44 results about "Dynamic capacity" patented technology

A method includes providing a control algorithm that may include a constraint constraining production of the equipment below maximum values for a plurality of time steps, and dynamically adjusting the constraint by updating the maximum values as a function of predicted values of a dynamic variable for the plurality of time step. The dynamic variable affects an actual maximum production of the equipment. The method includes determining control decisions for the plurality of time steps by executing the control algorithm and controlling the equipment in accordance with the control decisions.

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 embodiment of the invention provides a dynamic capacity expansion and contraction method and device, equipment and a storage medium. The method comprises the steps that time series data in a first time period are obtained, the time series data are used for representing the situation that the number of online vehicles changes along with time in the first time period, and the first time period is a time period before the current moment; the time sequence data are input into a prediction model, the number of online vehicles in a second time period is predicted, and the second time period is a time period after the current moment; according to the number of online vehicles in the second time period, determining a capacity expansion and contraction strategy of resources in the second time period; and adjusting the resource quantity provided for the online vehicle in the second time period according to the capacity expansion and contraction strategy. According to the method, resource allocation in the next time period is arranged in advance by predicting the number of online vehicles in the next time period, the hysteresis of capacity expansion operation can be avoided, and the stability of the system is better maintained.

The invention provides a distributed equipment collaborative scheduling system for toy manufacturing and an optimization method. A data acquisition module is used for acquiring equipment operation data and production target data; the dynamic productivity evaluation and analysis module is used for evaluating dynamic productivity and analyzing redundancy; the collaborative scheduling strategy generation module is used for constructing a multi-objective optimization model and dynamically generating a collaborative scheduling strategy; and the execution and feedback module is used for executing the scheduling instruction, feeding back the equipment state in real time and triggering periodic re-optimization to match the dynamic production requirement. According to the method, the real-time production efficiency of each device is obtained and combined with the maximum production bearing capacity, the dynamic evaluation capacity of the unit time production capacity of each device is improved, and under the condition that the production redundancy is reduced, the scheduling strategy of collaborative dynamic planning distribution toy production is dynamically adjusted and optimized for each device, so that the production efficiency of the device is improved. And equipment production redundancy utilization is globally optimized, equipment idling or overload is avoided, and the overall efficiency is improved.

The invention provides a resource index prediction method and device based on a Transform framework and a medium, and the method comprises the steps: carrying out the pre-training of a PatchTST model through employing the historical data of resource indexes, such as a CPU, a memory, and the like, improving the future values of the prediction indexes of a conventional PatchTST model into the future distribution of the prediction indexes, obtaining a prediction mean value and a prediction variance, and carrying out the prediction of the prediction mean value and the prediction variance. And further converting into prediction value output, prediction interval upper bound output and prediction interval lower bound output. Compared with a traditional time sequence prediction method based on a Transform architecture, the method has the advantages that the prediction accuracy is high, the training time is short, the upper and lower bounds of a prediction interval can be output, and the predicted upper and lower bounds can be used for representing a confidence interval of a predicted value. According to the invention, richer and more accurate guidance can be provided for intelligent operation and maintenance scenes such as dynamic capacity expansion and contraction, capacity management, capacity planning and the like.

The invention relates to the technical field of server deployment and capacity expansion, in particular to a service dynamic capacity expansion method and device, equipment and a storage medium. Comprising the following steps: acquiring page view data of a plurality of target pages and call volume data of services in a preset time period, and calculating an average page view of each back-end service in the preset time period based on the call volume data; according to the page view data and the call volume data, calculating a current conversion rate from each target page to the corresponding back-end service, and calculating a current service load value of the corresponding back-end service after routing conversion; judging whether the current service load value is greater than a preset threshold, and if so, calling a historical data set corresponding to a preset time period in a historical database; and based on the historical data set, the comprehensive average access amount and the current conversion rate, judging whether a capacity expansion triggering condition is met, and if yes, triggering dynamic capacity expansion of the corresponding back-end service. The resource utilization rate and the better cost effectiveness can be improved, and the user experience is improved.

The invention discloses a power grid real-time data elastic capacity expansion system based on mass streaming data, relates to the technical field of power grid real-time data storage, and aims to solve the problem that a storage environment is not safe due to the fact that more intelligent elastic capacity expansion storage is not adopted when existing power grid data is stored. Through a real-time capacity monitoring function, the system can know the capacity condition of the storage node in time, so that an intelligent storage scheduling decision is made, when the capacity of the storage node is insufficient, a capacity expansion process can be automatically triggered, the need of manual intervention is reduced, and through dynamic capacity expansion and load balancing, storage resources can be more effectively utilized, and the capacity expansion efficiency is improved. By monitoring and analyzing the power grid data in real time, abnormal conditions in the power grid can be found in time, corresponding measures are taken for prevention and processing, and the storage space and transmission bandwidth requirements of the data can be effectively reduced by adopting a lossy compression mode.

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.

The present application provides a power system economic dispatch method and related device considering line dynamic capacity increase. The method establishes an objective function with the minimum operating cost of the units in the power system as the goal; determines the constraint conditions of the objective function according to the parameters of the power system; solves the economic dispatch model to obtain the square term of the current of each line in the power system; performs thermal stability verification on each line according to the square term of the current of each line to determine the over-limit lines; performs iterative solution on the power economic dispatch model to obtain the economic dispatch plan of the power system. The present application improves the calculation accuracy of the line temperature rise by determining the power adjustment rate constraint and considering the dynamic process of unit ramp-up in detail, and generates constraint iteration for the high-dimensional constraint set caused by considering the dynamic process of unit ramp-up by performing thermal stability verification on the solution result of the economic dispatch model, reducing the calculation complexity of the power system economic dispatch model and improving the solution speed and efficiency of the model.

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 belongs to the technical field of data storage, and particularly relates to an operating system partition dynamic capacity expansion method, which comprises the following steps of: S1, searching available storage equipment units in an operating system; s2, selecting a plurality of storage device units to be subjected to storage capacity combination from the retrieved available storage device units, and endowing each selected storage device unit to be combined with combination identification information; s3, identifying the selected storage device units to be combined one by one according to the combination identification information of the selected storage device units, and combining the identified storage device units to obtain combined mass storage; and S4, the stacked and combined large capacity is stored in an operating system partition. According to the invention, secondary utilization of idle fragmented small-capacity storage is realized; operating system partitions are dynamically expanded, and the space capacity of the operating system partitions is increased.

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.

A control and protection system (9) for controlling and protecting components (7a, 7b, 7c, 7x) of an HVDC system (1), the control and protection system (9) being configured for receiving input data (15, 16), the input data comprising sensor data (15) for a component and forecast data (16) for operation of the HVDC system, calculating from the input data (15, 16), by using a physical model (8a, 8b, 8c, 8x ) for each of the components (7a, 7b, 7c, 7x), an individual dynamic capacity (20a, 20b, 20c) for each of the components (7a, 7b, 7c, 7x), and calculating an overall dynamic capacity (11) of the HVDC system (1) by combining the individual dynamic capacities (20a, 20b, 20c).

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.

The invention belongs to the field of computer system architecture, and discloses a data storage optimization method and system based on a real-time operating system, and the method comprises the steps: carrying out the hierarchical storage of task data through a data buffer module adopting a multi-stage buffer architecture; dynamically scheduling the storage tasks by adopting a task scheduling module of an event-driven mechanism; a future data flow mode is predicted based on a dynamic capacity adjusting module of a machine learning model, so that the capacity of each buffer area is actively and cooperatively adjusted; and finally, realizing high-efficiency persistence of the data in a ping-pong buffer writing mode. Through a multi-module collaborative innovative architecture, the problem of storage performance bottleneck possibly existing in some real-time systems is effectively solved, and the throughput capacity, the real-time responsiveness and the resource utilization efficiency of the system are remarkably improved.

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 invention discloses a real-time optimization scheduling method considering dynamic capacity increasing of a line in emergency weather. The method comprises the following steps: acquiring data information of a target power system and data information of an overhead line provided with dynamic capacity increasing equipment in real time; calculating to obtain the current-carrying capacity increase rate of the dynamic capacity-increasing circuit; modeling is carried out on the relation between the short-time overcurrent rate and the temperature rise duration of the line; constructing a source network storage real-time optimization scheduling model considering line dynamic capacity increase and corresponding constraint conditions; and solving the constructed model to realize real-time optimization scheduling considering dynamic capacity increase of the line in emergency weather. The invention also discloses a system for realizing the real-time optimization scheduling method considering the dynamic capacity increase of the line in the emergency weather. According to the method, real-time optimization scheduling of the target power system in consideration of dynamic capacity increase of the line in response to sudden weather is realized, the potential of short-time power transmission of the power grid is utilized, the reliability is high, and the accuracy is good.

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 invention discloses a process node processing method and device, a storage medium and electronic equipment. The method comprises the steps that a task pulling request sent by a service client in a workflow engine is obtained, the task pulling request is sent to a group of clusters in the workflow engine, a first cluster is determined according to a feedback result of the group of clusters, and the first cluster is formed by deploying a target process instance in the group of clusters; and sending a first task identifier to the service client according to the cluster in which the target process node is in the to-be-activated state, so as to indicate the service client to execute the target task according to the first task identifier, and setting the target process node to be in an activated state. The technical problems that the processing efficiency of the flow node in the cluster is difficult to guarantee and the processing mode of the flow node is not flexible enough due to the fact that the cluster cannot realize dynamic capacity expansion and contraction are solved.

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 present application provides a method and related apparatus for economic dispatch of an electric power system taking into account dynamic capacity expansion of lines. The method comprises: establishing an objective function with the goal of minimizing the operating cost of the units in the electric power system; determining the constraints of the objective function based on the parameters of the electric power system, the constraints including unit constraints, network variable constraints, linearized AC power flow constraints, and linearized line thermal stability constraints; solving the economic dispatch model of the electric power system to obtain an economic dispatch plan for the electric power system, the economic dispatch model of the electric power system including the objective function and the constraints. The present application utilizes linearized line thermal stability constraints to track the temperature changes of the line, which can ensure that the line improves its transmission capacity on the basis of satisfying the thermal stability constraints, thereby optimizing the economic dispatch strategy. By linearizing the AC power flow constraints and the line thermal stability constraints, the economic dispatch model of the electric power system is constructed as a mixed integer linear programming model, which can improve the solution speed and efficiency of the model.

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.