801results about How to "Improve scheduling efficiency" patented technology

The invention relates to the technical field of power systems, in particular to a substation main equipment self-checking system based on a multi-source heterogeneous data fusion technology. The self-inspection system comprises a data acquisition and preprocessing module, a self-adaptive data fusion algorithm module, an equipment fault mode dynamic identification and intelligent repair module, a multi-scale time sequence prediction and emergency plan generation module, an equipment health state evaluation and residual life prediction module, and a fault diagnosis and knowledge base updating module. A user interaction and remote monitoring management module, and an intelligent prediction and optimization scheduling module; according to the substation main equipment self-checking system based on the multi-source heterogeneous data fusion technology, comprehensive monitoring and intelligent management of substation equipment are achieved by integrating various data acquisition and processing technologies.

The invention relates to the technical field of electric power design, in particular to an intelligent dispatching system of a transformer area power supply grid. According to the method, a self-adaptive sampling strategy of real-time load fluctuation and distributed energy output characteristics is adopted; when the system detects the rapid change or abnormal fluctuation of the load, the data acquisition frequency is automatically improved, so that the capture of key information is ensured; when the load is stable, the sampling frequency is reduced, and the collection of redundant data is reduced; adaptive sampling can dynamically adjust the acquisition frequency, it is guaranteed that important data are not lost at the critical moment, and meanwhile the system burden is reduced; a hierarchical data processing architecture is adopted, data processing is divided into an edge layer and a cloud layer, the edge layer is responsible for conducting primary processing on data collected in a transformer area, including denoising, feature extraction and the like, and the data quality is guaranteed; only the key data subjected to primary processing is uploaded to a cloud end for data analysis and decision optimization; therefore, the burden of data transmission and processing is reduced, and the real-time response capability of the scheduling system is improved.

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 relates to the field of data visualization, in particular to a visualization method and system based on a vehicle service supply chain. The method comprises the following steps: acquiring whole-process information of a vehicle service supply chain; carrying out supply chain main body identification on the whole flow information of the vehicle service supply chain, carrying out spatial topology distribution analysis, and constructing a supply chain main body topology distribution map; obtaining supply chain main body interaction logic data based on the plurality of supply chain main bodies; performing topology interaction logic connection on the supply chain main body topology distribution graph by utilizing supply chain main body interaction logic data, and constructing a supply chain topology interaction network; performing intra-period path transportation frequency calculation on the whole-process information of the vehicle service supply chain to generate logistics demand trend characteristics; and performing operation behavior analysis of each link on the whole-process information of the vehicle service supply chain to obtain the capacity characteristics of each link. According to the invention, through visualizing the vehicle service supply chain, the resource scheduling efficiency and flexibility of the supply chain are improved.

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 smart warehouse material sorting two-stage scheduling method based on an embedded neighborhood search strategy genetic algorithm, relates to the field of smart logistics scheduling, and is used for solving the problem of warehouse material sorting scheduling in enterprise production logistics links. The method comprises the following steps: under the condition of considering the heterogeneity characteristics of a production line, establishing an order task batching and batching path optimization two-stage optimization model taking the minimum total cost of order picking order delay as a target function, calling a channel type storage picking path strategy, and providing four neighborhood search strategies, and a traditional genetic algorithm is improved by embedding a neighborhood search strategy to solve problems. According to the method, to-be-sorted order batching and batch grouping path planning automation is realized, so that the total order sorting delay cost is minimized, and the problem of'order batching-path planning 'two-stage scheduling of storage sorting in the production material supply process of an enterprise can be solved, so that the comprehensive benefits of production logistics are improved, and the purposes of reducing cost and improving efficiency are achieved.

The invention discloses an in-tunnel construction vehicle meeting scheduling system based on positioning equipment. The system comprises a basic service module which is used for storing basic data for business service analysis in an acquisition and collection mode; the business service module is used for carrying out detailed analysis of the corresponding service through the data acquired by the basic service, and providing an analysis result to the display end for display; and the display end module is used for displaying the analysis result of the business service module, wherein the display modes comprise a mobile phone APP, a webpage large screen, an LED panel, a tunnel loudspeaker and a tunnel traffic light. The system carries out weight grading on vehicle and personnel positioning labels by standardizing a tunnel construction process, and integrates data of avoidance points of a transverse tunnel. And in combination with an intelligent technology, efficient vehicle meeting scheduling, intelligent construction path planning and avoidance point path planning of the construction area are realized.

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 a multi-dimensional data-driven urban traffic scheduling method and platform, and relates to the technical field of traffic scheduling, and the method comprises the steps: obtaining the distribution information of urban traffic facilities, and determining a traffic key region set; a fixed source detection device is arranged, and a key area multi-dimensional traffic operation data flow set and a road traffic multi-dimensional real-time data flow are obtained; acquiring a traffic operation associated index set, and performing preprocessing and index feature extraction; performing congestion prediction analysis to obtain urban traffic congestion parameters, performing scheduling analysis on the urban traffic congestion parameters, and determining a traffic scheduling strategy scheme set; scheduling simulation and effect comparison optimization are carried out, and traffic scheduling optimization control is carried out. The technical problems of insufficient traffic scheduling and low operation efficiency in the prior art are solved, and the technical effects of accurately mastering the traffic condition and improving the traffic scheduling and operation efficiency are achieved.

The invention discloses a self-adaptive resource optimization and model generalization method in an end-side cloud collaborative intelligent system, and the method comprises the following steps: end equipment collects corresponding environment data and equipment operation parameters, and carries out the training of a preset local model based on the collected data; the method comprises the following steps: constructing a digital twinborn model, calculating a statistical distance between a real-time data stream monitored in real time and a preset data distribution model based on the real-time data stream monitored in real time by the digital twinborn model, and if the statistical distance is greater than a preset value, starting an active sample selection mechanism to optimize and update a local model. The optimized and updated local model is sent to the edge node; a local model sent by the end equipment and the edge node is compressed by adopting a bidirectional knowledge distillation mechanism, a lightweight model is obtained, and global optimization and fusion are carried out through a cloud server. According to the method, resource allocation can be optimized, and the model generalization ability is improved, so that the data processing efficiency of the system, the fairness of resource allocation and the accuracy of intelligent decision making are improved.

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 strategy generation method for satellite ground station resource scheduling, and relates to the technical field of spaceflight. Measurement and control tasks of multiple satellites are optimized and scheduled through the improved particle swarm optimization, and an optimal scheduling scheme is generated according to factors such as satellite states, measurement and control resources and task priorities; by monitoring satellite and ground station states and environment changes in real time, a scheduling strategy is dynamically adjusted in combination with an intelligent algorithm and historical experience data, and manual intervention and revision of the strategy are supported at the same time, so that the scheduling scheme has relatively high flexibility and adaptability; the distribution problem of measurement and control resources is converted into a multi-objective optimization problem; and an editable and extensible data reading module is designed, so that a data access mode can be flexibly selected. The automation level of satellite measurement and control scheduling can be effectively improved, and the error rate is reduced; the method has a wide application prospect in multi-satellite networking measurement and control scheduling, and has remarkable technical advantages and economic benefits.

The invention discloses an intelligent self-adaptive real-time task scheduling method and system based on multi-dimensional optimization. The method comprises the following steps: S1, inputting a task; s2, a DAG task is decomposed into independent sequential sub-tasks; s3, dynamically calculating the priorities of the tasks by adopting a multi-factor dynamic priority scheduling module, and updating the priorities of the tasks; s4, dynamically adjusting an acceleration factor by adopting a self-adaptive acceleration factor management module; s5, vertex correlation analysis is carried out, and a variance of processor load imbalance is calculated by adopting a load balanced distribution algorithm and is minimized; s6, in combination with static analysis and dynamic inspection, a hybrid schedulability judgment module is adopted to judge the tasks, and judgment results are fused; and S7, fusing decisions by adopting a decision fusion algorithm, obtaining a scheduling strategy through a scheduling strategy coordinator, executing the task, and monitoring the task in real time. Real-time monitoring and real-time optimization scheduling are realized, and the utilization rate of resources is improved.

The invention discloses a multi-energy system optimization scheduling method based on a carbon flow theory and deep reinforcement learning. The method comprises the following steps: firstly, carrying out mathematical description on a multi-energy system dynamic optimization problem based on a carbon emission flow theory, and establishing an objective function and constraint conditions of multi-energy system optimization scheduling; then, the scheduling decision problem is expressed as a Markov decision process framework, a reinforcement learning environment is created, and a state space, an action space and a reward function of the multi-energy system are defined; meanwhile, in the depth deterministic strategy gradient algorithm, the training efficiency is improved by introducing an experience replay mechanism based on proportion priority, the overfitting risk in the network training process is reduced by adopting an L2 regularization measure, and the generalization ability of the network in the optimization scheduling decision of the multi-energy system is improved. The method can respond to random fluctuation of a source side and a load side in real time, outputs a coordinated scheduling strategy, ensures low-carbon operation of a multi-energy system, and overcomes the defect that a traditional energy scheduling system cannot quickly adapt to changes.

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 freight scheduling, in particular to a freight scheduling information management method and system and a storage medium. The method comprises the following steps that uncompleted order collection is carried out on a freight platform to obtain an uncompleted order set, and the uncompleted order set comprises a plurality of uncompleted orders; each uncompleted order at least comprises an order ID, order release time, target cargo attribute data, publisher identity data, a departure place, a destination, an order state identifier, an order transportation task schedule, cargo stacking attributes, predicted delivery time and cargo expected arrival time; and performing order state classification and marking on the uncompleted order set to obtain a classified order set. By optimizing the loading space utilization rate of the vehicle, the unloaded and half-loaded conditions of the vehicle can be reduced, so that the fuel consumption and the transportation cost are reduced.

The invention provides a revenue distribution method for an internal alliance cooperative game of a virtual power plant, and the method comprises the steps: forming an alliance by distributed energy resources, and constructing a multi-agent optimization scheduling model in the virtual power plant with the maximized electricity selling revenue; constructing constraint conditions of a multi-body optimization scheduling model in the virtual power plant; determining a multi-agent cooperative game model of the game participants, the strategy set and the feature function; determining the overall electricity selling income of the virtual power plant, and designing a two-stage refined income distribution scheme of a multi-factor improved shapley value method and a kernel method based on odd-even circulation; and calling a CLPEX solver of the YALMIP on a Matlab platform to solve the cooperative game scheduling model and the income distribution strategy. According to the method, cooperative complementary operation among the alliances in the virtual power plant is realized, and fairness and rationality of income distribution among multiple subjects are ensured.

The invention discloses a networked measurement and control data transmission task scheduling method for a giant low-orbit satellite constellation, and solves the problem of low completion rate of measurement and control data transmission tasks caused by incapability of global station distribution in the prior art. Obtaining a resource attribute, a ground station state and a low earth orbit satellite state by using the task layer, the resource layer, the equipment state and the constellation state, performing data conversion to obtain input data, inputting the input data into the trained multi-agent network to obtain an action corresponding to the current time slot, and determining an entry low earth orbit satellite and an associated ground station according to the action; obtaining a routing path between the entry low earth orbit satellite and the task low earth orbit satellite according to the inter-satellite shortest path routing table, and determining a scheduling sequence according to the routing path and the associated ground station; the mutual influence between global tasks and resources is considered, the effectiveness of decision making during execution is reduced, and meanwhile, the scheduling efficiency of measurement and control data transmission tasks in a giant low-orbit satellite constellation scene is improved.

The invention discloses a self-adaptive resource scheduling method for improving cluster reconstruction efficiency, and relates to the field of resource scheduling distribution, and the method comprises the steps: monitoring the resource use data of each node in a cluster in real time through a monitoring agent, and obtaining the health state of the node through a heartbeat mechanism; collecting input task requests, predicting the resource demand quantity of each task, and performing preliminary queue processing on the tasks to obtain a task queue group; setting a resource competition constraint according to the task queue group and the resource use data and the health state of each node, obtaining dynamic resource scheduling corresponding to each task by using an optimization algorithm, and allocating the tasks to each node; a resource scheduling dynamic graph is constructed, a visual interface is set, and the resource scheduling dynamic graph is arranged on the visual interface and used for displaying resource scheduling, providing a user-friendly interface and realizing high-efficiency scheduling of adaptive resources in a cluster reconstruction process.

The invention belongs to the technical field of power grid dispatching, and discloses a power distribution network fault emergency recovery method considering ECV-MES active regulation, and the method comprises the steps: building a power distribution network line fault probability model under a typhoon disaster condition; based on a power-traffic coupling network, determining a pre-disasters ECV-MES pre-deployment position by optimizing an ECV-MES scheduling distance and response time; constructing a post-disaster communication system recovery model by taking the minimum communication failure cost of the power distribution network and the shortest first-aid repair duration for realizing emergency communication as targets; and constructing a post-disaster power system recovery model by considering the importance level of the load, and solving the model to obtain an optimal power-communication coordinated scheduling scheme. The method can ensure that the power distribution network reliably and quickly recovers the normal operation state, and provides reference for improving the anti-disaster capability and the operation efficiency of the power distribution network.

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 discloses a flood season ship scheduling optimization method and system based on channel-ship lock coordination, and belongs to the technical field of water transportation traffic intelligent management, and the method comprises the steps: obtaining ship information, ship lock attribute information and flood season channel navigation requirement information of a water transportation junction; taking minimization of the total waiting time of the ship in the anchorage ground as an optimization target, constructing a ship scheduling optimization model, and establishing constraint conditions satisfied by ship lockage scheduling in the flood season; solving the ship scheduling optimization model based on a dynamic multi-population particle swarm optimization algorithm of recombination learning and mixed mutation to obtain an optimized flood season ship scheduling scheme; estimating the carbon emission of the ship, and verifying the low carbon and sustainability of the ship scheduling scheme in the flood season. According to the method, the ship off-anchor time and channel-ship lock coordination decision problem can be solved, the defect of shortage of a flood season scheduling method is overcome, and an effective reference is provided for reducing the ship navigation risk and improving the ship scheduling efficiency.

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 provides a production scheduling method of multi-cavity etching equipment and related equipment. The method comprises the following steps: acquiring wafer batches in an etching parallel processing mode, available machine information and an available processing path set of each machine for the wafer batches of each process type; obtaining a process type transfer matrix; according to the available machine information, an etching scheduling task is generated for the wafer batch arriving in the planned period, and the etching scheduling task comprises a first-stage machine scheduling task and a second-stage machine internal cavity scheduling task; in the first stage, a distribution strategy for machine scheduling is created, and in the second stage, a sub-batch fragmentation strategy of each wafer batch in a cavity is created; and when the task execution time reaches the triggering time of any adjustment task, creating a non-processing state instruction of the cavity, and alternately executing the triggered adjustment tasks in sequence. According to the method, a two-stage allocation scheduling production scheduling algorithm is used, and the dual scheduling task of wafer batches on the machine and cavity level is synchronously realized.

The invention relates to the technical field of hydraulic engineering operation prediction, in particular to a hydraulic engineering operation trend prediction method and system. The method comprises the following steps: acquiring multi-source hydraulic engineering data, and generating a hydraulic engineering three-dimensional model through multi-source space fusion processing and three-dimensional model simulation; performing geometric complexity identification and stress path analysis on the three-dimensional model to determine key nodes in the water conservancy project; historical water conservancy data are obtained, time sequence conversion and potential mode mining are carried out, and potential modes related to engineering are obtained; performing stress superposition comparison on the key nodes of the water conservancy project and the potential mode to generate stress superposition comparison data, and iteratively adjusting the key nodes according to a result to obtain adjusted stress nodes; constructing a prediction model based on the adjusted stress node and the potential mode, performing operation trend prediction, and generating an operation trend prediction result of the water conservancy project; the high-precision and high-adaptability water conservancy project operation trend prediction method is realized.