7results about How to "Meet scheduling needs" patented technology

The application discloses a power grid carbon emission flow dynamic tracking method based on a physical guided graph neural network, and belongs to the technical field of power system operation analysis and low-carbon dispatching, and comprises the following steps: collecting power grid data in real time and dynamically constructing a power grid topology graph structure; inputting the graph structure into a physical guided graph attention network model trained through a loss function containing physical constraints, wherein the model fuses the edge features of line power and impedance when calculating attention coefficients, so as to infer the carbon potential of all nodes in the network; based on the inferred carbon potential, using a breadth-first search algorithm to start from a load node, tracking and proportionally allocating in the reverse direction along the active power flow path, and determining the carbon emission contribution of the generator set. The application ensures the physical consistency and precision of carbon flow tracking under the condition of lacking real labels, and significantly improves the efficiency of large-scale power grid carbon flow analysis and the adaptability to topology changes.

This application provides a Bluetooth service scheduling method, apparatus, device, and storage medium. The method includes: obtaining the target service scenario currently in which a target Bluetooth device is in a multi-link state; planning a service scheduling period for the target service scenario according to the time allocation rules corresponding to the target service scenario and dividing the service scheduling period into time slices to obtain a target service scheduling strategy corresponding to the target service scenario, wherein the target service scheduling strategy includes the correspondence between the links in the target service scenario and the time slices obtained by dividing the service scheduling period into time slices; and performing service scheduling on the target Bluetooth device according to the target service scheduling strategy. This technical solution can avoid problems such as lag or disconnection of Bluetooth devices in a multi-link state.

The application discloses a cross-domain perception self-evolution energy storage battery management system and a protection method thereof, and belongs to the technical field of energy storage battery management. The system comprises a perception unit, a data fusion unit, an algorithm core unit and a collaborative control unit. The perception unit collects cell microstate, battery body operation and external scene parameters; the data fusion unit pre-processes and outputs a feature vector; the algorithm core unit comprises a double-cycle AI algorithm module of online incremental learning and offline iterative updating, and a digital twin engine module for constructing a special twin for each cell; and the collaborative control unit allocates and schedules tasks according to the cell health state, and reversely requests load adjustment in an abnormal situation. The application realizes accurate cell state perception, model self-evolution updating and health constraint collaborative control, and improves the safety, economy and cycle life of the energy storage system.

The embodiment of the application provides a resource optimization method, any node, a storage medium and a computer program product, the method comprises the following steps: obtaining first information corresponding to a k8s node cluster; wherein the node cluster comprises N first nodes, and the first information comprises resource utilization rate information corresponding to each first node; determining one or more second nodes based on the resource utilization rate information corresponding to each first node and a preset migration condition; for each second node, determining a first score corresponding to one or more first container group pods that are running; wherein the first score comprises a comprehensive score of migrating the first pods to one or more third nodes, and the first node comprises the third node; determining a second pod to be migrated based on the first score, and migrating the second pod to a corresponding fourth node; wherein the first pod comprises the second pod, and the third node comprises the fourth node, thereby improving the overall resource utilization rate.

The invention belongs to the field of power system dispatching control, and relates to a hydroelectric energy storage system optimal dispatching method considering energy storage and water consumption rate, which comprises the following steps: constructing a hydroelectric generating set water consumption characteristic model and a battery energy storage life loss cost model; establishing an optimization model by taking minimization of the comprehensive operation cost of the system as a target and taking power balance and hydroelectric-energy storage coupling operation as core constraints; solving the optimization model by adopting a double-layer frame combining day-ahead optimization scheduling and intra-day rolling correction, and generating a combined scheduling instruction of hydropower and energy storage; issuing the generated joint dispatching instruction to a hydropower station automatic power generation control system and a battery energy storage energy management system for execution, and collecting actual operation data to perform feedback correction on the model; on the premise that the power grid dispatching requirement is met, the utilization efficiency of water energy resources is improved, attenuation of the service life of an energy storage battery is delayed, and economical operation of a water-electricity-energy storage combined system is achieved.

The application discloses a power plant peak shaving and climbing operation guidance system and an operation optimization method based on digital twinning, which comprises a high-fidelity full-digital twinning model module, a hybrid modeling method combining a mechanism model and a data-driven model is used to construct a digital twinning body covering the whole process of a power plant from coal entering the plant to power generation and grid connection, and the digital twinning body comprises sub-models of a boiler, a steam turbine, a generator and main auxiliary systems; a global rolling optimization engine module is used to receive a power plant operation target instruction, adopt a rolling time domain optimization strategy, and solve an optimal set value sequence of a full-plant adjustable actuator in a future preset prediction time domain; optimal set value sequences obtained by the global rolling optimization engine module are set as guidance values based on the optimal set value sequences, and are issued to a DCS for execution by the DCS or reference by operation personnel; and a data communication module is used to realize data interaction between different modules.

This invention discloses a dual-cycle collaborative scheduling method, system, equipment, and medium for sea-rail intermodal AGVs, comprising: constructing a multi-resource collaborative scheduling model with minimizing completion time as the objective, setting train arrival time as a dynamic trigger parameter; judging the execution timing of direct-fetch tasks based on this, splitting them into sub-tasks from shore to yard and then to train if the arrival time is earlier, otherwise maintaining direct-fetch; constructing dual-cycle constraints in time periods and matching import and export tasks to generate operation sequences; finally, through multi-layer coding mapping, using an improved whale algorithm to iteratively solve and output the optimal scheduling scheme. This invention significantly improves equipment utilization, operation continuity, and overall system scheduling efficiency under the sea-rail intermodal mixed operation mode.