12 results about "Group scheduling" patented technology

Embodiments of the present disclosure disclose a virtual cluster-based crowd scheduling system and device. A specific implementation of the system includes: a trace synchronization module that automatically identifies a load object created by a user in a virtual cluster, synchronizes task annotation metadata corresponding to the load object to a corresponding execution unit in a host cluster, and writes address annotation metadata corresponding to the load object in the execution unit; a scheduling module that reads task annotation metadata of each execution unit, generates a scheduling score value corresponding to each execution unit in each execution unit according to the read task annotation metadata set, and batches each execution unit to a corresponding resource adaptation node, the scheduling module being deployed in the host cluster; and a billing module configured to generate a task billing sheet. The implementation improves the versatility and resource utilization efficiency of the system.

This invention discloses a parallel pump group scheduling method and system based on dynamic programming, belonging to the technical field of intelligent pump group scheduling. The method involves data acquisition and preprocessing, discretizing and equivalently treating the variable frequency pumps as n equivalent fixed frequency pumps. Then, single-pump performance curves and pipeline network performance curves are fitted. Based on the single-pump performance curves, the high-efficiency operating range of each pump is determined. Then, using water supply flow demand, water supply head demand, and the high-efficiency operating range of each pump as hard constraints for pump group operation, candidate pump group combinations are selected. The start-up and shutdown costs and usage time of each pump are incorporated as soft constraints into the total cost, constructing a minimum objective function for the total cost. Finally, a dynamic programming algorithm is used to obtain the global optimal solution, generating the optimal pump group scheduling sequence within the scheduling cycle. This invention balances low energy consumption and equipment health, and is applicable to mixed pump group scenarios including fixed-frequency and variable frequency pumps, significantly reducing energy consumption in traditional scheduling and mitigating equipment wear.

This application provides a container group scheduling method, apparatus, device, storage medium, and product. The method includes: obtaining a list of container groups corresponding to a target task, wherein the list of container groups includes multiple container groups; constructing a first K-layer graph corresponding to each of the N idle extended processors in the cluster based on the cluster's network topology and traffic information, wherein the first 1-layer graph corresponding to the j-th idle extended processor is used to represent the network between extended processors located on the same server, including the j-th idle extended processor and other extended processors connected to it, and the first 1-layer graph corresponding to the j-th idle extended processor is used to represent the network between extended processors located on different servers, including the j-th idle extended processor and other extended processors connected to it, managed by an i-1-layer switch; and determining and scheduling multiple container groups based on the first K-layer graph corresponding to each of the N idle extended processors and based on the target first graph.

The application provides a lease cabinet cluster scheduling and transport capacity allocation method based on time sequence prediction, which comprises the following steps: obtaining the lease cabinet's lending and returning log, inventory state and regional passenger flow data, and constructing a cabinet body operation sequence of a unified time axis; secondly, using a time sequence prediction model to generate an operation prediction sequence in a future window, and calculating a future window imbalance result in combination with time attenuation and passenger flow extrusion factors; thirdly, generating a cabinet group scheduling relationship result in combination with a service cost and a current capacity boundary according to the imbalance result; finally, generating a scheduling instruction set meeting physical constraints in combination with actual capacities on both supply and demand sides according to a relationship allocation coefficient. Through the deduction of continuous time windows and the modeling of cabinet group relationships, the application effectively solves the problems of supply and demand mismatch and local node imbalance in peak periods, and improves the overall balancing capacity and service efficiency of the lease cabinet cluster.

The application discloses a flow shop group scheduling fast evaluation method, relates to the technical field of workshop scheduling, and constructs a fast evaluation method between groups and in groups based on an insertion neighborhood; the method comprises the following steps: step 1, calculating the forward completion time of workpieces in a group on a machine; step 2, calculating the backward completion time of the workpieces in the group on the machine; step 3, obtaining the maximum completion time; step 4, updating the forward completion time; and step 5, updating the backward completion time. The application solves the problem of quickly evaluating the maximum completion time as a target value in the flow shop group scheduling problem, reduces the calculation time, can effectively avoid repeated evaluation, saves the calculation cost, and improves the scheduling efficiency.

The application relates to the field of hydrology, in particular to a runoff prediction and reservoir group scheduling series connection framework supporting complex river network topology. A surface rainfall calculation module is used for calculating the measured surface rainfall and the predicted surface rainfall of the river network section to obtain the entity surface rainfall data; a reservoir scheduling strategy module is used for calling the reservoir scheduling strategy; a reservoir scheduling strategy configuration module is used for configuring the reservoir scheduling strategy and related parameters, and supports the hourly hot-pluggable configuration of the reservoir scheduling strategy of each reservoir in the river network; and a river network topology series connection calculation module is used for calculating the water level and flow process of the entity and all upstream sections, and the river network topology series connection calculation module comprises an entity calculation unit and a series connection algorithm unit. The application solves the problem of hard coding or fixed configuration of the river network topology relationship in the system, is applicable to different river network topologies, automatically adapts to and is applied to any complex basin network, and realizes the decoupling of the model algorithm and the topology relationship.

The application discloses a reservoir group ice prevention scheduling method coupled with a river channel ice water dynamics model, first, taking historical Longgu inflow as input, taking satisfaction of preset constraint conditions and a reservoir group scheduling objective function as target, training an NSGA-II model to construct an ice prevention optimization scheduling model, and obtaining a scheduling scheme set of Longliu reservoirs; second, based on the relationship between historical key section flow and historical key section water level change parameters, an ice and snow water dynamics model is constructed, key section water level change parameters are output, and a water level fluctuation index is calculated; then, the output of the ice prevention optimization scheduling model is connected to the input of the ice and snow water dynamics model through the Muskingum method, and the coupled model is obtained by training with historical Longgu inflow as input; finally, Longliu scheduling schemes are determined according to the scheduling scheme set. Through coupling of the scheduling model and the ice and snow water dynamics model, explicit simulation of the influence of reservoir scheduling on downstream river channel water level is realized.

The application provides a reservoir group scheduling method and device based on an agent, equipment and a storage medium. The method comprises: obtaining scheduling task instruction information input by a user, performing task analysis and processing on the scheduling task instruction information by using a large language model to obtain a task instruction recognition result; the large language model is trained based on a knowledge graph about the field of reservoir scheduling in a knowledge management unit of the agent; in response to the task instruction recognition result meeting scheduling calculation boundary conditions, generating a reservoir group scheduling scheme for a target reservoir group according to the task instruction recognition result and a perception data set of the target reservoir group in the knowledge management unit, and scheduling the target reservoir group based on the reservoir group scheduling scheme. The scheduling calculation boundary conditions include the spatio-temporal distribution of the runoff and the spatio-temporal distribution of the weather in the basin in a future preset time period. The reservoir group scheduling scheme includes the scheduling task and the discharge flow process of each reservoir in the target reservoir group. In this way, adaptive scheduling can be achieved.

This invention belongs to the field of reservoir group scheduling technology, specifically providing a method and system for optimizing the scheduling of cascade reservoirs based on the synergy of physical embedded deep learning and evolutionary computation. This invention hard-codes physical rules into a temporal convolutional network, constructing a physical embedded temporal convolutional network surrogate model. Through survival analysis, the discrete water discharge objective is transformed into a differentiable loss. PeTCN is used to perform gradient inverse optimization, generating a high-quality initial solution for outflow that satisfies the constraints. This initial solution is injected into the NSGA-III algorithm population for hot start, overcoming the feasibility obstacles caused by random initialization, accelerating the Pareto front search, and ultimately obtaining a non-dominated solution set that balances power generation benefits, water discharge control, and delayed water discharge. This invention structurally ensures the satisfaction of physical constraints, significantly improves the efficiency of feasible solution generation and optimization convergence speed, and the obtained solution set is uniformly distributed and highly applicable to engineering, effectively supporting the efficient and safe operation and scheduling of cascade hydropower stations.

The application discloses a short-medium-long-term coupled digital twin reservoir group collaborative scheduling method and system, and particularly relates to the technical field of reservoir group scheduling. The method comprises the following steps: acquiring multi-source water and rainfall conditions and working condition data related to the reservoir group, performing unified time scale alignment and quality control, and generating a twin initial state package; generating a candidate rainfall sequence based on the twin initial state package for rainfall input disturbance, driving a digital twin reservoir group model to form a time scenario set, and generating a time scenario credibility weight set from rainband drop zone drift, rising edge alignment residual error, propagation delay residual error and key data freshness; under the constraint of executability, a candidate collaborative peak-shaving discharge scheme set is generated and multiple scenarios are deduced, a weighted peak-shaving super-alarm risk index is obtained by weighting according to the credibility weight, a robust peak-shaving scheme is selected, a collaborative scheduling instruction set is generated, and the instruction set is issued to an on-site control system; and the system is used for implementing the method.

This invention relates to the field of chip design, and in particular to a compilation method, electronic device, and storage medium free from SIMT deadlock risk. The method involves the compiler inserting initialization code for a thread group scheduling queue at the beginning of the target source code, thus obtaining the thread group scheduling queue. This queue is used to rearrange the execution order of basic blocks during the execution of the target source code. Queue maintenance code is inserted at the end of each basic block. The maintenance steps of the thread group scheduling queue include: the processor executing the queue maintenance code inserted at the end of the i-th basic block; the queue maintenance code detecting the next target instruction to be executed and pushing the target instruction and the binary execution mask of the current thread group onto the tail of the thread group scheduling queue; and the dequeueing of the head instruction of the thread group scheduling queue, followed by processor execution of the dequeueed head instruction, thus resolving the deadlock problem.

This invention provides a multi-modal transportation scheduling method and system for logistics freight vehicles based on the Internet of Things (IoT), belonging to the field of transportation scheduling technology. The method includes: obtaining multiple multi-source real-time datasets through IoT terminals; calculating the dynamic health of each dump truck; performing state analysis of multiple dump trucks; performing group fairness enhancement training on a pre-trained scheduling model; inputting multiple health labels, a resource state mapping table, and the enhanced scheduling model into a multi-objective optimizer to generate a truck-cargo pairing scheme and an unloading point allocation scheme, and then executing transportation scheduling. This invention solves the technical problems of existing technologies where logistics freight vehicle transportation scheduling relies on static rules and lacks real-time perception and fairness constraints, leading to low resource utilization and uneven load distribution among vehicle groups. It achieves the technical effect of improving resource utilization and realizing fair group scheduling of logistics freight vehicles through real-time perception via IoT and the introduction of fairness enhancement.