11 results about "Dynamic capacity" patented technology

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.

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.

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 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 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.