12results about How to "Improve scheduling ability" patented technology

This application discloses a multi-tenant task scheduling method and related apparatus, relating to the field of cloud computing technology. It receives tasks to be scheduled and their metadata, determines dynamic priority information, and sends the task to a target priority queue if the tenant corresponding to the task has an effective quota. When a tenant has a concurrent consumption token in the target priority queue, it acquires the token, executes the task, and then releases the token. This application sets different dynamic priorities for tasks to be scheduled, ensuring that tasks are distributed to resource-isolated independent queues, guaranteeing priority processing of each task, and improving resource utilization. Before task publication, sliding window rate limiting is implemented, and tenant-level concurrent consumption tokens are distributed during task consumption, isolating tasks between tenants. In complex multi-tenant, high-concurrency production scenarios, this enables intelligent task adjustment, significantly improving resource utilization and scheduling efficiency.

The present application relates to the technical field of intelligent manufacturing and intelligent scheduling, and particularly relates to a flexible processing unit dynamic regulation method, system and model based on multiple agents. The method comprises the following steps: S1: establishing an event-driven Markov decision process model; S2: constructing corresponding task selection agents and workpiece selection agents to form a hierarchical multi-agent deep reinforcement learning architecture; S3: designing the system state features, action space and reward function of the task selection agents and the workpiece selection agents; S4: jointly training the task selection agents and the workpiece selection agents based on a multi-agent proximal policy optimization training algorithm to obtain optimal policy parameters of the task selection agents and the workpiece selection agents; and S5: the task selection agents and the workpiece selection agents output task actions and workpiece actions respectively based on the optimal policy parameters obtained through training. The present application can improve the adaptability of the system to dynamic disturbances and the real-time decision-making capability.

The application discloses a coal furnace mixed coal parameter determination method, device and equipment, wherein the method comprises the following steps: obtaining historical power plant operation data and historical coal type data; based on the historical power plant operation data and the historical coal type data, a coal power coupling model for representing the mapping relationship between the coal quality parameters and the coal furnace performance indexes is trained; current power grid operation data and current coal furnace operation data are obtained, and based on the current power grid operation data and the current coal furnace operation data, a constraint condition is constructed; a target function is constructed based on the coal furnace performance indexes, a mixed coal parameter determination model is constructed based on the target function, the coal power coupling model and the constraint condition, the mixed coal parameter determination model is solved, and the target mixed coal parameter corresponding to the coal furnace on the power plant side is determined; and the target mixed coal parameter is used for representing the mixed coal proportion of different coal types. Through the above method, the mixed coal parameter can be quickly and accurately determined, and the power grid peak regulation demand can be adapted.

The invention relates to the technical field of unmanned collaboration and resource virtualization, and provides a task collaboration-oriented unmanned system virtual resource representation and organization method and device and a storage medium. The method comprises the following steps: firstly, determining physical resource virtualization granularity (platform level, equipment level and function unit level) according to unmanned collaboration styles (task level, resource level and element level); secondly, establishing a resource tree model comprising a resource base class, an attribute resource, a data table resource and a resource sub-tree based on granularity; resource storage is carried out by utilizing an organization structure constructed by a hash table, a list and a set; and finally, forming a resource pool through standardized packaging, providing management interfaces (resource registration, cancellation and state maintenance) and scheduling interfaces (demand response, resource allocation and release), and realizing resource addressing by adopting URI coding. According to the method, the problems of low task execution efficiency, insufficient resource utilization rate and complex management caused by poor coordination task and resource adaptability, non-uniform heterogeneous resource description and non-standard packaging are solved.

The utility model discloses a kind of feeding device and spraying system, it is related to the technical field of automation equipment.The feeding device includes base, material transmission assembly and feeding back component, material transmission assembly is located in base and has material transmission passage, the width of material transmission passage is adapted to product to make product be sequentially conveyed from the feeding end of material transmission passage to the discharge end of material transmission passage;Feeding back component is located in base and includes first conveyor belt and second conveyor belt arranged side by side, the feeding end of first conveyor belt and the feeding end of second conveyor belt are connected, and the discharge end of second conveyor belt is connected respectively with the feeding end of material transmission passage to make the part of product of the discharge end of second conveyor belt be conveyed to the feeding end of material transmission passage, part of product is conveyed to the discharge end of first conveyor belt and by the feeding end of first conveyor belt be conveyed to the feeding end of second conveyor belt.The technical scheme provided by the utility model improves the scheduling capability of feeding device.

The application relates to an energy data processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: obtaining original energy data based on space division multiplexing transmission energy data; sequentially performing denoising processing and data quality evaluation processing on the original energy data to obtain denoised energy data and a quality evaluation result of the denoised energy data; performing denoising processing on the denoised energy data again according to the quality evaluation result to obtain target energy data; and inputting the target energy data into a trained energy scheduling processing model to obtain target scheduling information of the target energy data. The method can improve the energy data processing efficiency, utilize the target scheduling information to improve the scheduling effect of energy equipment, and realize intelligent management of the energy equipment.

This invention belongs to the field of sensor resource allocation and discloses a sensor resource scheduling method and system based on reinforcement learning of combined action space. The method first obtains the system state of the sensor network system to be scheduled, then inputs the system state into a pre-trained sensor resource allocation model to obtain combined actions, and finally outputs the combined actions as the optimal scheduling scheme for flight targets. By designing a combined action space, this method overcomes the limitation of classical single-agent reinforcement learning algorithms that execute one discrete action at a time in discrete action space, enabling the execution of a multi-dimensional action combination at a given moment, and facilitating the scheduling of multiple sensors for tracking multiple flight targets. The sensor network system is trained using reinforcement learning to obtain a sensor scheduling scheme, enabling adaptive resource scheduling, dynamic adjustment of allocation strategies, and optimization of system performance.

The application discloses a power distribution network scheduling method and system based on multi-stage safety reinforcement learning, and belongs to the technical field of power system operation control, which comprises the following steps: constructing a cost reward function with the minimum power distribution network cost as the target, taking the candidate output adjustment amount of the cost scheduling subject as the cost action space, and constructing a cost agent; obtaining the actual output adjustment amount of the cost scheduling subject by using the cost agent according to the output boundary constraint of the cost scheduling subject; constructing an adaptive reward function with the maximum safety scheduling performance of the safety scheduling subject as the target, constructing a power grid safety penalty function with the maximum power distribution network safety as the target, taking the candidate output adjustment amount of the safety scheduling subject as the safety action space, and constructing a safety agent; obtaining the actual output adjustment amount of the safety scheduling subject by using the safety agent; and scheduling the power distribution network based on the actual output adjustment amount of each subject. The technical problem that the prior art is difficult to balance the safety of the power distribution network and the scheduling performance of new energy is solved.

This invention discloses a power system dispatch optimization method and system based on deep reinforcement learning, belonging to the field of smart grid optimization and dispatch technology. It includes: receiving and synchronizing real-time operational data, meteorological data, equipment health indicators, and renewable energy output data; constructing a power grid diagram and generating a node time-series matrix, encoding it as a multi-temporal embedding vector; generating short-term output prediction values ​​and uncertainty indicators based on meteorological and renewable energy output data, converting them into compensation factors; calculating risk scores based on equipment health indicators and meteorological data and mapping them to dynamic weights; inputting the multi-temporal embedding vectors, compensation factors, and dynamic weights into a deep reinforcement learning model to generate a dispatch strategy and performing feasibility verification; if the verification passes, the strategy is executed and the results are returned for online model updates. This invention effectively improves the security and robustness of power system dispatch by integrating multi-source data and a risk perception mechanism.

The application provides a tracking method, device and equipment of a target object and a storage medium. The method comprises the following steps: in the case that a panoramic camera device detects a target object, acquiring target parameters corresponding to each of at least two dynamic point camera devices, wherein the target parameters comprise a service life usage rate of the dynamic point camera device and / or a rotation distance of the dynamic point camera device rotating to a target position, and the target position is a position at which the dynamic point camera device can monitor the target object; determining a target dynamic point camera device based on the target parameters; and controlling the target dynamic point camera device to track the target object. The application can optimize the scheduling of the dynamic point camera device and improve the scheduling performance of the monitoring system.

This invention discloses a hierarchical dynamic scheduling method based on the policy optimization DDQN algorithm for solving the dynamic scheduling problem of a reentrant hybrid pipelined workshop with batch processing machines. First, the objective function and constraints of the scheduling problem are determined. Then, by introducing a self-attention mechanism, a hierarchical structure based on DDQN is proposed, constructing two agents: a batching agent and a scheduling agent, to solve the batching and scheduling subproblems respectively. Furthermore, to address the multi-stage batch processing and reentrant scheduling characteristics of the problem, a Markov decision process considering the characteristics of these two agents is designed, including state, action, and reward. Further, an action selection strategy based on masking combined with an ε-greedy strategy and a soft-start target network update strategy are proposed to improve efficiency and generalization ability. This invention demonstrates significant effectiveness in solving the dynamic scheduling problem of a reentrant hybrid pipelined workshop with batch processing machines.