257results about How to "Improve scheduling efficiency" patented technology

The invention provides a distributed computing resource smart evolution method and system based on digital twinning, and relates to the technical field of resource evolution.The distributed computing resource smart evolution method comprises the steps that a smart sensing probe is deployed at a computing node to collect operation data, a double-layer attention mechanism is used for extracting features, and a digital twinning model is constructed based on a graph neural network; a resource allocation strategy and a scheduling decision are optimized in combination with a hierarchical reinforcement learning framework, a prediction sequence is generated through an attention-enhanced sequence prediction network, and finally an optimal configuration scheme is solved by adopting a particle swarm algorithm. According to the method, the utilization rate of distributed computing resources can be improved, the energy consumption of the system is reduced, and intelligent scheduling and optimal configuration of the computing resources are realized.

The invention belongs to the technical field of power prediction, and discloses a new energy power prediction method and system based on multi-source meteorological data fusion, and the method comprises the steps: obtaining meteorological data from different meteorological data sources in real time or at regular time through a data obtaining module; carrying out preliminary screening and preprocessing on the obtained data by utilizing a data processing module; the data fusion module is used for integrating meteorological data from different sources by adopting an advanced data fusion algorithm to eliminate data redundancy; a mathematical model or a machine learning model suitable for new energy power prediction is constructed by using the prediction model module based on meteorological data of multi-source fusion; and the result output and evaluation module is used for outputting the prediction result to a user in a visual form, and carrying out real-time or regular evaluation on the prediction result. The method has the characteristics of diversified data sources, advanced data fusion technology, flexible prediction model construction, high real-time performance and accuracy, wide application scenarios and the like.

The invention discloses a construction site digital-to-analog conversion and collaborative management method based on a distributed computing architecture. The method comprises the following steps: S1, collecting microscopic level data of a construction site; s2, obtaining mid-level data of the construction site; s3, acquiring macroscopic hierarchical data, and constructing a multi-scale spatio-temporal data set; s4, decomposing and processing the multi-scale spatio-temporal data set by using a spatio-temporal diagram convolutional network, and constructing a dynamic digital twin model; s5, designing a competition and cooperation mechanism between agents by using a game theory model, solving resource allocation conflicts through Nash equilibrium, and dynamically optimizing resource scheduling; s6, optimizing a scheduling strategy of the intelligent agent through a distributed algorithm based on near-end strategy optimization; s7, detecting errors in digital-to-analog conversion and scheduling processes by using a belief propagation algorithm; and S8, optimizing a scheduling strategy through a distributed adaptive feedback control mechanism. According to the invention, a distributed computing architecture, space-time diagram convolution, an intelligent scheduling technology and the like are adopted, and digital-to-analog conversion and resource management of the construction site are optimized.

The invention provides a decision-making large model-oriented multi-level heterogeneous memory collaborative scheduling method, which comprises the following steps of: acquiring hardware configuration information and initial state information of a heterogeneous memory system, constructing a burst memory access model based on an Erlang distribution family, and acquiring dynamic memory access characteristics in combination with self-adaptive stratified sampling; representing the state of the memory system by adopting multi-dimensional efficiency measurement and a hierarchical affinity matrix; predicting load characteristics based on a generalized extreme value theory, and constructing an augmented Lagrange optimization model to generate a resource allocation scheme; formulating a data migration strategy through a Pareto optimization algorithm; and dynamic parameter adjustment of Thompson sampling and an exception handling mechanism based on a combined early warning index are introduced. According to the method, memory access features can be accurately described, resource allocation is optimized, migration overhead is reduced, and large decision model training efficiency is improved.

The invention discloses a multi-warehouse intelligent distribution and scheduling method for raw material inventory. The method comprises the following steps: collecting raw material inventory data of each warehouse, and monitoring the inventory data of the warehouses in real time; based on the real-time inventory data and the historical inventory data, the inventory state of each warehouse is analyzed to determine the inventory balance state of each warehouse, and the warehouses with excess or short inventory are identified; and calculating an optimal inventory distribution scheme by adopting a dynamic adjustment algorithm based on an inventory state analysis result. According to the invention, by collecting the real-time inventory data and the historical inventory data, the inventory state of each warehouse is deeply analyzed, the inventory balance state is determined, and the warehouse with excess or short inventory is identified. Through the real-time data monitoring and analysis, the system can provide accurate inventory early warning, help managers to take measures in time, avoid production interruption and resource waste, and improve the accuracy and predictability of inventory management.

The invention relates to the field of medical material management, and discloses a hospital material management server, which comprises a data collection module used for collecting material consumption data, the number of patients and operation plans of all departments from a hospital information system and an electronic medical record system, and transmitting the collected data to a prediction analysis module; the prediction analysis module is connected with the data collection module, and the invention also discloses a hospital material management method which comprises the following steps: S1, collecting material consumption data of each department from a hospital information system and an electronic medical record system through the data collection module; by using an adaptive neural network and a virtual twinning technology, accurate demand prediction, inventory dynamic adjustment, distribution optimization, global monitoring and environment simulation adjustment are realized; the interactive feedback module forms closed-loop optimization, the meta-universe cooperation module supports virtualization operation and training, and the defects of a traditional system in prediction, scheduling, monitoring and cooperation are overcome.

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 an intelligent vehicle dispatching system for logistics transportation, and relates to the technical field of logistics transportation vehicle dispatching. Comprising a data acquisition and preprocessing module, a feature extraction and analysis module, a task classification and complexity evaluation module, a simple task solving module and a complex task dynamic solving module, and the data acquisition and preprocessing module firstly acquires real-time data information related to a current scheduling task and preprocesses the acquired data information; by introducing real-time data preprocessing, feature extraction and intelligent evaluation, the system can dynamically identify task complexity and reasonably distribute solving time, simple tasks can be rapidly solved, solving time of complex tasks can be prolonged, stable operation of the system is ensured, and logistics efficiency and service quality are improved.

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 unmanned aerial vehicle intrusion detection, in particular to an unmanned aerial vehicle intrusion detection system based on personnel intrusion alarm, which comprises a user authentication module, an area setting module, an unmanned aerial vehicle control module, a shooting control module, an intrusion trend judgment module, an intrusion identification module, a display terminal, a server and a cloud database. According to the invention, a user can flexibly set a monitoring area after authentication, generates an unmanned aerial vehicle control signal, dispatches an unmanned aerial vehicle in a target aircraft nest base station, accurately determines a dispatchable unmanned aerial vehicle, inputs task data information to the dispatchable unmanned aerial vehicle, photographs personnel in the monitoring area through the dispatchable unmanned aerial vehicle, generates an image set, and sends the image set to the target aircraft nest base station. And then an intrusion trend value is calculated according to the image sets to determine whether a face recognition instruction is triggered or not, and identity recognition judgment is performed according to the face recognition instruction to determine whether an alarm signal is generated or not, so that omnibearing and high-efficiency monitoring of a monitoring area is realized, and the accuracy and reliability of safety monitoring are remarkably improved.

The invention relates to the technical field of intelligent buildings and elevators, and discloses an intelligent elevator passenger flow analysis and optimization system which comprises a flow monitoring module used for monitoring the passenger flow of an elevator waiting area in real time and obtaining passenger flow data; the data processing module is used for analyzing the passenger flow data and predicting the passenger flow in a future time period; the elevator dispatching module optimizes the dispatching strategy of the elevator according to the prediction result of the data processing module, and improves the efficiency of passengers to get on and off the elevator; and the energy efficiency optimization module adjusts the acceleration and deceleration process of the elevator according to the running state of the elevator so as to reduce the energy consumption of the elevator. By monitoring the passenger flow in the elevator waiting area in real time and conducting accurate analysis, the passenger flow in the future time period can be predicted, the elevator dispatching strategy is optimized, the waiting time of passengers is shortened, the waiting efficiency is improved, and intelligent dispatching is conducted on the elevator based on the shortest waiting time and the minimum energy consumption target.

The invention provides a hospital operation monitoring analysis method and platform based on artificial intelligence, and the method comprises the steps: collecting data from a plurality of data sources of a hospital, and integrating the multi-source heterogeneous data into a unified data platform through a data access gateway; performing cleaning, standardization and feature extraction on the acquired data, and extracting key features by using automatic feature engineering; constructing an AI model according to a hospital operation scene, and training and verifying the model by using historical data; inputting the real-time data into an AI model for analysis, generating a prediction result or a classification label, and performing real-time early warning on an abnormal condition; key indexes and analysis results are displayed through a visual instrument panel, and an operation report is automatically generated. Through the artificial intelligence technology platform, hospital resource use conditions can be analyzed in real time, resource allocation can be dynamically optimized, medical staff scheduling and equipment use plans can be automatically adjusted, waiting time of patients is shortened, and scheduling efficiency and response speed are remarkably improved.

The invention relates to the technical field of micro switch production line intelligent scheduling methods, in particular to a micro switch production line intelligent scheduling method and system based on a graph neural network, and the method comprises the steps: obtaining an attribute set of nodes, and constructing a node graph of a production line; the dynamic representation module is used for dynamically representing real-time states of nodes; generating a multi-scale state prediction model based on the aggregation weight matrix, and obtaining a state prediction matrix for predicting the state change trend of the equipment; based on the state prediction matrix, constructing a nonlinear global optimization scheduling strategy, and further optimizing task allocation and resource allocation in the production line to obtain a global optimal scheduling scheme; constructing a layered feedback correction mechanism based on the global optimal scheduling scheme; based on a hierarchical feedback correction mechanism, an intelligent maintenance trigger signal is generated for identifying potential faults and triggering preventive maintenance, deep modeling and state prediction are performed on each node of the production line, and efficient task scheduling and equipment maintenance management are realized.

A public test resource scheduling method based on multi-level random characteristic particle swarm optimization comprises the steps of construction of a task scheduling model and a resource constraint model, design of a multi-level optimization structure, definition of a task dependency relationship and formulation of a resource allocation rule, and meanwhile an optimization algorithm used for public test resource scheduling is designed. According to the method, the efficient scheduling strategy is constructed by designing the multi-level random characteristic particle swarm optimization algorithm, reasonable allocation of resources in multi-task scheduling is achieved, and the project construction period is effectively shortened. Compared with a traditional method, the method has more advantages in the aspects of solving efficiency and scheduling quality, the total project construction period can be shortened, the resource utilization rate can be increased, and the method is very suitable for a complex multi-skill public measurement resource-limited project scheduling scene.

The invention relates to the technical field of loading building control, and discloses a loading building automatic operation control method and system. The method comprises the following steps: analyzing and generating a vibration characteristic curve of a loading mechanism based on operation state data, and calculating transmission control parameters through wavelet transform; processing the loading task list to obtain a job scheduling sequence, and calculating and conveying partition data according to dynamic planning; conveying partition data are calculated in real time to obtain feeding mechanism operation speed and conveying belt speed parameters, and an execution instruction set is output after verification; the evaluation execution instruction set calculates conveying mechanism load, loading platform state and material accumulation data, and generates a control instruction after grading processing; and acquiring control instruction execution data to obtain loading time, loading mechanism energy consumption and equipment utilization rate data, and determining a target control strategy after correlation analysis. Accurate control over the automatic operation process of the loading building is achieved, and therefore the stability and safety of the loading process are guaranteed.

The invention discloses a coordinated scheduling method and device for a mixed hydrogen-electricity combined system, and electronic equipment, and belongs to the technical field of electrical engineering, and the method comprises the steps: reconstructing a dynamic response process model of a hydrogen-mixed natural gas network into a linearization matrix adaptive to optimal scheduling; the response conditions of an electric power system and a natural gas system under the action of various flexible resources such as an electric hydrogen production device, a hydrogen-doped gas turbine and pipeline gas storage after a disturbance event can be accurately reflected; taking the linearization matrix as a hydrogen-mixed natural gas network security constraint, and constructing a double-layer optimization model considering different benefit subjects in the joint system; reconstructing the double-layer optimization model into a single-layer optimization problem and solving the single-layer optimization problem to realize scheduling; coordinated operation of an electric power system operator and a natural gas system operator is guided through two price signals, excitation compatibility and high dispatching efficiency are achieved on the economic benefit level, wind and light abandoning is reduced on the operation safety level, power load supply is guaranteed, and meanwhile gas network blocking is relieved.

The invention relates to the technical field of electric vehicle cluster scheduling, and discloses an electric vehicle cluster scheduling strategy and system based on deep reinforcement learning, and the method comprises the steps: determining an electric vehicle cluster scheduling range based on the charging willingness of a user; converting a cluster scheduling problem into a Markov process to obtain a conversion result; and designing a reward function for each charging station, and solving through a deep enhancement algorithm to obtain a final electric vehicle cluster scheduling strategy. According to the method, the load peak-valley difference and peak regulation cost of the urban power grid system are converted into an optimization problem by using deep reinforcement learning, so that the load pressure of the urban power grid system can be reduced, and normal and safe operation of a power distribution line is ensured; and the scheduling efficiency can be improved by considering the number of the electric vehicles needing to be scheduled instead of charging scheduling of all the electric vehicles in a city range.

The invention provides a shared vehicle scheduling method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining current vehicle order data of each station in a target area and current environment information of the target area; inputting the current vehicle order data and the current environment information of each station into a pre-trained prediction model to obtain vehicle order prediction data, output by the prediction model, of each station; determining a plurality of associated stations from the stations, and integrating the vehicle order prediction data of the stations to obtain integrated vehicle order prediction data; according to the current vehicle number and the current vehicle reservation data of each target station and the integrated vehicle order prediction data, calculating the vehicle missing number or the vehicle stacking number of each target station; and determining a vehicle scheduling scheme of the target area according to the historical operation and maintenance scheduling data of the target area and the number of missing vehicles or the number of stacked vehicles of each target station. The efficiency of shared vehicle scheduling is improved, and the real-time performance and the flexibility are high.

The invention belongs to the technical field of ship scheduling, and provides an intelligent scheduling method, system and device for port ship berthing and departing, and a medium, and the method comprises the steps: obtaining key parameter information related to port ship berthing and departing; determining a plurality of state vectors of the port in a preset time period based on the key parameter information, and establishing an initial scheduling model according to the plurality of state vectors; configuring action space data for the initial scheduling model, determining a berthing duration award value, an equipment utilization rate award value, a work waiting duration penalty value, a work completion duration award value and an environment safety award value after the port executes the action space data according to the initial scheduling model, and calculating to obtain a comprehensive award value; determining a loss function value based on the comprehensive reward value, and optimizing the initial scheduling model to obtain an intelligent scheduling model; and according to the intelligent scheduling model, carrying out intelligent scheduling on port ship berthing and departing. According to the scheme provided by the invention, the scheduling accuracy and safety of berthing and departing of the port ship are improved.

The invention relates to a dispatching method and device for distributed grid connection of an electric energy storage system group and electronic equipment, and belongs to the technical field of electric power grids, and the method comprises the steps: constructing an objective function and a constraint condition of each electric energy storage system in the electric energy storage system group, the objective functions comprise a first objective function constructed with the maximum benefit of the electric energy storage system, a second objective function constructed with the minimum peak-valley difference, a third objective function constructed with the minimum load standard deviation and a fourth objective function constructed with the minimum voltage amplitude deviation; selecting a global search operator in the global search operator pool, and selecting a local search operator in the local search operator pool; obtaining an update operator based on the global search operator and the local search operator; and selecting operators in the updated operators based on a preset deep reinforcement learning algorithm, and solving the target function under constraint conditions to obtain a scheduling result of distributed grid connection of the electric energy storage system group. The scheduling efficiency of the integrated electric energy storage system is improved.

The present invention relates to a multi-ship anchoring scheduling path planning method based on an improved RRT algorithm, comprising the following steps: S1, constructing a multi-ship anchoring scheduling destination selection mechanism; S2, constructing a multi-ship anchoring scheduling evaluation index selection mechanism; S3, calculating the ship scheduling priority according to the multi-ship anchoring scheduling evaluation index; S4, constructing a multi-ship anchoring scheduling collision avoidance decision-making mechanism; S5, calculating the multi-ship anchoring scheduling path planning result. The present invention can be used to deal with the situation where multiple ships need anchoring scheduling at the same time, optimize their berthing sequence and navigation path, thereby minimizing the ship anchoring scheduling waiting time, reducing the navigation distance of all scheduled ships as much as possible, and improving the berth utilization rate; it has important significance in multi-ship scheduling in complex waters, emergency evacuation of ships, and comprehensive management of water transportation hubs.

The invention relates to a ship gear identification method and device based on target tracking and re-identification, and a medium. The method comprises the following steps: obtaining ship information; target identification is carried out based on YOLO5s introduced with an extended Kalman filtering algorithm, a ship trajectory is tracked from ship entry, and a ship detection frame of a trajectory tracking result and ship basic information are superposed on a ship real-time video; if the ship trajectory is lost due to shielding in the ship trajectory tracking process, determining a lost target trajectory based on a Deep OC-SORT algorithm and a Redis re-recognition technology; in the ship trajectory tracking process, if it is recognized that the ship does not move for continuous preset time, it is judged that ship gear stopping is completed, the ship stopping position is obtained, and a ship actual gear diagram is formed; and judging whether the ship is scheduled according to the ship scheduling graph, the actual scheduling graph and the basic information of the ship, and if the ship is not scheduled, giving an alarm. Compared with the prior art, the method has the advantages that accurate positioning of the ship position can be achieved, and the like.

The invention provides a dynamic flexible workshop multi-target online rescheduling method based on hierarchical MADDQN driving, aiming at the problem of dynamic multi-target rescheduling in actual production. According to the method, two performance indexes, namely the minimum total tardiness and the maximum machine utilization rate, are optimized. The MADDQN comprises two DDQN agents, and the DDQN of the higher level is a controller for determining the temporary optimization target of the DDQN of the lower level. At each rescheduling point, the high-level DDQN considers a target which needs to emphasize optimization at present, the low-level DDQN considers process machine selection under the current optimization target, and the two intelligent agents cooperate with each other to carry out iterative training. Compared with other rules-based algorithms, the multi-target dynamic scheduling method has certain advantages in each performance index when processing a multi-target dynamic scheduling problem, and still has good performance when solving a large-scale problem.