13results about How to "Improve resource scheduling efficiency" patented technology

The invention, which relates to the technical field of intelligent logistics management, discloses an AI-based intelligent logistics management system comprising a warehouse management unit, a data fusion acquisition unit and a digital twin supervision unit. The warehouse management unit is used for managing warehouse-in and warehouse-out information and cargo information of a warehouse, reminding inventory risks, and carrying out demand prediction and inventory optimization; the data fusion acquisition unit is used for acquiring personnel, goods, equipment and environment information of a warehouse; the digital twin supervision unit comprises an intelligent AI recognition unit and a data analysis processing unit, the intelligent AI recognition unit is used for carrying out recognition analysis and risk reminding on personnel, goods, equipment and environment of a warehouse through an AI intelligent algorithm, and the data analysis processing unit is used for mapping data of an entity warehouse to a digital twin warehouse. And visual interaction management and intelligent scheduling of personnel, goods and equipment are carried out on the digital twin warehouse, so that the warehouse can be comprehensively monitored, and the goods storage and management efficiency is improved.

The invention provides a resource scheduling method, device and equipment, a medium and a computer program product. The method comprises the following steps: converting received service demand information into resource demand parameters; performing correlation evaluation on the resource data information and the resource demand parameter of the scheduling node based on the resource data prediction model to obtain resources required by the service; the resource data prediction model is obtained by training an LSTM model based on historical resource data; the parameter of the resource data prediction model is an optimal parameter combination obtained by performing population iteration optimization on a particle swarm based on a particle swarm optimization algorithm by taking the loss value of the LSTM as a fitness value and the parameter of the LSTM as the particle swarm in a model training stage; and determining a resource scheduling scheme based on the resources required by the service and the resource scheduling model. According to the method, through unified node resource data management, historical resource data are trained by applying the particle swarm optimization algorithm and the LSTM model, and efficient scheduling and accurate scheduling of resource demands are realized.

The invention belongs to the technical field of resource scheduling, and provides an online resource dynamic scheduling method and system for a power transformation intelligent patrol cloud platform, and the technical scheme comprises the steps: obtaining historical resource scheduling data of the power transformation intelligent patrol cloud platform; training a resource quantity prediction model based on the obtained historical resource scheduling data, and predicting the resource quantity required by the to-be-scheduled task based on the trained resource quantity prediction model; establishing a fitness function according to the total time and the total cost of the to-be-scheduled task, taking the fitness function as an optimization target, and scheduling idle resources of the service terminal by using a multi-target longhorn cattle herd combined artificial fish swarm hybrid algorithm to obtain an optimal resource scheduling scheme; the advantages of good global convergence of the artificial fish swarm algorithm and local rapid convergence, easy realization and the like of the Tiancattle swarm algorithm are comprehensively utilized, the defects that the convergence speed of the artificial fish swarm algorithm is low and the later global convergence of the Tiancattle swarm algorithm is poor are overcome, the advantages of the artificial fish swarm algorithm and the Tiancattle swarm algorithm are fully combined, and the optimal allocation problem of cloud resource scheduling can be well solved.

The invention relates to the technical field of optimal scheduling, in particular to an optimal scheduling method based on physical ID drive and a digital twinborn model. The method comprises the following steps: carrying out landform modeling on a target fruit farm to obtain a three-dimensional landform model of the fruit farm; carrying out planting area data acquisition on the target fruit farm to obtain detailed data of a planting area of the fruit farm; performing intelligent semantic annotation on the three-dimensional terrain model of the fruit farm according to detailed data of a planting area of the fruit farm to obtain a digital twin model of the fruit farm; performing passable path intelligent identification on the fruit farm digital twinborn model, and performing identifier endowing to obtain a fruit farm path network diagram and a fruit farm path ID list; performing picking operation area division on a target fruit farm based on the fruit farm digital twinborn model, and performing identifier endowing to obtain a fruit farm picking area division map and a fruit farm acquisition area ID list; according to the method, the resource scheduling efficiency is remarkably improved, including reasonable distribution of personnel, effective use of tools and optimal scheduling of transport vehicles.

The invention relates to an enterprise project informatization management method, device and system and a storage medium. The method comprises the steps of obtaining enterprise project internal data, enterprise project external data, initial fuzzy reasoning parameters and project execution historical data corresponding to a target enterprise; performing dynamic fuzzy reasoning on the enterprise project internal data and the enterprise project external data according to the initial fuzzy reasoning parameters to obtain dynamic fuzzy reasoning data; according to the dynamic fuzzy reasoning data and project execution historical data, identifying a relationship between each execution object and each task detail in the target enterprise to obtain project behavior analysis data; performing structured fusion on the dynamic fuzzy reasoning data and the project behavior analysis data to obtain enterprise project fusion data; and scheduling fixed resources and human resources of the target enterprise according to the enterprise project fusion data to obtain enterprise project management information. By adopting the method, the resource scheduling efficiency of the enterprise management project can be improved.

The invention provides a scheduling information generation method and apparatus, and a related device. The method comprises the steps of matching a target server corresponding to a to-be-processed task in a plurality of candidate servers; setting a reward function according to the target server and a first deep learning network, and updating the first deep learning network based on the reward function to obtain a second deep learning network; and inputting the to-be-processed task into the second deep learning network for processing, and generating a target scheduling strategy. According to the method and the device, after the target server corresponding to the to-be-processed task is matched, the corresponding first deep learning network is determined according to the target server, so that the first deep learning network is generated after the first deep learning network is updated by setting the reward function; and through the second deep learning network, generating a scheduling strategy for the target server to process the to-be-processed task, thereby improving the resource scheduling efficiency of the server.

This invention provides a method and related equipment for generating digital human videos based on the Ascend AI processor, optimizing digital human synthesis efficiency through dynamic resource scheduling and task splitting mechanisms. The method includes: after a user submits a task, a task forwarding service performs sensitive word detection and generates synthesized audio; the main video synthesis service dynamically adjusts its deployment based on Ascend AI processor resources, splitting the task into sub-tasks and distributing them to sub-services; frame integrity verification ensures synthesis quality, and finally, the video is generated and stored. This invention significantly improves resource utilization and task execution reliability, making it suitable for large-scale digital human video generation scenarios.

This application discloses a resource scheduling method, apparatus, electronic device, and medium, belonging to the field of network resource scheduling technology. The method includes: acquiring energy consumption data, latency data, and load data from multiple servers, wherein the first server includes a cloud server and an edge server; inputting the energy consumption data, latency data, and load data into a preset resource scheduling model, and outputting multiple system data of the resource scheduling system composed of the multiple servers; inputting the multiple system data into a preset deep learning model, and outputting response data corresponding to the resource scheduling system to represent the network environment's response to the state; the deep learning model is a model combining a time-recurrent neural network and an attention mechanism; and scheduling the network resources of the multiple servers based on the response data. The embodiments of this application employ a resource scheduling model and a deep learning model to improve resource scheduling efficiency.

The embodiment of the invention provides a TSN resource scheduling system, method and device, a medium and a product, relates to the technical field of communication equipment, and aims to solve the problem that a general resource scheduling method cannot ensure low delay and low jitter of a TSN. The resource scheduling system of the TSN comprises a TSN controller and a plurality of network nodes, the plurality of network nodes comprise a starting node and a destination node; the starting node and the destination node are configured with a TSN configuration proxy TCB client; the TCB client is used for collecting service demand information of a node to which the TCB client belongs, and sending the service demand information to the TSN controller; and the TSN controller is used for carrying out resource scheduling according to the service demand information.

The embodiment of the invention discloses a resource scheduling method, device and equipment, and the method comprises the steps: receiving a resource scheduling request for a to-be-constructed target communication project, determining a to-be-scheduled region corresponding to the target communication project in response to the resource scheduling request, and obtaining the schedulable resource information of the to-be-scheduled region, and determining a plurality of candidate scheduling strategies and action values corresponding to the candidate scheduling strategies based on schedulable resource information by using a pre-trained decision model based on importance weights determined based on the resource quantity and / or position information of the to-be-scheduled region, and determining the action values corresponding to the candidate scheduling strategies based on the importance weights of the to-be-scheduled region. And adjusting the action values corresponding to the candidate scheduling strategies, determining a target scheduling strategy from the candidate scheduling strategies based on the adjusted action values, and performing resource scheduling processing on the target communication project based on the target scheduling strategy.

The invention provides a resource scheduling method and device and electronic equipment. The method comprises the steps that target computing power resources corresponding to resource information with the minimum resource weight in multiple resource queues are scheduled; deleting the resource information of the target computing power resource from the target resource queue, and subtracting a first preset value from the resource weight of each piece of resource information in the target resource queue; wherein the resource weight of each piece of resource information in the plurality of resource queues is in positive correlation with the sequence of placing the resource information into the resource queues, the first preset value is the sum of the second preset value and the preset inertia factor of the target resource queue, and the target resource queue is the resource queue where the resource information of the target computing power resource is located. In the resource scheduling process of the embodiment, the target computing power resource corresponding to the resource information with the minimum resource weight in the plurality of resource queues is scheduled, so that the resource scheduling efficiency is improved.

This paper relates to the field of refined oil resource scheduling technology, and provides a method, apparatus, equipment, and storage medium for refined oil resource scheduling. The method includes: using a simulator to simulate the scheduling scheme for the first sub-cycle of the current cycle based on the node parameters of each node, to obtain the inventory level and sub-cycle reward value of each node at the end of the first sub-cycle; using a deep reinforcement learning module to predict the target inventory level of each node at the end of the first sub-cycle of the next cycle based on the inventory level, sub-cycle reward value, and scheme penalty value of the current cycle, with the second sub-cycle of the current cycle as the first sub-cycle of the next cycle; using a mathematical programming module to determine the scheduling scheme for the next cycle based on the target inventory level and the operational constraints of each node, with the goal of minimizing scheduling costs and inventory level differences; and performing refined oil resource scheduling on each node according to the scheduling scheme of the first sub-cycle of the next cycle. The embodiments described in this paper can improve the efficiency of refined oil resource scheduling.

A mine car resource scheduling method, system, device and medium based on a second constraint convex optimization method, the method comprising: establishing a scheduling model based on a second constraint convex optimization with the overall minimization of transportation time and processing time and the maximization of transportation volume as the optimization target; linearizing the constraint conditions of the optimized scheduling model; obtaining an optimal solution by KKT condition calculation of the convex optimization scheduling model through linearization transformation and genetic algorithm; and calculating the reasonable number of mine cars, the number of excavators and the optimal speed and optimal spacing of travel in real time to achieve the goal of minimum overall efficiency processing time and maximum workload of the mine car resources; the system, device and medium are used to implement the mine car resource scheduling method based on the second constraint convex optimization method; the reasonable number of mine cars, the number of excavators and the optimal speed of travel are calculated in real time by solving the multi-objective convex optimization problem through the construction of the scheduling model, so as to achieve the goal of minimum overall efficiency processing time and maximum workload, realize the resource matching and optimal scheduling of the mine car and the excavator, efficiently solve the problem, greatly improve the efficiency of the mine car resource scheduling, and improve the comprehensive management level of vehicle transportation and operation.