54results about How to "Improve scheduling ability" patented technology

One or more embodiments of the invention provide a load-aware scheduling method of an inference system and the inference system. The method is applied to the inference system comprising a global scheduler and an inference engine. The inference engine comprises a Prefill engine and a Decode engine; the Prefill engine and the Decode engine respectively comprise at least one calculation instance; computing resources of the computing instances comprise GPUs carried on the computing nodes where the computing instances are located; the method comprises the following steps: a global scheduler obtains a target reasoning request to be executed, determines a first calculation instance in a Prefill engine of which the GPU load meets a first preset condition based on maintained GPU load information, and sends the target reasoning request to the first calculation instance to execute reasoning calculation in a Prefill stage; and a first local scheduler corresponding to the first calculation instance in the Prefill engine determines a second calculation instance in the Decode engine with the GPU load meeting a second preset condition based on the maintained GPU load information, and sends an inference calculation result of the Prefill stage to the second calculation instance to execute inference calculation of the Decode stage.

The invention discloses an RISC-V-based thread bundle scheduling method and device and a medium, belongs to the technical field of general graphics processors, and aims to solve the technical problem that the execution efficiency of instructions is affected by the fact that an existing thread bundle scheduling module is affected by a synchronous fence mechanism and a thread bundle scheduling strategy, and the thread bundle scheduling efficiency is easily reduced. The method comprises the following steps: performing instruction configuration on a current thread bundle state to obtain a real-time thread bundle state instruction; performing state control on the thread bundle queue to obtain a state change control strategy; performing instruction fetching sequence control on the thread beam queue, and determining a thread beam instruction fetching control strategy; performing address fetch control on the thread bundle queue, and determining an address type control strategy; and performing thread beam scheduling processing based on a thread beam scheduling system architecture on the thread beam queue through the real-time thread beam state instruction and the thread beam scheduler after the strategy configuration is completed to obtain a thread beam scheduling result.

The invention discloses a configurable flexible job shop scheduling method based on a multi-agent game model, and the method comprises the steps: mapping a scheduling problem into a dynamic disjunction graph model to describe the relation between a process and equipment, representing process nodes, equipment nodes and the mutual relation through a node set and an edge set, and combining the flexibility of a dynamic disjunction graph to obtain a flexible job shop scheduling model. And matching of equipment and processes is dynamically adjusted according to changes of production requirements, so that the equipment utilization rate is optimized. And a clustering method based on structure entropy is adopted, and process nodes are clustered into sub-graphs, so that the search space of a scheduling problem is reduced, and the calculation complexity is reduced. Construction of an order agent and an equipment agent and a game and cooperation process between the order agent and the equipment agent are covered. And each agent optimizes a decision through a deep Q network, and cooperates or competes according to a respective target. An automatic optimization configurator is used for optimizing hyper-parameters and a network structure of a deep Q network in a multi-agent system, and scheduling performance and adaptability are further improved.

The invention relates to the technical field of power system optimization, and discloses a novel source-grid-load-storage integrated collaborative optimization method and system under a power system, and the method comprises the steps: extracting a power grid power supply layout parameter and a power grid operation parameter in the power grid parameter information of a target region; the power grid operation parameter prediction model is used for predicting power grid operation prediction parameters of a target scheduling period, then a scheduling priority list is constructed according to power grid power supply layout parameters, and the power grid operation prediction parameters of the target scheduling period are obtained by obtaining geographic position parameters and emergency power supply deployment information of each scheduling sub-region in the scheduling priority list. According to the access positions of the emergency power supplies and the deployment conditions of the fixed emergency power supplies and the mobile emergency power supplies in different dispatching sub-regions, a plurality of different influence factors are considered to carry out long-term and overall optimal dispatching, an emergency power supply dispatching strategy is generated, dispatching of the mobile emergency power supplies of the power system is executed, and the emergency power supply dispatching effect is improved. And the conditions of unbalanced regional load of the power system and poor experience of power users are relieved.

The invention relates to the technical field of intelligent manufacturing, and discloses a product production process scheduling method and system based on intelligent manufacturing, and the method comprises the steps: 1, data collection and analysis; 2, establishing a production process model; 3, applying an intelligent scheduling algorithm; 4, a real-time monitoring and feedback mechanism; and 5, continuously optimizing and learning. According to the invention, in a real-time monitoring and feedback mechanism, the scheme combines historical data to dynamically correct real-time production data, and performs anomaly detection. By comparing the trend of the current production data with the trend of the historical data in real time, the system can identify and correct data abnormity in time, and it is ensured that each link in the production process is smoothly carried out according to a predetermined plan. The mechanism has the advantages that the accuracy of production data can be ensured by quickly detecting and correcting the deviation, the production delay caused by data errors or lagging is avoided, and the production efficiency and the scheduling precision are improved.

The invention discloses a polycrystalline silicon production industrial load participated power grid dispatching method and related products. According to the method, a target function is constructed according to the maximum adjustable capacity of a polycrystalline silicon factory within a preset time, and constraint conditions comprise a material balance constraint, an electric quantity balance constraint, a storage constraint, a yield constraint, a purification task starting constraint and a reduction task starting constraint. The constraint conditions consider the coupling relationship of each link in the polycrystalline silicon production process, so that the formed economic dispatching model can improve the dispatching capability of the polycrystalline silicon production industrial load to the power grid. Moreover, in the construction process of the economic dispatching model, materials and processes are abstracted into states and tasks respectively by utilizing a state task network method in the polycrystalline silicon production process, and a state task network block diagram is drawn to describe the coupling relationship among the processes, so that the accuracy of the model can be ensured.

The invention provides a mixed traffic transmission method and system in a time sensitive network, and relates to the technical field of mixed traffic transmission, and the method comprises the steps: constructing a TSN time sensitive network topology and a directed graph model of a time trigger flow; obtaining a TT stream set and an AVB stream set, and sorting the priorities of the TT stream set and the AVB stream set; performing preliminary scheduling on the sorted TT stream set and AVB stream set by using an ASAP scheduling algorithm, generating a time slot after the preliminary scheduling of all streams is completed, determining an initial scheduling scheme according to the time slot, and calculating the total worst end-to-end time delay of all streams in the stream set from a source node to a destination node by using network calculation; selecting the stream with the maximum priority from the AVB stream set to exchange with the stream in the TT stream set, re-scheduling, and deleting the exchanged AVB stream from the AVB stream set; and recalculating the total worst time delay until all AVB streams participate in exchange, and determining an optimal scheduling scheme. According to the invention, the scheduling efficiency of the AVB stream is improved.

The invention discloses an electric quantity adjustment method and device for coping with extreme weather, and the method comprises the steps: obtaining the area of a disaster region when the extreme weather occurs, the duration of the extreme weather, and the electric quantity adjustment demand parameter data of the duration, and obtaining a time sequence of electric quantity adjustment demand parameters; performing normalization processing to obtain an electric quantity adjustment demand parameter normalization value; calculating an influence coefficient of the electric quantity adjustment demand index in the next time period based on the area of the affected area, the normalized value and the electric quantity adjustment demand parameter data; and calculating a predicted value of the extreme weather electric quantity adjustment demand index in the next time period to obtain the adjusted output electric quantity. According to the method, the power demand under the extreme weather condition can be predicted, a basis is provided for reasonable configuration and resource scheduling of a power system, and the method is very necessary. The method can effectively improve the adjustment capability of the power system and the multi-energy scheduling capability, promotes the power system to quickly recover stability after extreme weather, and guarantees the stable operation of the power system.

The invention provides an optical storage DC flexible power distribution system and a regulation and control method thereof, and the system comprises a plurality of transformer area systems carried in a plurality of transformer area energy stations and a plurality of household subsystems carried in a plurality of household energy walls. The transformer area system comprises a static change-over switch, an isolation transformer, a bidirectional energy converter and the like. The transformer area system takes power from a power grid and supplies the power to a direct-current bus. The household subsystem comprises a photovoltaic module, an energy storage module, a charging pile, a direct-current load, an alternating-current load and an energy management system controller, and the household subsystem is connected to the direct-current bus; the plurality of transformer area systems are connected to the lower end of the static change-over switch, and the plurality of household subsystems are connected through a direct current bus. According to the optical storage direct flexible power distribution system provided by the embodiment of the invention, interaction between a power grid and a transformer area, between transformer areas, between transformer areas and users and between users can be perfectly adapted, multiple transformer areas are combined into a whole, the electric energy dispatching capability is improved, and work such as peak load shifting and response to a virtual power plant can be better realized.

The embodiment of the invention discloses a picking rechecking task management method and device, equipment and a medium, and the method comprises the steps: obtaining picking task information and picker information corresponding to a picking task in an ongoing state, and the working state information of a rechecking table; performing information prediction according to the acquired information to obtain a residual picking time length, an average transportation time consumption of transporting the goods to each rechecking station, a first estimated time consumption of processing an uncompleted picking rechecking task by each rechecking station and a second estimated time consumption of completing a to-be-allocated picking rechecking task corresponding to the picking task by each rechecking station; and determining a target review station corresponding to each to-be-allocated order picking review task according to the predicted information by taking the shortest review completion estimation time of all the to-be-allocated order picking review tasks as a target task scheduling result so as to perform task scheduling management. According to the technical scheme of the embodiment of the invention, manual scheduling of the picking rechecking task can be replaced, the task scheduling cost is reduced, and the task scheduling efficiency and effect are improved.

The invention discloses a power grid dispatching method and device and a storage medium. The method comprises the following steps: constructing an optimal power flow optimization model of a power grid system and a mobile energy storage vehicle optimization dispatching model; setting a first objective function and a first constraint condition of the optimal power flow optimization model, and setting a second objective function and a second constraint condition of the mobile energy storage vehicle optimization scheduling model; modeling the mobile energy storage optimization scheduling problem into a mixed integer linear programming problem, and solving the mixed integer linear programming problem based on the first objective function, the first constraint condition, the second objective function and the second constraint condition according to the node load and the carbon emission factor of the power grid system to obtain an updated path planning and charging and discharging scheme. According to the method, the carbon emission factor is introduced to fully consider the carbon emission characteristics of energy storage, the carbon emission is taken as a main optimization target, and the charging and discharging behaviors of the energy storage system can be dynamically optimized, so that the carbon emission can be effectively minimized, and the carbon emission effect can be effectively improved.

The invention relates to the technical field of unmanned aerial vehicle communication, in particular to an avoidance method and device based on optical communication unmanned aerial vehicle identification information automatic broadcasting, and the method comprises the steps: obtaining the current geographic position information of a target unmanned aerial vehicle, and calculating the kinematics information of the target unmanned aerial vehicle in combination with the historical geographic position information; calculating configuration information of a wireless light source of the target unmanned aerial vehicle according to the kinematics information so as to set a communication boundary of the wireless light source through the configuration information; constructing to-be-transmitted state information, and constructing the to-be-transmitted state information into a data frame conforming to a preset frame structure; controlling a wireless light source to broadcast a data frame into the space; and receiving state information of other unmanned aerial vehicles, and controlling the target unmanned aerial vehicle to avoid according to the state information. Therefore, the interference to the existing radio frequency network is reduced, the security of data transmission is improved, the cooperative control performance of the unmanned aerial vehicle cluster is improved, the real-time monitoring and scheduling capability is enhanced, and the method has important significance for constructing a safe and efficient unmanned aerial vehicle system.

The invention discloses a cement production industrial load participated power grid dispatching method and related products. According to the method, an objective function is constructed by minimizing the power purchase cost of a cement plant within preset time, constraint conditions comprise a material balance constraint, an electric quantity balance constraint, a storage constraint, a yield constraint and a rotary kiln continuous work constraint, and the constraint conditions consider the coupling relation of all links in the cement production process. Therefore, the formed economic dispatching model can improve the dispatching capability of the cement production industrial load on the power grid. Moreover, in the construction process of the economic dispatching model, materials and processes are abstracted into states and tasks respectively by utilizing a state task network method aiming at the cement production process, and a state task network block diagram is drawn to describe the coupling relationship among the processes, so that the accuracy of the model can be ensured.

The invention relates to a cloud resource scheduling method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring scheduling parameters of a virtual machine; matching the user identifier in the scheduling parameter with the user identifier configured by each preset computing node group to obtain a matched computing node group, each preset computing node group being adjusted in real time in the running process of the cloud resource service; performing static resource scheduling screening on each computing node in the matched computing node group based on the scheduling parameter to obtain a target computing node; and creating a virtual machine at the target computing node. By adopting the method, refined resource scheduling can be realized.

The present application relates to the technical field of flexible job shop scheduling in intelligent manufacturing and production scheduling, and particularly relates to an optimization method for solving flexible job shop scheduling with adjusted resource constraints. The method comprises the following steps: initializing parameters and randomly generating an initial population; using a crossover operator and a mutation operator in sequence to evolve the current population; executing a hybrid decoding strategy on the current population; sorting the individuals in the current population in ascending order of maximum completion time to form an elite population, and updating the elite population through problem-specific local search; judging whether the evolution condition is met, and if yes, executing mathematical evolution based on CP, and outputting a final solution when the running time reaches the total running time. The present application has the positive effects of reducing resource waiting time, improving machine utilization, and improving the resource utilization efficiency and scheduling performance of the entire workshop production.

The embodiment of the present invention discloses a method for determining accompanying information, including: determining the target object to be accompanied, querying the trajectory data of each trajectory point of the target object within a set time range according to the unique information of the target object; dividing the trajectory point into a set number of accompanying areas according to the trajectory data, and querying the accompanying objects that co-appear with the target object in at least two accompanying areas within the set time range; obtaining the accompanying trajectory data of each accompanying object, and determining the accompanying rate corresponding to each accompanying object according to the accompanying trajectory data, and determining the accompanying trajectory data and the accompanying rate as the accompanying information. The method for determining accompanying information provided by the embodiment of the present invention divides the accompanying area according to the trajectory data of the target object, and then determines the accompanying object and the corresponding accompanying information that co-appears with the target object, thereby realizing the use of big data to analyze and mine the distribution rules in time and space, and improving the reliability of the trajectory accompanying rate and the method.

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 provides a kind of desertification irrigation water resource intelligent scheduling system and method based on deep learning.The existing desertification irrigation water resource scheduling mode is mostly traditional experience type scheduling or simple quantitative scheduling.The application collects vegetation growth state data, soil moisture data, meteorological data, water resource supply and demand data, water quality data and water quantity data in desertification area, and transmits to data preprocessing module;standardized data is input into water resource demand prediction model, and vegetation water resource demand prediction information is output;based on water resource demand prediction information, the optimal water resource allocation scheme is selected;the execution module completes the scheduling supply of water resources.The application realizes the accurate scheduling of desertification irrigation water resources, improves water resource utilization rate, reduces waste and guarantees vegetation growth.

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 discloses a wind and light output prediction-based energy scheduling system, which comprises a data processing module, a control module and a power supply module, and is characterized in that the data processing module predicts wind power and photovoltaic power generation according to collected wind and light output data through a CNN-BiLSTM prediction model, and the wind and light output is predicted according to the predicted wind and light output and a scheduling strategy of the lowest system operation cost; the global search capability is improved by introducing a spiral search strategy, so that the situation that spider bees fall into local optimum when selecting a search space is avoided, an optimal system operation scheme is given by adopting an improved spider bee algorithm based on wind and light output prediction data and an energy management strategy, and safe operation of the system is ensured. The generating capacity of the new energy power station is regulated and controlled from the perspective of wind and light output, effective absorption of new energy and effective supply of electric energy required by users are achieved, and the reliability of the system can be guaranteed.

The present disclosure relates to a YARN cluster resource scheduling method, apparatus, medium and computer equipment, wherein the method includes: receiving a job resource request of a job task sent by an application manager; when determining that the job resource request is a rigid resource request, adding the rigid resource request to a request queue, the request queue includes multiple request units, and multiple rigid resource requests belonging to different job tasks are added to corresponding different request units; starting multiple first threads, each first thread obtains multiple rigid resource requests in each corresponding request unit from the request queue to perform resource pre-allocation processing; after resource pre-allocation, each first thread determines whether each pre-allocation result meets the resource demand of the corresponding job task; if so, each first thread submits the pre-allocation result to the application manager respectively. The implementation scheme of the present disclosure can improve the resource scheduling performance of the cluster.

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 invention provides a detection and electromagnetic interference method and system based on an intelligent protective vest, and relates to the technical field of individual combat intelligent protective vests.The method comprises the steps that a positioning chip, a radar detection system and an electromagnetic interference system are assembled in the intelligent protective vest, and the position information of an individual soldier is obtained based on the positioning chip; performing airspace target detection based on the position information coverage range through a radar detection system; and determining whether the electromagnetic interference system starts electromagnetic interference based on the airspace target detection result. The position information of the soldier can be acquired in real time through the positioning chip, and the airspace target in the detection range is acquired through the radar detection system to provide early warning for the soldier. When an enemy aircraft is detected, an electromagnetic interference system is started to enable the enemy aircraft to lose a flying target, and soldiers are protected from air threats.

The application discloses a novel flexible manufacturing system dynamic scheduling method based on multi-agent reinforcement learning, relates to the technical field of intelligent manufacturing, and comprises the following steps: constructing a cross-domain knowledge graph driven state space, fusing process knowledge and equipment capacity to perform semantic embedding; establishing an adaptive evolution intelligent agent collaborative network of a space-time heterogeneous graph, and dynamically adjusting a topological structure according to task dependence and equipment coupling; adopting a progressive collaborative value decomposition network to optimize a strategy and accurately realize credit distribution; and constructing a multi-scale closed-loop compensation decision mechanism to form deep coupling and closed-loop feedback at the equipment layer, the production line layer and the system layer, so that the scheduling efficiency and robustness of the flexible manufacturing system are significantly improved.

The invention provides a resource scheduling method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: determining a first resource node matched with a resource scheduling request, wherein the resource category of the first resource node and the resource category of the resource scheduling request are a first category; for each first resource node, determining a scheduling parameter value of the first resource node based on the resource demand quantity of the resource scheduling request and the available resource quantity of the first resource node; determining a second resource node from the first resource nodes based on the scheduling parameter values of the first resource nodes; and scheduling resources for the resource scheduling request from the resources of the second resource node. According to the invention, the resource scheduling effect can be improved.

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 invention discloses a generator scheduling method, belongs to the field of communication, and is used for improving the generator scheduling capability. The method comprises the following steps: acquiring a first position of first equipment and second positions of a plurality of first generators, wherein the first generators are dispatchable generators; determining a second generator from the plurality of first generators based on the distance between the first position and each second position, the second generator being a generator scheduled to the first device; obtaining the departure time of a second generator, and determining the arrival time of the second generator reaching the first position based on the departure time, the first position and a second position corresponding to the second generator; and when the arrival time is up, sending a wake-up instruction to the second generator to enable the second generator to generate power for the first 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.