25 results about "Dynamic capacity" patented technology

The invention discloses a shared resource access method and device, equipment, a medium and a product, and relates to the technical field of efficient concurrency control and resource management in a computer system.The shared resource access method and device adopt a mixed structure combining a lock-free annular queue and a double-end queue, queue data are analyzed through a load prediction unit, and the load trend is predicted; the queue capacity and the migration threshold value are dynamically adjusted; the lock-free annular queue is preferentially used in daily life to improve the access efficiency, and when the queue is close to the capacity upper limit or triggers the migration threshold, migration and capacity expansion of waiting coroutines are automatically realized through the double-end queue, so that the processing performance under high concurrency is ensured, and the dynamic capacity requirement is met; finally, the technical problems that the performance and the capacity of a single queue are difficult to consider in a high-concurrency scene and the single queue cannot adapt to a dynamic load are solved, and efficient scheduling of shared resources in a coroutine environment is realized.

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.

The embodiment of the invention discloses a dynamic capacity configuration method and device based on physical information digital twinning, and the method comprises the steps: employing a digital twinning model of physical information, and simulating the thermodynamic process of a to-be-processed data center; predicting energy consumption and carbon emission of the to-be-processed data center based on the thermodynamic process of the to-be-processed data center; based on the prediction result of the energy consumption and the carbon emission of the data center to be processed, optimizing the operation carbon footprint of the data center to be processed, and generating a capacity budget; and configuring the resources of the data center to be processed based on the capacity budget.

The invention relates to the technical field of factory operation and maintenance, in particular to an intelligent factory operation and maintenance scheduling method and system based on multi-source data. The method comprises the following steps: acquiring optimized data streams of all operation equipment based on a factory sensor network; performing equipment state operation analysis on the optimized data stream to generate an equipment real-time operation curve; performing fault prediction and shutdown maintenance planning according to the real-time operation curve of the equipment, and outputting a shutdown time point; calculating a shutdown period productivity gap index based on the shutdown time point to generate a productivity gap index; and carrying out capacity compensation analysis and production line scheduling planning according to the capacity gap index, and generating a multi-device scheduling instruction. According to the method, predictive maintenance and dynamic productivity optimization from equipment level to system level of the intelligent factory are carried out, and the operation and maintenance efficiency and the overall production stability of the factory are improved.

The invention relates to the technical field of energy storage system control, and discloses a dynamic capacity flexible regulation and control method of an energy storage system. According to the method, historical operation data is preprocessed to form standardized operation index time sequence data, and a plurality of capacity regulation and control characteristic profiles reflecting different operation states are constructed according to the standardized operation index time sequence data. In real-time application, a target profile with the highest matching degree and an adjacent profile associated with the target profile are extracted by identifying the matching degree of a current operation data stream and each characteristic profile. The core lies in that a fixed strategy of a target section is not directly adopted, but a capacity regulation and control strategy is dynamically generated according to data distribution characteristics between the target section and an adjacent section. And finally executing the strategy to realize flexible adjustment of the output capacity of the energy storage system. Through multi-section matching and data distribution fusion, the limitation of a traditional rigid regulation and control method is overcome, and the adaptive capacity of the system to complex operation conditions is remarkably enhanced.

The present application relates to the field of SSD and discloses an SSD device with dynamic capacity cache acceleration, comprising: a memory comprising multiple storage subspaces; a cache comprising multiple cache subspaces; a cache allocator configured to calculate a cache execution configuration based on a cache configuration request for the storage subspace; a cache executor which selects a traversal range and a traversal order based on a cache execution configuration, a structured data for traverse history and a virtual index of a storage unit, and calculates minimum and maximum cost thresholds, the traversal range includes a set of cache units in a corresponding cache subspace and is divided into a plurality of pending windows; traverses the pending windows in the traversal order, calculates a cost function for each cache unit in the current pending window based on a structured state table, and selects a cache unit with a cost function that is less than the minimum cost threshold. This application configures and changes the cache subspace without resetting the device.

The application discloses a shared resource access method and device, equipment, medium and product, relates to the technical field of efficient concurrent control and resource management in a computer system, and adopts a hybrid structure combining a lock-free ring queue and a double-ended queue. A load prediction unit is used to analyze queue data and predict a load trend, and then the queue capacity and a migration threshold are dynamically adjusted. The lock-free ring queue is preferentially used in daily use to improve access efficiency. When the queue approaches the upper limit of the capacity or triggers the migration threshold, the migration and expansion of the waiting coroutine are automatically realized through the double-ended queue, which guarantees the processing performance under high concurrency and meets the dynamic capacity demand, finally solves the technical problems that the performance and capacity of a single queue are difficult to balance under a high concurrency scenario and the single queue cannot adapt to dynamic loads, and realizes efficient scheduling of shared resources in a coroutine environment.

This invention provides a method and system for optimizing the configuration and scheduling of energy storage capacity in virtual power plants, relating to the field of virtual power plant energy storage technology. The method includes extrapolating time-period capacity demand sequences based on historical data, calculating and correcting the dynamic adjustable capacity boundaries of energy storage units. By identifying the temporal complementarity between energy storage units, they are divided into collaborative configuration groups and their capacity is aggregated. Based on the matching degree between the aggregation results and grid commands, power allocation and reserve are performed hierarchically for units within each group. When the capacity within a group is insufficient, complementary time periods across groups are identified, and the reserve capacity of other groups is used for compensatory scheduling. This invention achieves refined configuration and collaborative scheduling of energy storage capacity, improving the responsiveness of virtual power plants to grid demands and enhancing their operational economy.

When supporting ancillary services for a target power system, existing solutions consider a fixed capacity of the battery energy storage system. In contrast, disclosed embodiments leverage a forecast of ancillary events, in combination with an optimization model, to produce an optimal determination for ancillary services. In particular, required capacity may be calculated from the forecast of ancillary events, and a capacity constraint may be generated from the required capacity and incorporated into the optimization model. Then, the optimization model may be solved to produce an optimal determination for ancillary services.

The invention discloses a dynamic capacity expansion scheduling method, device and system for a security industry large model, and belongs to the technical field of security industry and large model capacity expansion, and the method specifically comprises the steps that a large model management platform sends a state calculation request carrying a copy resource demand to a node; the node calculates a time sensitive state score based on the real-time time sensitive parameter and replies the time sensitive state score; and the large model management platform calculates and ranks the final state scores of the nodes in combination with the scores replied by the nodes and the non-time sensitive parameters, and selects the optimal node for scheduling. The method effectively solves the problems of insufficient real-time performance of node parameters, low scheduling decision accuracy and high scheduling delay of a central platform in the prior art, and is particularly suitable for dynamic resource scheduling of large model applications such as real-time research report generation, intelligent customer service and abnormal transaction monitoring in the security industry.

An automatic provisioning and configuration system for threat mitigation may be provided. Hardware and software resources may be automatically configured to designate a return path for forwarding clean data packets to a target network. A return path from a scrubbing center to the target network may be selected and configured, for example, based on the geographic location of the scrubbing center and information regarding available capacity of the return path to the target network, among other information. The system may also perform a set of dynamic checks to determine whether one or more scrubbing centers have capacity (and / or are likely to continue to have capacity) to provide an encapsulation tunnel between the scrubbing center and the target network for clean return traffic.

The invention discloses an electronic machine account intelligent early warning system based on big data analysis. A distributed data medium table (based on a Hive number bin of a Hadoop 3. X framework and a Spark SQL component, adopting an ORC column type storage format, having daily processing capacity greater than or equal to 5 million pieces, realizing dynamic capacity expansion through YARN resource scheduling, supporting lateral expansion to ten-million-level throughput, and adopting date and business dimension dual hash for a data partitioning strategy); the Flink streaming computation engine is used for ensuring data consistency by adopting Exactly-Once semantics, realizing Checkpoint barrier synchronization based on a Kafka message queue, controlling delay within 200ms, and supporting dynamic back pressure regulation, and the peak value processing capability reaches 100,000 pieces per second; provided is a visual early warning billboard. The system response delay is controlled within 500ms (the actually measured industrial scene average delay is 378ms) through the multi-source data parallel access and stream processing channel, the efficiency is improved by 6 times compared with the traditional scheme, and the real-time performance is improved.

In supporting ancillary services for a target power system, existing solutions consider a fixed capacity of a battery energy storage system. In contrast, the disclosed embodiments utilize a prediction of an ancillary event, in combination with an optimization model, to produce an optimal determination of the ancillary service. In particular, a required capacity can be calculated from the prediction of the ancillary event, and a capacity constraint can be generated from the required capacity and incorporated into the optimization model. The optimization model can then be solved to produce the optimal determination of the ancillary service.

The embodiment of the invention provides a dynamic capacity expansion and contraction optimization method and optimization system of a cluster and a storage medium, and belongs to the technical field of resource scheduling. The optimization method comprises the following steps: acquiring a multi-dimensional monitoring index related to a load parameter and a service index; based on a deep learning prediction model, processing the multi-dimensional monitoring index to output a prediction result of the application load of the cluster; dynamically generating a current optimal capacity expansion and contraction strategy based on a multi-objective optimization algorithm according to the prediction result and a preset capacity expansion and contraction rule; and based on the capacity expansion and contraction strategy, executing capacity expansion and contraction optimization operation. According to the technical scheme, modeling and analysis can be carried out on the multi-dimensional monitoring data through the deep learning algorithm, accurate prediction of the application load is achieved, and then intelligent scheduling and optimization of resources are achieved. According to the invention, business demand changes can be quickly responded, load peaks and low ebbs can be handled in advance, and stable application performance is ensured to improve user experience.

The application belongs to the field of computer system architecture, and discloses a data storage optimization method and system based on a real-time operating system, which stores task data in stages through a data buffer module adopting a multi-stage buffer architecture; then a task scheduling module adopting an event-driven mechanism dynamically schedules storage tasks; and a dynamic capacity adjustment module based on a machine learning model predicts future data flow patterns, thereby proactively and cooperatively adjusting the capacities of the buffer zones; finally, efficient data persistence is realized through a ping-pong buffer writing mode. Through the innovative architecture of multi-module cooperation, the application effectively solves the storage performance bottleneck problem that may exist in some real-time systems, and significantly improves the throughput capacity, real-time responsiveness and resource utilization efficiency of the system.

The invention relates to an in-situ failing memory device associated with tag capacity. A system may include a memory device having a plurality of dynamic capacity devices and a processing device operatively coupled with the memory device. The processing device performs the following: recording an error metric associated with a first tag, where the first tag is associated with a first memory section of the dynamic capacity device, the first memory section being allocated to a first host system to store data; determining whether the error metric satisfies a threshold criterion for an unrecoverable error; in response to determining that the error metric satisfies a threshold criterion, excluding the first memory section from available memory sections of the dynamic capacity device from being used for future memory allocation; in response to receiving a request for memory allocation in the memory device, determining whether an available memory segment capacity size of the dynamic capacity device is not less than a capacity size specified in the request; and in response to determining that the capacity of the available memory section of the dynamic capacity device is not less than the capacity size specified in the request, identifying a second memory section of the dynamic capacity device and associating a second tag with the second memory section.

The application discloses a new energy large generator caused periodic overload dynamic capacity increasing method and system of a transformer in a transformer area, relates to the technical field of distribution transformer overload management, and comprises the following steps: collecting multi-source data of a transformer area distribution transformer, new energy, load and environment; analyzing periodic load characteristics, establishing a distribution transformer overload and overvoltage characteristic model; real-time monitoring of transformer oil temperature, winding temperature and load current, dynamic capacity increasing of the transformer, evaluation of real-time overload capacity and residual capacity of the transformer; determining the transformer load rate according to the actual maximum load and rated capacity; optimizing the charging and discharging parameters of the energy storage system according to the real-time overload capacity, residual capacity and transformer load rate, generating a source-grid-load-storage collaborative control strategy; executing energy storage charging and discharging to smooth the load curve and realize dynamic capacity increasing of the distribution transformer; real-time monitoring of the operating state and closed-loop correction of the control strategy. Periodic overload can be precisely managed, overvoltage can be inhibited, and the asset utilization rate of the distribution network and the power supply reliability can be improved.

The application relates to the storage technical field and discloses a dynamic capacity expansion or reduction method, device, equipment and medium. The method comprises the following steps: collecting load information of a server processing data, comparing the load information with a preset load degree, obtaining a first comparison result, counting the first comparison result, obtaining a count value, comparing the count value with a preset number value, obtaining a second comparison result, sending a capacity expansion or reduction instruction to a preset cloud server according to the second comparison result and a preset configuration file, obtaining server information corresponding to capacity expansion or reduction, receiving the server information corresponding to capacity expansion or reduction, and configuring corresponding servers to balance the load, so that the load of the host server can be maintained in a balanced state, the operation and maintenance cost is reduced, and the operation and maintenance efficiency is improved.

The invention relates to the technical field of supply chain management and intelligent logistics, and discloses a cross-border e-commerce intelligent replenishment processing method based on big data, and the method comprises the steps: 1, collecting platform storage rule data, ship position information, port congestion information, detour probability information and a preposed market popularity signal in real time; step 2, establishing a dynamic capacity prediction model based on the acquired platform storage rule data and the preposed market popularity signal, and calculating and generating a virtual available quota of the target warehouse in a future preset time window; and step 3, based on the acquired ship position information. According to the technical scheme, a dynamic capacity prediction model is established, and a future virtual available quota is calculated in combination with a platform storage rule and preposed market popularity, so that the technical effect of estimating and locking the future available storage capacity is achieved. The defect that cargoes cannot be warehoused due to insufficient capacity when the cargoes arrive at the port in a replenishment plan is overcome.

This invention discloses a method for screening N-2 faults in overhead transmission lines that takes into account dynamic capacity expansion. Step 1: Obtain an evaluation index for the probability of occurrence of each N-2 fault scenario in the N-2 fault set of the overhead transmission line, taking into account dynamic capacity expansion factors. Step 2: After screening and sorting the N-2 fault set of the overhead transmission line based on the evaluation index value of each N-2 fault scenario, a set of N-2 fault scenarios to be processed is obtained. The method for screening N-2 faults in overhead transmission lines that takes into account dynamic capacity expansion provided by this invention can correctly screen out N-2 faults in overhead transmission lines with a high probability of occurrence, and at the same time accurately reflect the impact of dynamic capacity expansion technology on the screening of N-2 faults in overhead transmission lines.

This application relates to the field of SSD and discloses an SSD device accelerated by dynamic capacity cache. The device includes: a memory comprising multiple storage subspaces; a buffer comprising multiple cache subspaces, each corresponding one-to-one with the storage subspaces; a cache executor configured to allocate a corresponding cache unit for a storage unit based on a necessary capacity, a read peak capacity, and a write peak capacity of the cache subspace corresponding to the storage subspace to which the storage unit belongs when reading out or writing into the storage unit, and send allocation information of the cache unit to a cache allocator; and the cache allocator configured to set a total capacity of the corresponding cache subspace for at least one storage subspace based on an external cache configuration request, as well as initial values of the necessary capacity, the read peak capacity, and the write peak capacity, and adjust the necessary capacity, the read peak capacity, and the write peak capacity of the cache subspace based on the total capacity of the cache subspace and the allocation information provided by the cache executor. This application configures and changes the cache subspace without resetting the device.

Implementations herein relate to a memory system with a dynamic capacity. In some implementations, the memory system may include a set of memory arrays that corresponds to a first address space and that includes a plurality of disjoint subsets of memory arrays. Additionally, the first address space may be divided into a plurality of capacity blocks that are each associated with a respective one of the plurality of disjoint subsets. The memory system may additionally include error detection circuitry configured to detect an error in a memory array within a first disjoint subset and a controller configured to remap a portion of the plurality of capacity blocks to a second address space, where the second address space does not include a first capacity block based on the first capacity block being associated with the first disjoint subset including the error.

The application relates to the field of SSDs, and discloses an SSD device with dynamic capacity cache acceleration, which comprises a memory including a plurality of storage subspaces; a cache including a plurality of cache subspaces; a cache allocator configured to calculate a cache execution configuration according to a cache configuration request of the storage subspaces; and a cache executor configured to select a traversal range and a traversal order and calculate minimum and maximum cost thresholds according to the cache execution configuration, a history data structure and a virtual index of a storage unit, the traversal range comprising a group of cache units in a corresponding cache subspace and being divided into a plurality of candidate windows; and the candidate windows are traversed in the traversal order, a cost function of each cache unit in a current candidate window is calculated according to a structured state table, and a cache unit with a cost function less than the minimum cost threshold is selected. According to the application, the cache subspaces can be configured and changed without resetting the device.