16 results about "Flexible scheduling" patented technology

The application provides a flexible scheduling group and a scheduling method. The scheduling group comprises a manufacturing execution system, a logistics execution system, an intelligent group control system, a logistics intelligent trolley scheduling system and a process intelligent trolley scheduling system. The intelligent group control system receives a production plan issued by the manufacturing execution system, receives inventory material information of the logistics execution system, receives vehicle position information of the logistics intelligent trolley scheduling system and the process intelligent trolley scheduling system respectively, and formulates scheduling tasks of the process intelligent trolley and the logistics intelligent trolley in real time. Through deep cooperation of the intelligent group control system with the manufacturing execution system, the logistics execution system and other systems, overall management of the whole production process is realized. Through real-time sensing of data of environmental changes and / or data of demand fluctuations by the intelligent group control system, and through on-demand pulling, the coordination and stability of line operation are improved through cooperation with multiple systems.

This invention discloses a method and system for optimizing game resources in a virtual game. The invention relates to the technical field of virtual games. It loads the current load of the virtual game based on a target resource demand sequence and constructs the resource distribution of the virtual game. Then, it performs topological clustering based on the content to be loaded in the virtual game, generating a resource topology map containing multiple game resource clusters, thus improving the accuracy of the resource topology map. The node dependencies in the resource topology map are marked, and flexible scheduling is performed based on the resource content allocated to each resource cluster, thereby outputting a multi-level flexible scheduling strategy. The high-speed video memory area and low-speed memory area of ​​the virtual game are optimized along this flexible scheduling strategy. Resource optimization events of the virtual game are determined based on the smooth constraints of the virtual game. Furthermore, resource anomaly signals of the virtual game are used to determine optimization configuration measures for the high-speed video memory area and low-speed memory area in terms of resource dimensions, improving the accuracy of the optimization configuration measures.

This invention discloses a flexible scheduling method, system, and application for UPMS (Upright Manufacturing System) workshops based on an improved differential evolution algorithm. The method includes: establishing an independent parallel machine scheduling model with sequentially dependent mold-changing time as the objective of minimizing the maximum completion time; using real-valued vector encoding and decoding it into a scheduling scheme through the maximum position value rule; performing mutation and crossover operations of the differential evolution algorithm on the generated test vectors, with optimizations including load balancing optimization based on destruction and reconstruction and mold-changing optimization based on sequential smoothing; finally, updating the population and outputting the optimal scheduling scheme. This invention significantly reduces sequentially dependent mold-changing time, achieves dual optimization of load balancing and mold-changing cost, and improves solution accuracy and efficiency, making it particularly suitable for discrete manufacturing workshops such as textile printing and dyeing, and injection molding.

The application discloses an edge intelligent body flexible scheduling method based on Lyapunov optimization and Stackelberg game. The application constructs a cloud-edge-end three-layer edge intelligent body federated learning network architecture to complete distributed collaborative training under the premise of protecting data privacy. In view of the problems of heterogeneous edge intelligent body network nodes, non-independent and identically distributed data and limited communication resources, a long-term joint optimization model of client selection and bandwidth allocation is established, and model accuracy, time delay and energy consumption are considered. Lyapunov optimization is used to decouple long-term random optimization into real-time decision-making per time slot, and to convert constraints into virtual queue stability control. Dynamic client selection is realized based on Stackelberg game, and high-quality nodes are adaptively selected. Adaptive bandwidth allocation is completed through an evolutionary algorithm, and communication requirements of key clients are preferentially guaranteed. The application can significantly improve model accuracy, reduce time delay and energy consumption, guarantee long-term stability of the system, and is suitable for high-dynamic edge intelligent body federated learning scenes.

The application provides a task processing method and device, which are applied to a terminal. The task processing method comprises the following steps: acquiring sequence state information of a to-be-processed task sequence, and determining a current query frequency according to the sequence state information, wherein the order of each to-be-processed task in the to-be-processed task sequence is determined according to priority information corresponding to each to-be-processed task; a current to-be-processed task is acquired from the to-be-processed task sequence based on the current query frequency; a target processing strategy of the current to-be-processed task is determined according to available resource information corresponding to the terminal; and the current to-be-processed task is processed according to the target processing strategy. The application implements a dynamic flow limiting strategy, can distinguish the priority of to-be-processed tasks, and realizes flexible scheduling and adjustment of to-be-processed tasks in the case that the importance of to-be-processed tasks is different and resource occupation is different, thereby improving the resource utilization efficiency of the terminal.

This invention discloses a visualized flexible scheduling method and system for machining production, relating to the field of production scheduling technology. The method includes: obtaining machining machine groups and the machining coordinate system of each machining machine group based on the process machines of all core processes; obtaining the first span machine of schedulable parts and all process machines based on simulated machining; and performing flexible scheduling using a multi-machine scheduling method. This invention addresses the problem in existing flexible scheduling methods for machining production where, when there are many types of machining machines in the workshop and multiple processing routes for parts, the numerous possible equipment combinations in case of machine failure lead to poor timeliness or local optima after rescheduling the machining machines, thus preventing the most efficient flexible scheduling of parts.

The application discloses a 6G all-in-one network management computing power session method and system, and belongs to the technical field of mobile communication.The technical problem to be solved by the application is how to realize flexible scheduling and on-demand allocation of computing power and network fusion resources in a 6G all-in-one network environment without changing network devices, and to build a mobile network environment supporting the computing power as a service mode for users.The technical scheme adopted is that the method is to deploy an algorithm network fusion perception network element, an algorithm network fusion control scheduling network element and an algorithm network fusion service arrangement network element on the core network side, so that the improved core network has the functions of communication, perception and computing power integration;and the computing power application request index is analyzed and identified, the computing power service arrangement is performed, the computing power and network Qos configuration mapping is performed, the algorithm network fusion resources are decided and scheduled, and the distributed collaborative computing task is issued, so that the computing power session creation is completed, so as to meet the different computing power requirements of mobile terminals.

This invention relates to the field of outbound logistics and discloses a method, apparatus, equipment, and medium for outbound avoidance in a warehousing and logistics system. The method includes: when several hangers awaiting outbound are currently in an idle state and not in a prohibited outbound time interval queue, calculating the total outbound time for each hanger; obtaining the outbound timestamp of the first hanger and, combined with the total outbound time, calculating the outbound timestamp of the last hanger; updating the current storage bar to an outbound state, and calculating the avoidance time intervals for other storage bars based on the outbound timestamps of the first and last hangers and the track running speed, adding them to the corresponding prohibited outbound time interval queues; issuing an execution command to cause the current storage bar to perform a mechanical action, and after execution, updating it to an idle state. This achieves outbound avoidance functionality, is easy to execute and manage, has low cost, and allows for flexible scheduling without hanger data limitations during a single outbound process, making the entire process efficient and simple.

This application provides a license scheduling method, system, storage medium, and electronic device, relating to the field of cloud service technology. The technical solution provided in this application determines the degree of license stability required by the current license request and allocates it to a static license pool or a dynamic license pool. The first license sequence in the static license pool has a lower license switching frequency, providing users with long-term stable license guarantees and avoiding frequent license opening and closing operations that disrupt user workflows. The second license sequence in the dynamic license pool allows for a higher license switching frequency to improve the utilization efficiency of license resources. Through this dual-pool separation and differentiated scheduling mechanism, the system ensures both the continuity of licenses and work stability for users with high stability requirements, while also enabling flexible scheduling and efficient resource utilization for users with low stability requirements. It can adopt targeted scheduling methods based on users' stability needs, thereby improving user experience.

This invention discloses a virtual power plant system scheduling method based on deep reinforcement learning, belonging to the field of power energy optimization management technology. The method includes: collecting multi-source real-time data from the virtual power plant; constructing a causal graph model and generating causal feature vectors and adjacency matrices; inputting the causal feature vectors and adjacency matrices into a reinforcement learning framework, performing enhancement processing through a causal attention mechanism to generate an optimized scheduling strategy; extracting economic and safety objective function values ​​from the optimized scheduling strategy, constructing a multi-objective optimization problem, and using gradient descent to track equilibrium points and obtain a flexible scheduling scheme; inputting the flexible scheduling scheme into a high-fidelity digital twin model, performing behavioral risk detection during simulation operation, and outputting a risk report and correction parameter vectors. This invention achieves a leap from statistical correlation analysis to causal reasoning, solving the problem of blind spots in scheduling decisions under high-dimensional uncertainty.

This invention discloses a multi-path scheduling and sub-agent generation method and system for intelligent agents, belonging to the field of artificial intelligence. The method provides multiple mutually exclusive scheduling paths: a collaborative agent generation path distributes tasks to agents in the same process or terminal split-screen sessions through a delegation mechanism; a sub-agent derivation path achieves efficient sub-agent creation through context inheritance and hint cache sharing optimization; and a general agent scheduling path achieves flexible scheduling through rule filtering and definition matching. This invention implements hint cache sharing optimization for derivation paths, and by standardizing the processing of tool execution results in parent agent messages, it enables API request prefixes to achieve cache hits, significantly reducing the response time of the first token. It also supports features such as execution environment isolation, recursive derivation protection, and automatic background operation, providing an efficient and flexible scheduling solution for multi-agent collaboration of complex AI tasks.

The application discloses a kind of based on industrial knowledge graph man-machine dual resource coordination workshop flexible scheduling method.First, the workshop scheduling knowledge graph of fusing workpiece, process, machine, worker entity and its associated relationship is constructed, to provide structured semantic constraint support for decoding process.Further, a kind of dual population collaborative evolution framework is designed, wherein global population focuses on convergence, and local population protects isomeric solution through decision space niche to maintain diversity.In the evolution process, the adaptive knowledge transfer mechanism based on the edge weight change of knowledge graph is introduced, and the information exchange between populations is dynamically adjusted.The output Pareto optimal solution set is fed back to the knowledge graph as scheduling experience, to enhance the weight of high-quality resource path.The application combines physical constraint modeling and scheduling knowledge accumulation, significantly improves the search efficiency and solution quality in huge solution space, and can effectively obtain collaborative optimization scheduling scheme considering maximum completion time, total cost and total carbon emission.

The application discloses an RPA process scheduling method based on large model fine tuning, comprising the following steps: constructing an RPA process knowledge base, fine tuning an intention classification model and an entity recognition model, collecting user instructions, inputting the user instructions into the fine-tuned intention classification model for classification, obtaining corresponding intentions, inputting the corresponding intentions into the fine-tuned entity recognition model and the RPA process knowledge base respectively, extracting relevant entities and matching corresponding processes to obtain entity extraction results and process matching results; and inputting the entity extraction results and the process matching results into the RPA to execute the RPA process. Through the organic combination of the double model, the RPA knowledge base architecture and the RPA process, the application realizes accurate recognition of user instructions and flexible scheduling of the RPA process.

This invention provides a flexible scheduling method, system, and storage medium for co-production processes, specifically including: collecting and processing scheduling index judgment data from multiple experts; after completing data consistency verification, converting qualified expert judgments into expert fuzzy weight vectors representing judgment uncertainty; dividing the data into multiple opinion clusters based on the distance metric between vectors using a clustering algorithm, aggregating vectors within each cluster using expert weights to obtain the central fuzzy weight of each opinion cluster; building a rule base for the association between real-time production status parameters and opinion clusters; matching target opinion clusters based on the current production status; processing and defuzzifying the central fuzzy weights to determine the index weights for the current scheduling cycle; constructing and solving a multi-objective optimization scheduling model to generate a scheduling scheme; and updating the expert weights based on scheduling performance evaluation feedback after scheme execution to obtain a closed-loop correction mechanism, thereby optimizing the scheduling index weights and scheduling strategy.

1. Name of the product in this design: Modular Energy Storage and Boost Transformer. 2. Purpose of this design: To achieve efficient conversion and flexible scheduling of electrical energy. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.