting a most suitable scheduling algorithm according to the graph structure, and generating a task execution topological graph; performing task distribution and execution according to the task execution topological graph; sensing the comprehensive state of each node in real time in the execution process of the task, and determining a resource elastic scaling strategy of the node; fault detection is conducted on the nodes and the task execution environment, after it is judged that faults occur, the operator operation state is rebuilt in other available nodes based on operator-level local snapshots, tasks are recovered through hot loading, and persistent storage and output of result data are completed.

Embodiments of the present disclosure provide a data processing method, an electronic device, and a non-transitory storage medium. The method includes: acquiring, when at least one target node cluster corresponding to a multimedia data stream acquisition request is found based on a pre-created mapping relationship, target stream pulling information of a current node cluster corresponding to the multimedia data stream acquisition request; and determining, when the target stream pulling information meets a preset condition, a target server address corresponding to the multimedia data stream acquisition request based on node information of nodes in the at least one target node cluster and the current node cluster so that a multimedia server provides a target service corresponding to the multimedia data stream acquisition request based on the target server address.

The invention discloses a chromatographic data optimization processing method for a liquid chromatograph, and relates to the technical field, and the method comprises the following steps: collecting an original signal flow, carrying out preliminary peak detection, and generating an original peak list; extracting modal exclusive characteristics of each chromatographic peak; converting the modal exclusive features into a unified multi-dimensional semantic feature vector; calculating a time interval weight, and calculating a semantic relevancy weight based on the multi-dimensional semantic feature vector; correcting the time interval weight, generating a network edge weight, and constructing a peak correlation topology network by taking the multi-dimensional semantic feature vector as a node; identifying node clusters connected with high edge weights, and aggregating the node clusters into candidate compound entities; tracking a node signal intensity change track, and adding a conflict mark; conflict resolution arbitration is carried out on the candidate compound entities with the added conflict marks, dominant detector evidence is output, and an analysis report is generated. According to the method, the problems of poor data collaboration of multiple detectors, inaccurate peak identification and association and isolated data processing flow are effectively solved.

The invention relates to the field of distributed storage, and discloses a distributed memory system based on erasure codes and a dynamic repair mechanism. Comprising a data node cluster, a metadata management cluster, a bandwidth token management module, a dynamic repair composer and a repair agent node. And the data node cluster divides the object into strip generation data fragments and verification fragments and dispersedly stores the data fragments and the verification fragments according to a placement rule. And the metadata management cluster maintains object stripe mapping, a fragment position set, erasure code protection configuration and a version epoch, and records a repair intention log. And the bandwidth token management module grants repair execution permission according to the fault domain budget. The dynamic repair composer generates availability repair and durability repair tasks, the temporary check fragments are written in when the number of the available check fragments is lower than a threshold value in the availability stage, and the configuration fragments are recovered and the temporary fragments are recovered in the durability stage. And the consistency of version epochs is verified before the agent node is repaired and written back, and if not, recalculation is carried out.

The invention discloses a server data access identity authentication system, which relates to the technical field of data access identity authentication, and comprises a session credibility calculation component, which is configured to acquire multi-dimensional data of a user terminal through a distributed verification node cluster to obtain session context credibility, the dynamic time window generation component is configured to generate an authentication effective time window, the dynamic challenge difficulty adjustment component is configured to dynamically determine a behavior challenge difficulty coefficient, the challenge generation component is configured to obtain a challenge verification result, and the verification result processing component is configured to process the challenge verification result according to the behavior challenge difficulty coefficient and the challenge verification result. And the access authorization component is configured to decide whether to grant server data access authority to a user based on session context credibility and a challenge verification result, and the access authorization component is configured to decide whether to grant server data access authority to the user based on session context credibility and a challenge verification result. And the calculation efficiency and the user experience are optimized.

The invention discloses a traffic flow prediction method for a double-layer multi-scale dynamic graph convolutional network, and relates to the technical field of traffic, and the method comprises the following steps: constructing a double-layer structure of a traffic network, which comprises a node layer composed of traffic sensor nodes and a region layer formed by clustering nodes in the node layer, node layer traffic flow data and area layer traffic flow data are extracted; respectively mapping the node layer traffic flow data and the region layer traffic flow data to a potential space to obtain a node layer initial hidden state and a region layer initial hidden state; inputting the initial hidden state of the node layer and the initial hidden state of the region layer into a sequence containing at least one space-time module for processing so as to capture a dynamic space-time dependency relationship; the method has the effect of providing more reliable decision support for traffic management and travel planning.

The invention discloses a training fault tolerance method and device applied to a distributed training system and a chip product, and relates to the technical field of distributed training. The method comprises the following steps: for a distributed training system which comprises a plurality of computing node clusters and each computing node cluster comprises a plurality of computing node groups, the plurality of computing node groups are used for executing distributed training tasks of the computing node clusters in parallel, and the plurality of computing node clusters are used for executing distributed training tasks of the distributed training system in parallel; under the condition that a fault node occurs in the distributed training system, determining a fault node cluster from a plurality of computing node clusters, the fault node cluster being a computing node cluster comprising the fault node; and controlling the fault node cluster to cancel execution of the distributed training task corresponding to the fault node cluster, and controlling at least one residual computing node cluster to execute the distributed training task of the distributed training system. According to the embodiment of the invention, the training fault-tolerant capability and the training stability of the distributed training system can be improved.

The invention provides a power distribution network partitioning method and device, equipment and a storage medium. The method comprises the steps that firstly, a node voltage-reactive power sensitivity matrix and historical operation data of a distributed power supply are obtained, the structural distance between nodes is calculated, the historical data are normalized, the attribute similarity is obtained in combination with an attribute weight matrix, and the two are added to generate an electrical distance. Resolution parameters are dynamically adjusted according to reverse power flow intensity, and the parameters are increased when the power flow is enhanced to refine clusters. According to the method, the limitation that traditional partition depends on geographical proximity or simple parameters is broken through, accurate partition is achieved by quantifying electrical coupling and resource characteristics, the problem of poor connection in the cluster is solved, the on-site consumption capacity of new energy is effectively improved, the transmission pressure of a main network is reduced, and the method is suitable for large-scale popularization and application. And meanwhile, the fault recovery capability and the calculation efficiency of the power grid are enhanced, and the method is suitable for district power balance optimization in a high-permeability distributed power supply access scene.

The application relates to the field of artificial intelligence, and discloses a model distillation method, which comprises the following steps: at a first computing node of a computing node cluster, distilling a student model by using part of the student model and part of a teacher model, and performing gradient back propagation in the distillation process in the first computing node, without relying on other computing nodes to complete the distillation of a network layer responsible by the first computing node, so that greater computing resource utilization is achieved, and the distillation process is accelerated.

The invention discloses a method and a system for arranging and constructing an industrial chain based on semantics. The method comprises the following steps: collecting enterprise, supply, demand, market and policy data, cleaning and standardizing to generate a semantic map and embedding representation; calculating semantic signal vectors of the candidate links or node clusters; selecting chaotic mapping and setting disturbance intensity according to the signal vector, and generating an initial candidate solution set; coding the chromosomes with topology maintenance, and checking and repairing non-compliant individuals to obtain a feasible solution set; evolutionary optimization is carried out by using a chaos sequence, and an elite solution set is screened according to fitness; updating a semantic coverage file based on the elite solution set; if drifting or stagnation is detected, self-adaptive adjustment and restart are executed, and finally the industry chain topology and the alternative path are output and written back to the atlas. According to the method, semantic map modeling is combined with chaotic mapping and evolutionary optimization, so that intelligent generation, compliance repair and dynamic adaptive optimization of the industry chain topology are realized.

The invention discloses a source load multi-body dynamic clustering and regulation method based on a double-layer coupling local subgraph, and belongs to the technical field of source load regulation. The method comprises the following steps: firstly, constructing a local sub-graph model with coupled topology layer and physical layer, fusing node power, energy storage state and load characteristics, and realizing accurate description of high-order interaction relationship between source and load; secondly, providing a multi-scale feature extraction and incremental random walk clustering algorithm, and realizing dynamic updating and feature adaptive adjustment of node clustering in a local subgraph; and finally, constructing a layered autonomous regulation and control and cross-sub-graph cooperation mechanism, realizing local power balance in a sub-graph layer, realizing energy storage coordination and optimal energy distribution in a global layer, and realizing continuous updating and closed-loop optimization of a regulation and control scheme in combination with an incremental feedback actuator. According to the method, an integrated framework of coupling modeling-dynamic clustering-distributed regulation and control is formed, and the stability and toughness of multi-main-body cooperative operation of the power system are remarkably improved.

The invention discloses an automatic test method and test platform based on cloud computing, and belongs to the technical field of software testing, and the method comprises the steps: firstly constructing a dynamic elastic resource pool containing a virtualized test node cluster, a tool chain and a distributed data warehouse; after obtaining a performance test request, analyzing data to screen an optimal node combination and tool configuration, and generating a test case set covering a boundary scene and an abnormal path in combination with historical data and reinforcement learning; using an improved genetic algorithm and a load prediction model to disassemble the subtasks and intelligently distributing the subtasks; the progress is tracked in real time through a digital twinborn model, abnormity is recognized, and a multi-stage processing mechanism is activated; and after the test is finished, normalizing the processed data, and generating a panoramic report by means of the analysis model. According to the method, the intelligent level, the execution efficiency, the resource elasticity and the exception handling capacity of automatic testing are improved, and the high-performance testing requirement in a complex scene can be met.

The invention provides a dynamic predictive job scheduling method, which comprises the following steps of: acquiring a configuration file of a to-be-scheduled EDA (Electronic Design Automation) task by a scheduling central system, and extracting job characteristics of the to-be-scheduled EDA task; acquiring real-time monitoring data of the current running task from the computing node cluster; the prediction engine predicts the to-be-scheduled EDA task and the current running task according to the job characteristics and the real-time monitoring data to obtain first prediction data of the to-be-scheduled EDA task and second prediction data of the current running task, and the first prediction data and the second prediction data are fed back to the scheduling center system; the scheduling center system sends the first prediction data, the second prediction data and the real-time monitoring data to a decision engine; the decision engine determines a resource preemption strategy of the to-be-scheduled EDA task and feeds back the resource preemption strategy to the scheduling center system; the scheduling center system determines the execution sequence of the EDA tasks to be scheduled and sends the execution sequence to an execution engine; and the execution engine performs resource allocation on the to-be-scheduled EDA task.

A data processing method performed on a verification node and including obtaining task event information and a signature for the task event information from a scheduling node. The task event information includes a first computing node set participating in execution of a computing task and a random seed provided by a task submission end of the computing task. The method further includes performing verification on the signature, deriving a random number set based on the random seed in response to the verification of the signature succeeds, selecting, for the computing task, a second computing node set associated with the random number set, verifying validity of the first computing node set by using the second computing node set, to obtain a verification result, and adding a block including the verification result to a blockchain of a verification node cluster including the verification node.

The invention discloses a future computing-oriented super-node cluster liquid cooling efficient computing platform and a resource management method thereof. A CPU, a GPU and a reserved photon / quantum accelerator slot in a super node are virtualized into a computing resource pool through a unified heterogeneous resource abstraction layer, and a CXL large-capacity memory and an NVMe-oF storage are virtualized into a cross-node memory resource pool and a storage resource pool respectively; the physical state of the super node cluster, the temperature-flow distribution of the liquid cooling heat dissipation system and the operation parameters of the heat recovery system are mapped in real time in the digital twin system; the intelligent scheduler takes task completion time, cluster total power consumption and waste heat recovery efficiency as a joint optimization target, and decomposes the task based on a virtual model; and in the task execution process, the energy efficiency cooperative controller adjusts the operation frequency of the allocated resources and the rotating speed of the liquid cooling pump in real time according to the node temperature change predicted by the digital twin model, so that the task performance, the energy consumption and the heat dissipation capability are globally matched.

The invention relates to a virtual consistency multi-node message passing interface expansion method, device and equipment. The method comprises the following steps: deploying a distributed file system client at each computing node of a multi-node cluster, creating a unified virtual directory mounting point, transparently converting a file operation request under the unified virtual directory mounting point into a corresponding network protocol, and docking a global unified data storage pool, the unified virtual directory mounting point is used for providing consistent file views for all the computing nodes; pointing a file read-write path prefix of the message passing interface process to the unified virtual directory mounting point; and in response to the received user operation request, allocating computing nodes to the user operation request, ensuring that all the allocated computing nodes are mounted to the distributed file system, and starting a message passing interface process. By adopting the method, the consistency of the cross-node file view can be solved from the system level on the premise of not changing the communication logic.

This application discloses a method, apparatus, electronic device, and storage medium for splitting a storage cluster, relating to the field of computer technology, particularly to artificial intelligence fields such as cloud computing, distributed storage, and big data processing. The specific implementation scheme is as follows: The data path of the target resource in the original storage cluster is adjusted so that requests to access the target resource are sent to the target node in the original storage cluster; the target node is network isolated; the target node is restarted, and in response to the target node's target startup parameter being set to the target value, the target node is split from the original storage cluster as a new storage cluster; wherein, the target startup parameter is a parameter used to force a node to start as a new single-node cluster; the target node is removed from the original storage cluster, and the network isolation of the target node is lifted.

The invention provides an Internet of Things intelligent gateway suitable for an Internet of Things cloud platform system. The Internet of Things intelligent gateway suitable for the Internet of Things cloud platform system comprises a first network connection node cluster used for carrying out data transmission between an edge node network arranged in an Internet of Things cloud platform and a network layer of the Internet of Things cloud platform; the second network connection node cluster is used for carrying out data transmission between the network layer and the platform layer of the Internet of Things cloud platform; the first data transmission control module is used for controlling data transmission operation between the edge node network and a network layer of the Internet of Things cloud platform through dynamic screening of the first network connection node; and the second data transmission control module is used for controlling data transmission control operation between the network layer and the platform layer by dynamically screening the second network connection nodes.

A method for node cluster assignment in a graph includes initializing a plurality of wavefunctions, each one of the plurality of wavefunctions corresponding to nodes of the graph, constructing a plurality of quantum circuits, each corresponding to a graph Laplacian of the graph, evolving the plurality of wavefunctions at the plurality of quantum circuits, each one of the plurality of wavefunctions being evolved to a different time than other ones of the plurality of wavefunctions, measuring evolved states of the plurality of wavefunctions to generate a time-evolved wavefunction vector, and identifying a cluster assignment of a node of the graph based on the time-evolved wavefunction vector.

Disclosed are a computing power resource scheduling method, a network element, a storage medium and a program product, the method being applied to a first network, the method comprising: receiving a first computing power resource request, the first computing power resource request comprising an identifier of a computing power resource node cluster where a computing power resource requester is located, the computing power resource node cluster comprises computing power resource nodes of industry customers and operators; according to a preset computing power resource scheduling strategy, determining a target computing power resource node providing computing power resources for the computing power resource requester in the computing power resource node cluster; and sending the second computing power resource request to the target computing power resource node, so that the target computing power resource node allocates the corresponding computing power resource.

The invention discloses a substation multi-node cooperative control method, system and device and a medium, and the method comprises the steps: employing an inter-node coupling coefficient calculation formula, carrying out the calculation of harmonic admittance parameters between nodes in a substation, and obtaining an electrical coupling coefficient set; partitioning a plurality of nodes in the transformer substation according to the electrical coupling coefficient set to obtain a plurality of node clusters; according to the power constraint conditions of the plurality of node clusters and the operation constraint conditions of each node in the transformer substation, performing minimum value solving on the total power loss objective function of the transformer substation to obtain a power adjustment target value of each node cluster; and according to the unit adjustment capability cost of the power adjustment resource in each node cluster, adjusting the power adjustment resource in each node cluster until the power adjustment value of each node cluster is the power adjustment target value of each node cluster. According to the invention, on the basis of realizing cooperative control of multiple nodes in the transformer substation, efficient treatment of the multiple nodes in the transformer substation can be improved.

This invention discloses a task scheduling method and system based on an ITSM platform, belonging to the field of IT service management technology. The method includes the following steps: acquiring an available execution node cluster; collecting task execution records of nodes within a second scheduling set to generate a true performance baseline; performing periodic node rotation operations between the first and second scheduling sets based on historical execution data and the true performance baseline; calibrating the historical execution data of the first scheduling set; monitoring the real-time load distribution of the first scheduling set to trigger emergency node rotation operations; performing emergency node rotation operations and resetting historical data for nodes that have undergone rotation; and updating the initial node quantity ratio. This invention solves the problems of hotspot solidification, self-fulfilling oracle, and cluster resource utilization imbalance caused by historical affinity bias in existing technologies by dividing execution nodes into a first scheduling set and a second scheduling set.