36 results about "Dynamic load balancing" patented technology

This invention proposes a method and apparatus for processing AI target instances in an artificial intelligence security training range. First, an AI training cluster is constructed based on the requirements of the training task. During attack and defense exercises, the operating status, system resource usage, and AI security risks of multiple AI target instances in the cluster are monitored in real time from multiple dimensions. When a security anomaly is detected in an AI target instance, an adaptive adjustment operation is triggered. This involves selecting a candidate instance from a pre-set AI target library that meets the requirements of the current training task to replace the abnormal instance, or switching the traffic of the abnormal instance to a healthy instance, and generating corresponding alarm information. This invention achieves dynamic load balancing and fault self-healing capabilities with AI perception, significantly improving the realism, robustness, and efficiency of the training environment.

The application discloses a multi-mode non-sensing resource migration method for an electric power IMS virtualized core network, and is used for solving the problems of service interruption, load imbalance and low resource utilization in multi-mode resource migration. The method comprises the following steps: establishing an NFV system model, defining an utility index based on SLA violation, and establishing optimization targets for three modes of virtual machine migration, container migration and application backup respectively; calculating resource utilization and migration deviation in a single mode and a multi-mode fusion environment, and constructing a multi-mode non-sensing migration utility function as a weighted average target; modeling the problem as a Markov decision process model, and converting the migration decision into a strategy; and solving the model by using a deep deterministic policy gradient algorithm to obtain an optimal migration strategy. The application realizes unbiased and non-sensing multi-mode dynamic load balancing, and improves the resource utilization and service reliability of the electric power IMS virtualized core network.

The application belongs to the technical field of video stream real-time analysis, and particularly relates to a high-concurrency video stream real-time analysis engine scheduling system and method, which comprises a scheduling management layer, a task execution layer and a resource management layer; the scheduling management layer comprises a multi-dimensional task evaluation module, an adaptive scheduling decision module, a multi-level priority queue management module, a scene-aware scheduling module and a dynamic load balancing module; the task execution layer comprises a video stream receiving module, a task decomposition module, a parallel execution engine and a result aggregation module; and the resource management layer comprises a resource monitoring module, a resource allocation module and a resource recycling module. Through the multi-dimensional task evaluation mechanism, the adaptive resource scheduling strategy, the multi-level priority queue, the scene-aware scheduling and the dynamic load balancing, the application realizes low resource consumption, high-concurrency processing and real-time response of video stream analysis scheduling, and significantly improves the video stream real-time analysis performance and resource utilization efficiency in the multi-camera, multi-task and multi-scene conditions.

The present application relates to electric vehicle charging scheduling technical field, especially to distributed charging pile group dynamic load balancing and power optimization allocation system and method, including: cloud coordinator obtains the space correlation data between piles containing geographical distance and traffic similarity and fuses with historical and weather data, inputs into the spatiotemporal graph neural network prediction model, outputs probability distribution prediction value and prediction confidence interval and issues, edge control node constructs multi-objective optimization model based on prediction uncertainty, generates dynamic allocation table and backup scheme under capacity constraint to minimize charging time and maximize renewable energy consumption as the target, issues allocation table to execute power allocation and obtains actual power feedback, when the actual power exceeds the confidence interval range, switch backup scheme to preferentially reduce flexibility load. The present application effectively improves the deficiency that single-point prediction lacks defense, realizes reliable global dynamic load balancing under the premise of guaranteeing the safe operation of power grid transformer.

This invention discloses an automatic task allocation system and method based on workload and capacity balance monitoring. The system includes: a dynamic capacity modeling module for constructing ground station capacity models and measurement, control, and operations (TT&C) task models; a real-time workload assessment module for dynamically assessing ground station workload; an adaptive allocation decision module for adaptively balancing and allocating optimal ground station resources; and a task execution monitoring module for employing conflict resolution strategies to eliminate abnormal ground station states. By introducing real-time workload assessment and capacity margin calculation mechanisms, this invention effectively solves the problem of uneven resource utilization caused by traditional static allocation modes. The adaptive allocation decision module can dynamically adjust task allocation strategies and automatically trigger task migration mechanisms, effectively avoiding resource waste caused by some stations being overloaded while others are idle, achieving dynamic load balancing, and significantly improving system resource utilization.

This application relates to a multi-path load balancing congestion control method, apparatus, system, and medium, comprising: a sending end acquiring path network status information fed back by a receiving end, determining the path congestion level accordingly, filtering out a preset number of overloaded paths, and updating a congestion control context that only records information about overloaded paths; further updating a queue context containing a path bitmap, the path bitmap being used to mark whether each path is available; and finally selecting an available path to send a message. This application only needs to maintain the status information of a small number of overloaded paths, significantly reducing storage overhead, and achieves efficient dynamic load balancing and congestion avoidance through the path bitmap.

This invention discloses a campus report generation optimization system based on hierarchical data caching and parallel computing, belonging to the field of big data processing technology. The system includes: a data acquisition and preprocessing module for acquiring raw report data and performing normalization and partitioning; a hierarchical data caching and storage module for constructing a three-level hierarchical caching architecture and evaluating cache heat; a parallel computing and dynamic load balancing module for optimizing resource allocation and adjusting load balancing in real time; and a dynamic skew processing and adjustment module for using hierarchical sampling and identifying skew keys, performing integration and verification to generate reports and storing them in the cache layer. This invention effectively improves the efficiency and stability of campus report generation, solving the problem of data skew in distributed computing causing some tasks to become bottlenecks and slowing down overall report generation.

This application discloses a method for parallel integration of geospatial data based on spatial grid-aware routing. The method includes: acquiring source information of geospatial data files and identifying their format and metadata; logically segmenting the data files into record blocks; calculating the bounding rectangle of the geometric objects of geospatial elements in each record block and determining the covering grid set according to spatial grid partitioning rules, then determining a unique master spatial grid identifier according to master attribution rules; constructing grid clusters based on the master spatial grid identifier, merging adjacent grids into scheduling partitions and routing them to corresponding parallel processing tasks; and having each task stream-read and process elements before batch writing them to the target storage. This invention overcomes the bottleneck of single-file parallel processing, achieves spatial locality-aware routing and dynamic load balancing, improves the spatial aggregation and query performance of target data, reduces the risk of memory overflow, and possesses high-throughput writing and precise breakpoint resumption capabilities.

This invention relates to the field of e-government information management technology, and discloses a closed-loop management system for handling requests on the 12345 platform. The system includes: extracting the acoustic urgency and semantic information density of the input voice stream and converting them into initial work order quantification values; calculating a spatiotemporal correlation score to generate merged work orders; subsequently outputting the probability distribution of the power and responsibility matching of candidate departments and iteratively optimizing to determine the optimal dispatch path; simultaneously, the system tracks the department processing rate to obtain an efficiency lag index, calculates the time-series change rate of the overall request density to generate an abnormal and sudden heat field, and finally aggregates the two to obtain a rectification priority value to generate a supervision letter. This invention effectively achieves dynamic load balancing and proactive anomaly warning for work orders across the entire domain.

The present application relates to the technical field of dynamic load balancing. The present application relates to a unified scheduling and monitoring system for computer room computing power resources based on dynamic load balancing. It includes a computing power node setting module, a running prediction analysis module, a demand resource updating module, a demand resource analysis module, a standby resource setting module and a resource scheduling module. The computing power node setting module is used to collect computer room computing power resources, which are divided into to-be-allocated computing power resources and used computing power resources. The present application builds a bounded computing power node state prediction system, taking the latest running state of the computing power node as the prediction center, setting the upper and lower limit boundaries of the prediction in combination with the total available computing power of the computing power resource pool, avoiding the invalid results caused by boundaryless prediction, and based on the change trend and fluctuation law of the historical running state sequence, the prediction result is more in line with the actual running characteristics of the computing power node, providing a reliable state basis for the analysis of subsequent demand computing power resources.

This invention relates to the field of power transmission dispatching technology, specifically a dynamic load balancing dispatching method for power transmission lines based on reinforcement learning. The method involves: acquiring absolute elevation data, operating sag state parameters, and the elevation data of the terrain below to construct the insulation margin of the transmission line; acquiring meteorological data, and deriving the upper limit of stable operation based on the insulation margin and settlement displacement data, combined with the conductor expansion coefficient and span parameters, and calculating the safe current carrying capacity limit of the transmission line based on the thermal balance principle; obtaining tension distortion data based on the relative elevation difference change of the support structure and the real-time temperature quantification of the conductor, and mapping it together with the real-time branch power flow to the edge weights of the topology model, outputting an updated topology model; calculating the settlement deterioration rate, and inputting it along with the updated topology model into a reinforcement learning network to generate an initial dispatching scheme set; converting the initial dispatching scheme set into expected branch currents, verifying them using the safe current carrying capacity limit, outputting a safe candidate scheme set, and performing balanced dispatching.

The application provides a dynamic load balancing method and device for a big data stream processing system, the method comprising: collecting message primary key distribution on a computing node and data volume corresponding to each message primary key; analyzing distribution of stream data according to the message primary key distribution and the data volume corresponding to each message primary key; and selecting a load distribution strategy based on the distribution of stream data to distribute unassigned load. The application can better balance the load of the computing node.

Embodiments of the present disclosure relate to the technical field of networks, and provide a packet forwarding method, a network interface card, a gateway device, a storage medium, and a program. The method comprises: in response to a first packet sent by a source end device, determining a packet forwarding mode of the first packet on the basis of feature information of the first packet; if the packet forwarding mode is a dynamic load balancing mode, allocating a target packet sequence number of the first packet in an order-preserving queue corresponding to a target service; generating first metadata, and inserting the first metadata into the first packet to obtain a second packet; sending, in a polling manner, the second packet to a first processor core running a driver; and in response to a first processed packet fed back by the first processor core, determining a sending order of the first processed packet on the basis of the first metadata contained in the first processed packet, so as to send the first processed packet to a corresponding destination end device in the sending order. The present solution can ensure the forwarding performance of the gateway device.

The application provides a forging process-oriented parallel simulation method and device, equipment and medium, including: carrying out spatial discretization processing on the geometric model of the forging process simulation, generating a finite element grid model, and setting boundary conditions; based on the finite element grid information of each workpiece, the weight of each finite element node is determined; based on the weight of each finite element node, the number of regions and the topological structure, the finite element grid model is subjected to regional decomposition processing, and a plurality of calculation regions are determined; under multi-process, the stiffness matrix calculation and assembly of each calculation region are carried out at the same time, the forging process simulation process is divided into a plurality of load steps for sequential loading, and when the load step iteration converges, a dynamic load balancing operation is performed; after all the calculation regions complete their respective iteration calculation, the simulation result is output. Dynamic load balancing in the whole forging simulation iteration process is realized, which can adapt to the dynamic simulation characteristics of the forging process and accurate and efficient task scheduling.

The present application relates to the technical field of medical information parallel computing, in particular to a multi-thread parallel computing method and system for brucella spondylitis diagnosis parameters, the method comprising: a diagnosis task dependency graph construction step, a directed acyclic graph is constructed based on the calculation dependency relationship of the diagnosis parameters and the task priority is determined; an adaptive thread scheduling step, a dynamic load balancing is realized by using a work stealing strategy and a lock-free concurrent queue; a vectorized parallel computing step, a single instruction multiple data stream instruction is used to perform batch calculation on the diagnosis parameters; a feedback-driven optimization step, a closed-loop optimization is formed by dynamically adjusting the scheduling strategy according to the performance index, the four core modules are deeply coupled to realize synergistic effect.

This invention discloses a deep learning-based dynamic load balancing method and system for cloud storage resources, relating to the field of cloud storage technology. This deep learning-based dynamic load balancing method for cloud storage resources collects access sequences and node load data from the cloud storage cluster, inputs them into a prediction model, and obtains hotspot prediction results, confidence scores, and adversarial scores. It constructs a multi-tenant association graph with tenants as nodes and temporal synchronization as edge weights. Group behavior features are extracted through a graph neural network, fused with tenant historical behavior baselines and temporal compliance features, and then output causal inference strength via a structural causal model. This invention establishes a dual verification mechanism combining an adversarial discrimination subnetwork and a structural causal model, combined with the group behavior features extracted from the multi-tenant association graph, forming a closed loop from identification to blocking to feedback updates. This provides a multi-source verification and continuously evolving scheduling architecture for cloud storage load balancing.

The application provides a heterogeneous multi-core task scheduling and dynamic load balancing method based on an electric Hong system, comprising designing a heterogeneous core task scheduling framework, dynamically distributing computing tasks to different ARM cores, RISC-V cores or accelerator cores according to the different types of tasks in the distribution network automation device; providing a dynamic load monitoring and adjustment mechanism, triggering a task scheduling strategy to achieve load balancing by monitoring the load situation, core state, power consumption and task priority information of each core of the distribution network automation device in real time; wherein the task scheduling strategy allocates different scheduling priorities to different tasks according to the requirements of various tasks in the distribution network automation device; providing a task migration mechanism, which automatically migrates the computing task from the high-load core to the light-load core for continuous execution when detecting that the system load is unbalanced or the load of a certain core is too heavy. The application can improve the computing efficiency, real-time response capability and system stability of the distribution network automation system.

This invention relates to the field of mobile computing devices and energy management technology, and particularly to a dual-battery cell management method and system for industrial mobile devices. The method includes the following steps: splitting the battery pack of the industrial mobile device into two independent cells, connecting them in parallel to an intelligent power management unit, and configuring each cell with a fast-charging circuit, so that each fast-charging circuit forms a dual-path independent closed-loop control path with an external power supply, the corresponding cell, and the intelligent power management unit. This invention, through parallel connection of dual cells, intelligent power management, and dual-path fast-charging control, combined with dynamic load balancing, fault isolation, and load-adaptive power supply strategies, achieves safe and efficient application of batteries in industrial mobile devices, thereby improving charging efficiency and battery life stability, and enhancing battery life and safety.

This application discloses a method and related equipment for collecting power production data. The method includes: adaptively processing target heterogeneous protocol data to determine target parsing rules; parsing the target heterogeneous protocol data according to the target parsing rules to obtain standardized data; classifying the standardized data, determining differentiated collection strategies based on the classification results, and calculating the node load of data collection nodes at each level to achieve hierarchical distributed data collection and dynamic load balancing; calculating the link quality score between data collection nodes at each level, and dynamically scheduling the classified standardized data transmission based on the classification results, differentiated collection strategies, node load, and link quality score. This application can automatically complete the generation and updating of protocol rules, improve the system's compatibility with different types of power equipment, ensure the orderly flow of data between multiple levels, and can be widely applied in the field of power production data management technology.

This invention discloses a dynamic load balancing method and system based on Redis and memory collaboration, belonging to the field of intelligent resource scheduling technology. It includes obtaining complete profile data and generating a hot group list and link matrix through unified collection of multi-level indicators and synchronous monitoring of node links; obtaining a link migration execution report by detecting the link health of migration commands and node parameters; verifying and replaying the migration execution report using contradictory verification correction to generate an adoption report and an anomaly list; and dynamically adjusting the order of different levels and nodes and updating the migration execution report based on the adoption report and the anomaly list using an adaptive parameter tuning method. This invention achieves hot / cold zone perception and congestion prediction of migration commands by combining a cross-layer remapping candidate list with a link matrix for bandwidth hot zone projection and cold zone priority sorting, effectively improving the accuracy of resource scheduling under Redis and memory collaboration.

This invention discloses a dynamic task scheduling system for space control simulation model computation, comprising a scheduling center, several scheduler nodes, and several executor nodes. The scheduler nodes and executor nodes are deployed on different server clusters. The scheduling center dynamically adjusts task scheduling based on dynamic load balancing technology according to the task scheduling requirements of the space control simulation service, allocating tasks to registered scheduler nodes. The scheduler nodes are used for task production and caching, and trigger the executor center using Remote Procedure Call (RPC) technology. They are also used for dynamic elastic scaling of executor nodes for tasks that cannot be fulfilled. The executor center logs into the scheduling center via heartbeat registration to achieve remote communication between the two. It is also used to decompose tasks and trigger the corresponding executor nodes. The executor nodes execute the corresponding space control simulation model computation tasks.

Facilitating concurrent energy aware admission control, dynamic load balancing, and traffic steering in advanced communication networks is provided. A method includes facilitating energy efficiency aware load balancing of already served user equipment to distribute the already served user equipment among a group of cells of a communication network. The method also includes facilitating controlling of admission of incoming user equipment to the communication network and facilitating traffic steering of the incoming user equipment among the group of cells. Further, the method includes facilitating conflict mitigation among respective policies associated with facilitating the energy efficiency aware load balancing, facilitating of the controlling of the admission, and facilitating of the traffic steering. Facilitating of the energy efficiency aware load balancing, facilitating of the controlling of the admission, facilitating of the traffic steering, and facilitating of the conflict mitigation are performed concurrently and are based on a utility function.

This invention discloses a thermal stress balancing design method and module layout for a modular power supply system, comprising the following steps: Constructing a thermoelectric synergistic coupling network: The high-current bus between power modules is designed as a heat pipe-conductor composite structure, with an inner conductive core and an outer heat pipe structure. Joule heating generated by the current drives the circulation of the heat pipe working fluid, simultaneously achieving power transmission and heat dissipation; Dynamic load balancing control: Power distribution between modules is adjusted through thermal stress feedback. When the temperature of a module exceeds a threshold, a predictive control algorithm transfers part of the load to a low-temperature module. Combined with the thermal coupling effect of the bus heat pipe, a dual-balance closed loop of electrical load and thermal load is formed. This invention, with its integrated thermoelectric synergistic design, achieves deep coupling of power transmission and heat dissipation through the heat pipe-conductor composite bus, breaking through the limitations of traditional independent designs for electrical transmission and thermal management, significantly reducing system thermal resistance and improving heat dissipation efficiency.

This invention discloses a dynamic load balancing method suitable for weak network environments, belonging to the field of network load balancing technology. The method includes the following steps: Step 1: Perform multi-dimensional link status collection in the network, monitor the key performance indicators of the physical and transport layers of each candidate path in real time, and calculate a comprehensive metric for each path; Step 2: Dynamically filter out a set of reliable paths for load balancing; Step 3: Dynamically calculate and adjust the traffic allocation weights of each path, including dynamic weight calculation based on inverse performance ratio, adaptive adjustment of sensitivity index, and weight fine-tuning based on real-time congestion; Step 4: Perform stability control and oscillation suppression; Step 5: Identify different upper-layer applications, and implement traffic classification and differentiated policy execution for different upper-layer application requirements.

The application provides a distributed workflow task scheduling method and system, and relates to the technical field of computers. The scheduling task nodes and dependency relationships are defined and stored in a drag-and-drop manner through a visual canvas. The Worker nodes with a normal ZooKeeper center hop state are screened, the node weights are calculated based on dynamic load balancing, and the tasks are distributed to the Worker nodes with the highest weights for execution. When a Worker node fails, the ZooKeeper node deletion event is listened to, the IP and port of the faulty Worker are identified, the task states of the un-terminated nodes of the faulty node are marked as needing fault tolerance and are re-allocated. When a Master node fails, the surviving Master takes over the workflow instance of the faulty Master based on a slot allocation mechanism. The task reporting address in the Worker is updated to the current Master, and the uncompleted tasks are re-dispatched. The overall usability, efficiency and stability of the workflow scheduling are improved, and the method is suitable for diversified business requirements.

This application relates to the field of warehouse management technology and discloses a task allocation method, device, electronic device and storage medium. The task allocation method selects at least two candidate inventories that meet the requirements of a first task, determines the distance cost and heat cost of each work area corresponding to each candidate inventory, calculates the total cost based on the heat cost and distance cost, and allocates the first task to the work area with the lowest total cost, thereby achieving dynamic load balancing of tasks and improving the resource utilization rate of the warehouse system.

Provided herein is facilitating energy-aware multi-cell admission control with dynamic load balancing in advanced communication networks. One method includes facilitating, by a system comprising a processor, energy-efficiency aware load balancing for served user equipment. The method also includes facilitating, by the system, control of admission of other user equipment to a communication network. The communication network can be deployed as a disaggregated architecture comprising a central unit, a distributed unit, and a near-real-time radio access network intelligent controller. The cell group can be configured to operate according to a new wireless network communication protocol.

The application discloses a multi-dimensional fusion micro-service dynamic load balancing method, comprising the following steps: encapsulating real-time running state data of each service node into multi-dimensional state data objects according to a target format, and storing the multi-dimensional state data objects of all service nodes as an original state data set; filtering invalid data in the original state data set to obtain a valid state data set; calculating node weights of the service nodes in the valid state data set, distinguishing valid nodes and invalid nodes based on a preset weight threshold, automatically sequencing to generate an available node list and outputting the available node list to a request distribution module; the request distribution module directs a business calling request to a target node according to the available node list, and then updates real-time running state data of the target node; and executing a micro-service cluster process according to a running state of the micro-service cluster service calling. The application realizes dynamic weight adaptive load fluctuation and real-time sensing node state, and cluster resources can be balancedly utilized.

The application provides a hybrid translation system and method based on an embedded system and edge AI, comprising an embedded proxy server module, an edge AI translation server module, a text classification decision module, a dynamic content monitoring module and a translation closed loop module. The application greatly reduces the deployment difficulty, improves the convenience and flexibility, has a wide range of applications, and has high practicability and innovation by taking the embedded system as a carrier. Through the embedded proxy+edge AI hybrid translation architecture, plug and play, privacy security and offline availability are realized. Through length+term density dual text classification, industrial professional text translation is accurately adapted. Through dynamic load balancing scheduling, low delay, high accuracy and high stability are considered.