21results about How to "Meet scheduling needs" patented technology

The invention discloses a multi-target dynamic switching optimization scheduling method and system based on a virtual power plant. The method comprises the following steps: acquiring real-time state data of a target virtual power plant; analyzing the real-time state data through a multi-model integration method based on a big tree mechanism, and predicting to obtain first prediction data of the target virtual power plant; performing adaptive correction on the first prediction data according to an improved Kalman filtering algorithm to obtain second prediction data; based on a plurality of scheduling requirements of the target virtual power plant, respectively constructing a scheduling target function corresponding to each scheduling requirement, and constructing a multi-target dynamic switching scheduling database according to the real-time state data and each scheduling target function; and screening in a multi-target dynamic switching scheduling database based on the real-time state data and the second prediction data, and scheduling the target virtual power plant by using a target scheduling scheme obtained after screening. According to the method provided by the invention, the scheduling operation efficiency of the virtual power plant is improved.

The invention discloses an I / O request scheduling method and device, electronic equipment and a storage medium, and belongs to the technical field of computers. The I / O request scheduling method comprises the following steps: acquiring state information of all hard disk pools, and determining an I / O type priority mapping table corresponding to each hard disk pool according to the state information; wherein the I / O type priority mapping table stores a corresponding relationship between the type and the priority of the I / O request; determining the priority of the to-be-processed I / O request corresponding to each hard disk pool according to the I / O type priority mapping table; and enqueueing the I / O requests to be processed to the corresponding priority queues according to the priorities. The resources of the storage system can be balanced, and the scheduling requirements of different types of I / O requests can be met.

The invention discloses a multi-stage coordinated regulation and control method and system for a virtual power plant, and the method comprises the steps: carrying out the quantitative description of an adjustable space of various distributed devices in the virtual power plant through a unified modeling method, and building an evaluation model of real-time output and response capabilities; aiming at the problem of online coordinated scheduling between the virtual power plant and the power distribution network, a centralized-distributed hierarchical scheduling structure is adopted, and the scheduling efficiency of the system and the response capability of equipment are effectively improved through unified modeling and hierarchical optimization. According to the method, the total operation cost of the virtual power plant can be effectively reduced, the problem of comprehensive information sharing needed by traditional centralized scheduling is avoided through privacy protection design, efficient interaction and information safety between the virtual power plant and the power distribution network are achieved, the requirement for real-time economic scheduling in a complex scene can be met, and high expandability and adaptability are achieved. And meanwhile, the method accords with the development direction of optimal dispatching of the current power distribution network, and plays a crucial role in promoting energy structure transformation and accelerating renewable energy consumption.

The invention discloses a task scheduling method and device, a storage medium and a processor. The method comprises the steps of generating task resource configuration information of a target task based on the similarity between the target task and a historical task and task resource configuration information of the historical task; determining a to-be-matched node set corresponding to the target task from the cluster; according to the resource information of the to-be-matched node and the task resource configuration information of the target task, obtaining a resource change rate before and after the to-be-matched node runs the target task; according to the resource change rate before and after the to-be-matched node runs the target task and the task type feature data of the target type in the to-be-matched node, obtaining a prediction score of the to-be-matched node running the target task; determining a target node from the to-be-matched nodes based on the predicted scores of the target tasks operated by the plurality of to-be-matched nodes; and scheduling the target task to the target node. According to the method, the scheduling requirements of various types of tasks and nodes are met, and the task running efficiency of the data sandbox is improved.

The invention relates to a resource scheduling method and system, a resource model training method, equipment and a medium, and the method comprises the steps: receiving a scheduling request of a target Pod, the scheduling request carrying a scheduling demand corresponding to the target Pod, and the scheduling demand being used for indicating a resource use demand and / or a service quality demand of the target Pod; determining a target node in the target cluster based on the cluster resource information of the plurality of clusters at the current moment; the target cluster is one of the plurality of clusters; the target node is a node which is used as a running environment and can meet the scheduling requirement in a preset time period after the current moment; and returning a scheduling result, wherein the scheduling result is used for indicating that the target Pod is scheduled to the target node for operation.

The invention discloses a scheduling strategy determination method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: constructing a scheduling strategy set according to acquired sample data; performing the following operations in a loop iteration manner until a preset iteration ending condition is met, and taking a scheduling strategy in the scheduling strategy set as a target scheduling strategy: performing vehicle scheduling on the transportation order in the sample data by using each scheduling strategy in the scheduling strategy set, and further calculating to obtain a strategy evaluation index of each scheduling strategy; and screening the scheduling strategies in the scheduling strategy set according to the strategy evaluation indexes, evolving the screened scheduling strategies by adopting a genetic algorithm, and updating the scheduling strategy set by using the evolved scheduling strategies. According to the embodiment, the target scheduling strategy is automatically evolved and generated based on the thought of genetic evolution, then vehicle scheduling is dynamically carried out in real time according to the target scheduling strategy, and the method has high environmental adaptability.

The invention discloses a power distribution network heavy overload scheduling method, device and equipment based on a topological structure. The method comprises the following steps: generating a power grid topological structure according to power grid related data and a topological updating mechanism; based on the power grid topological structure, analyzing the connection relation between the equipment and the line, and generating a resource effective response set which comprises a line response set and a main transformer response set; according to the resource effective response set, judging whether the virtual power plant resource can completely solve the equipment heavy overload problem or not, and if yes, calculating equivalent impedance between nodes based on a power grid topological structure; and heavy and overload resource scheduling is carried out according to the equivalent impedance and a preset resource calling rule. According to the resource scheduling method and device, the technical problem that the resource scheduling effect based on the heavy overload condition is poor due to the fact that the distribution structure characteristics of distributed resources are not fully considered in the prior art and the flexibility of resource scheduling is limited can be solved.

The application discloses a kind of energy storage system charge-discharge control method and device, the method includes: the characteristic data of influence spot price of next day released by electric power market is acquired;Based on the characteristic data, the peak period and the valley period of spot price of next day are predicted;According to the predicted peak period and the valley period, the operating state of the wind farm matched with energy storage system and the operating constraint of energy storage system, generate and execute the charge-discharge control instruction of the energy storage system, so that the energy storage system is charged in predicted valley period, and discharged in predicted peak period.Using the present scheme can improve the utilization efficiency of energy storage system.

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.

The invention discloses an active power real-time optimization control method and system for a wind power station, and the method comprises the steps: circularly carrying out the following operations in an active power real-time optimization control process: when wind power ultra-short-term prediction power is greater than dispatching power, judging whether an energy storage charging state is not fully charged, if yes, enabling an energy storage system to carry out the charging through the excess electric quantity of wind power, and if not, carrying out the energy storage; further judging whether the difference value between the wind power ultra-short-term predicted power and the dispatching power is greater than the energy storage rated power or not, if so, taking the energy storage rated power as the energy storage charging power for charging by the energy storage system, and if not, directly taking the difference value between the wind power ultra-short-term predicted power and the dispatching power as the energy storage charging power for charging; when the wind power ultra-short-term prediction power is smaller than the dispatching power, whether the energy storage charging state is larger than the minimum threshold value or not is judged, if yes, whether the difference value between the dispatching power and the wind power ultra-short-term prediction power is larger than the energy storage rated power or not is further judged, if yes, the energy storage system takes the energy storage rated power as the energy storage discharging power to conduct discharging, and if not, the energy storage system conducts discharging. And the energy storage system directly takes the difference value between the dispatching power and the wind power ultra-short-term prediction power as the energy storage discharge power for discharging.

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 utility model discloses a cab supporting device suitable for different commercial vehicle types, which relates to the technical field of heavy-duty car production and comprises a fixing component and an adjusting component. The fixing assembly comprises a supporting frame body and a plurality of vertical hollow fixing lower supports arranged in the length direction and the width direction of the supporting frame body, height limiting devices are arranged on the fixing lower supports, and a hoisting part is arranged on the supporting frame body; the adjusting assembly comprises an upper fixing support, one end of the upper fixing support is arranged in the lower fixing support, the upper fixing support can vertically move along the lower fixing support, and a connecting support used for being connected with a front suspension and a rear suspension of a cab is arranged at the top of the upper fixing support. The supporting mechanism is suitable for cabs of various models and sizes, the types and differences of the supporting mechanism can be greatly reduced, and the storage management and use convenience is improved.

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 present invention provides a data-driven freight train operation prediction method and system. The present invention trains a ridge regression model, a Lasso regression model, and an elastic regression model respectively, and then uses them for weighting, to construct a multi-regression integrated learning model that can compensate for the output deviation of a single model, and can directly fit various emergencies in the freight train transportation network by setting weight coefficients. Therefore, the predicted value of the fault impact condition of the corresponding turnout obtained by the present application through the model calculation can ensure the accuracy and timeliness of the model output when the cause and mechanism of the emergency are unclear. The present application can more accurately predict the impact of abnormal conditions on the transportation network and meet the scheduling needs in the transportation production process.

The present application relates to a method for scheduling energy storage equipment, which first evaluates and scores each energy storage system that has been put into operation based on its own parameter data and operation data, then obtains power demand information, and evaluates and scores the power demand information based on the power-time curve, demand unit, and historical supply data of the power demand information, and then matches it with multiple energy storage systems that are suitable in the score dimension based on the evaluation score of the power demand information, and then screens multiple target energy storage systems that are suitable in the time dimension from the matched multiple energy storage systems, and judges whether the scheduling requirements are met by comparing the calculated total available power of all target energy storage systems with the required power of the power demand information.

The present invention relates to a human-computer interaction method for distribution network dispatching based on artificial intelligence speech recognition, comprising the following steps: user-end equipment collects user voice requests, and performs parsing and processing through a parsing and processing module; the parsed voice information is assembled by protocol, and output to an intelligent speech engine through an access layer; the intelligent speech engine normalizes and differentiates the user's request, parses the true intention of the user's request, and transmits key information to a back-end service module that processes the user's intention; the back-end service module processes the user's intention and converts it into a corresponding power grid dispatching operation. This method is conducive to improving the efficiency of distribution network dispatching and the intelligence level of the distribution network dispatching system.

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.

The invention relates to the technical field of water conservancy projects, and discloses a triangular gate structure which is provided with at least one first gate body, and the number of driving parts and the number of second gate bodies are equal, so that the application range of the triangular gate structure can be widened, and different schemes can be conveniently selected according to different working conditions. Meanwhile, due to the fact that each first gate body is provided with the independent driving piece and the independent second gate body, the opening and closing time of the gate can be further shortened, the water outlet can be further rapidly opened or closed, and the emergency response capacity of the triangular gate to the situation that the gate needs to be rapidly closed or opened when the triangular gate encounters tide water or flood and the like can be further improved. Besides, according to the arrangement, in tasks such as flood prevention, tide blocking and flood drainage, accurate control over water flow can be achieved by controlling the opening and closing degrees of different gates, and therefore the dispatching requirement is better met.

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.