176 results about "Integer programming model" patented technology

The invention relates to the technical field of atmospheric dust pollution prevention and environmental protection, and discloses a multi-objective optimization layout method for dust monitoring points of a bulk cargo port, which comprises the following steps: processing historical meteorological data and port geographic information, and calculating the comprehensive evaluation concentration of each candidate grid point in combination with a pollutant diffusion model; constructing a multi-target nonlinear mixed integer programming model taking maximization of environment sensitive point coverage and minimization of concentration interpolation deviation as target functions, wherein the model comprehensively considers multiple reality constraints; solving the model by adopting an improved genetic algorithm to obtain an optimal solution set capable of forming tradeoff among different targets; and carrying out quantitative evaluation on the solution set through preset monitoring area coverage rate and monitoring accuracy indexes, and determining an optimal monitoring point position layout scheme. According to the invention, a scientific and quantitative optimized layout scheme can be provided for bulk cargo port dust monitoring, and the reliability and spatial representativeness of a monitoring network are improved.

The invention discloses a high-temperature environment multimodal transport method and system based on composite heat insulation and active temperature control. The method comprises the steps that a multimodal transport capacity grid model simulating high-temperature transport cooperation is constructed; determining model condition hypothesis, and defining decision variables, parameters and state variables of the model; high-temperature special items and safety penalty are included in the total cost, a high-temperature associated total cost model is constructed, and a multi-scene cost calculation formula is deduced; with the purpose of minimizing the total cost, a mixed integer programming model including path connection and flow conservation, temperature constraint, refrigeration / heat dissipation energy and power constraint, node refrigeration / sunshade capacity constraint, battery and key component temperature safety constraint, butt joint / node capacity constraint and time window constraint is constructed; solving the model by adopting an improved hybrid immunity-particle swarm dual-optimization algorithm, and outputting an optimal transportation path, transportation mode, transfer node and high-temperature scene exclusive scheduling parameter; and collaborative optimization of high-temperature transportation safety, energy efficiency and economy can be realized.

The invention provides a truck-unmanned aerial vehicle cooperative dynamic scheduling method and system in emergency logistics, and relates to the technical field of unmanned aerial vehicle intelligent scheduling. Constructing a truck-unmanned aerial vehicle cooperative distribution network, wherein each truck is equipped with a cooperative unit comprising a large unmanned aerial vehicle and a small unmanned aerial vehicle; the network comprises warehouses, hub points and demand points; establishing a mixed integer programming model by taking minimization of the total operation cost as a target and taking electric quantity management, service distribution, a time window and collaborative feasibility as constraints; and solving the model by adopting a randomized greedy algorithm, and synchronously deciding a truck path and an unmanned aerial vehicle task in single iteration to obtain a truck path scheme, an unmanned aerial vehicle service distribution scheme and a dynamic electric quantity scheduling scheme. Therefore, the cost optimization and dynamic coordination of emergency logistics scheduling are realized, and the efficiency and feasibility of emergency distribution are effectively improved.

This disclosure proposes a shift-based employee scheduling method and related apparatus. The shift-based employee scheduling method includes: acquiring task information and inter-task conflict information; acquiring employee information; acquiring shift information; constructing an integer programming model by setting constraints and an objective function, the constraints including a first constraint requiring that conflicting tasks assigned to each shift can be executed, the objective function being based on multiple objectives; solving the integer programming model using the acquired information to determine the correspondence between employees and shifts and between tasks and shifts, and decoding the correspondence between employees and tasks from the employee-shift and task-shift correspondence information, thereby scheduling employees.

The invention relates to the technical field of intelligent traffic and smart city waste management, and discloses a path planning method and system for a garbage transport vehicle based on multi-source data, and the method comprises the steps: generating a reference path based on historical collection and transportation data, the method comprises the following steps: performing data cleaning on pre-acquired garbage quantity data, vehicle state data, real-time traffic data, hydroelectric data and fuel consumption data, performing multi-source time sequence feature fusion anomaly detection on a cleaned data set, performing trajectory inversion on the cleaned data set, performing target constraint through a mixed integer programming model, and performing multi-source time sequence feature fusion anomaly detection on the cleaned data set. Then gradient descent parameter optimization is carried out on the initial optimization model, elastic resource scheduling is carried out on an optimized path scheme based on a large event plan library, a path instruction set is generated, dynamic re-optimization evaluation is carried out on the path instruction set based on garbage amount change and traffic condition data fed back in real time, and an updated path is generated; according to the invention, the path planning efficiency of the garbage transport vehicle based on the multi-source data can be improved.

The invention discloses an alarm processing method, a related device and a system, and relates to the technical field of data processing, a mixed integer programming model is constructed by adopting a mixed integer programming modeling technology, priority information of alarm information is calculated in real time based on multi-dimensional alarm feature data, sorting is performed according to the priority information, and the alarm information is processed according to the priority information. The alarm processing sequence of each piece of alarm information in the to-be-processed alarm information set is calculated in real time, and the alarm processing sequence is determined based on the multi-dimensional alarm feature data acquired in real time, so that the accuracy of the alarm processing sequence can be effectively improved; moreover, the mixed integer programming model is adopted to realize discrimination and screening work of the alarm information, so that limited operation and maintenance resources can be quickly and preferentially allocated to the alarm information with high priority; the alarm processing requirement that alarm information which is related to the core fault and needs to be processed preferentially is quickly and accurately screened out from massive alarms and processed is met.

The invention discloses a message issuing speed control method and system based on an AI large model. The method comprises the steps of collecting multi-source data in real time and constructing a multi-dimensional fusion feature vector; based on the BERT-CNN distillation architecture, detecting abnormity and executing hierarchical fusing; based on the fusion feature vector, using a reinforcement learning model to predict message issuing rates of a plurality of time windows in the future and dynamically adjusting priorities; in the offline stage, a mixed integer programming model is adopted to generate minimum cloud resource cost, and in the online stage, a deep reinforcement learning model is adopted to carry out real-time resource fine tuning; based on the node health degree, calculating a traffic allocation weight through a smooth weighted polling algorithm so as to carry out load balancing; different business scenes are adapted through domain adversarial training and multi-task learning, and different languages are adapted through multi-language BERT coding. The message rate is accurately controlled to improve the system stability and the resource utilization rate; low-cost scheduling fine tuning of cloud resources is realized; various business scenes and languages can be quickly adapted, and the development cost and time are reduced.

The invention discloses an electric power and carbon management collaborative optimization method and system for a multi-industry symbiotic zero-carbon park, and the method comprises the steps: constructing a mixed integer programming model which comprises an electric power dispatching sub-model, a carbon collection sub-model and a source-sink matching sub-model, and defining a coupling relation between an electric power dispatching variable and a carbon management variable; designing a multi-objective optimization function, and bringing coal consumption cost, start-stop cost, carbon transaction cost, wind curtailment penalty cost, deep peak regulation cost, peak regulation compensation benefit and transportation cost into a unified optimization framework; and setting time-space coordination constraint conditions, including power balance constraint, output upper and lower limit constraint, climbing rate constraint, capture-transport volume balance constraint and carbon sink capacity limitation constraint, to realize coordination optimization of power production and carbon logistics in time and space dimensions. According to the method, the total cost of the system is effectively reduced, the utilization efficiency of carbon sink resources is improved, real-time controllability of carbon emission intensity is ensured, and the problem of global optimum deficiency caused by a traditional two-stage optimization method is solved.

The invention discloses a federal edge communication and calculation optimization method, system and device based on task prediction and a medium, and belongs to the technical field of edge intelligent collaborative optimization, and the method comprises the steps: collecting historical task data, carrying out the modeling of a communication and calculation process, building a multi-dimensional task feature modeling mechanism, extracting the heterogeneous features of the communication and calculation process, and carrying out the calculation of the communication and calculation process. Performing task demand prediction and load perception to obtain a prediction result; and establishing an integer programming model, performing approximate solution through a heuristic algorithm to complete service cache optimization, performing calculation unloading and resource joint allocation, calculating key performance indexes, performing periodic acquisition, and performing dynamic adjustment on a prediction result. According to the method, an efficient cache strategy is generated by adopting a heuristic algorithm, the defects of single resource allocation, decision lag and lack of global coordination in a traditional method are overcome through joint optimization of resource allocation and a real-time performance monitoring feedback mechanism, and the system response efficiency and the resource utilization rate are improved while the task success rate and reliability are guaranteed.

The invention discloses a highway-railway combined transportation scheduling method and system based on digital twinning and multi-agent cooperation. The method comprises the following steps: establishing a unique binding relationship of people, vehicles and goods in a transportation order based on an RFID binding algorithm, and synchronizing the unique binding relationship to a database; collecting railway transportation state data, road transportation state data and freight station operation data in real time; standardizing the railway transportation state data, the road transportation state data and the freight station operation data through a multi-source data fusion center; the connection time, the route and the parking position of the road vehicle are dynamically adjusted based on the LSTM collaborative scheduling model, and a road vehicle diversion and waiting duration estimation scheme under the emergency situation is output; railway carriage demands are predicted through LSTM, dynamic optimal matching of road trucks and railway carriages is dynamically matched based on a mixed integer programming model, an optimal matching scheme of the road trucks and the railway carriages is generated, unique binding of people, vehicles and goods is achieved by introducing an RFID technology, information barriers are broken, and real-time sharing and standardized interaction of data are achieved.

The invention provides a central warehouse selection and logistics route joint optimization method, belongs to the technical field of supply chain management and optimization, and can at least partially solve the problem that global optimization cannot be realized due to mutual independent optimization of central warehouse site selection and logistics route planning in the prior art. The method comprises the following steps: acquiring position coordinates of a production base and each warehouse node in a logistics network and distance parameters between the nodes; constructing a decision variable set based on the position information; establishing a constraint condition set including center bin number constraint, route connectivity constraint, jurisdiction uniqueness constraint and subring elimination constraint; constructing a target function by taking the minimization of the total transportation distance as a target; and integrating the decision variable, the constraint condition and the objective function to construct an integer programming model, solving the integer programming model, and outputting a central bin selection scheme and a logistics route planning scheme. According to the invention, joint global optimization of central warehouse site selection and logistics routes is realized, the logistics cost is effectively reduced, and the overall operation efficiency of a supply chain is improved.

The invention discloses an offshore wind power operation and maintenance method considering uncertainty and opportunity collaborative maintenance. The method is used for solving the technical problems that an offshore wind plant is high in operation and maintenance cost and large in uncertainty. The method comprises the following steps: firstly, acquiring wind speed, wave height, electricity price, wind speed and point prediction data thereof, and a prediction value of residual life of a wind turbine generator component; generating a group uncertain scene as the input of a subsequent optimization model through a space-time probability model; constructing a two-stage random mixed integer programming model as an optimization model, and setting decision variables and constraint conditions of the model; and finally, solving the two-stage random mixed integer programming model every day by adopting a rolling time domain optimization algorithm, and outputting a maintenance scheduling plan. The embodiment proves that compared with an existing advanced scheduling strategy, the offshore wind power operation and maintenance economy can be further improved, and the availability and robustness of the scheduling strategy are enhanced.

The invention relates to the technical field of traffic services, in particular to a traffic service transport capacity demand prediction method and system, and the method comprises the steps: collecting historical operation data, track data, passenger flow counting data and external impact factor data for prediction, carrying out the preprocessing of the data, and dividing the data into a plurality of space attributions; constructing a time sequence feature, a periodic feature, a neighbor space feature and an instant available transport capacity feature set external factor feature based on the preprocessed data; generating short-term baseline prediction based on a seasonal autoregressive moving average model, constructing and training a space-time hybrid neural network model to extract spatial correlation and time dynamics of space attribution, outputting a space-time depth prediction result, and integrating the space-time depth prediction result with the short-term baseline prediction to obtain a demand prediction value of a future multi-step time period; and establishing an integer programming model comprising vehicle availability, shift constraints and transfer time constraints and solving the integer programming model to generate vehicle allocation or shift suggestions by taking the transport capacity level as the target and minimizing the scheduling cost.

The invention is suitable for the technical field of intelligent traffic and unmanned aerial vehicle scheduling, and provides an unmanned aerial vehicle highway inspection task scheduling optimization method and system, and the method comprises the following steps: obtaining the dynamic state information and environment constraint information of all available unmanned aerial vehicles in real time, and forming an unmanned aerial vehicle resource state set and an environment constraint set; based on the initial inspection task set, constructing a space-time association diagram of an expressway road network, and reasoning to generate a derivative predictive task to obtain a to-be-scheduled task pool; according to the unmanned aerial vehicle resource state set, the environment constraint set and the to-be-scheduled task pool, a mixed integer programming model is established and solved, and an inspection task plan is allocated to each unmanned aerial vehicle; and monitoring the process of executing the inspection task plan by the unmanned aerial vehicle in real time, generating a predictive secondary task and an execution deviation degree, and judging whether to trigger rescheduling. According to the method, fundamental transformation from passive response to active prediction and from static planning to dynamic adaptation of highway unmanned aerial vehicle inspection is realized.

The present invention belongs to the technical field of industrial wireless networks, and particularly relates to an age-of-information optimization scheduling method for an industrial wireless network and based on a multi-dimensional conflict graph, the method comprising: compiling statistics on age-of-information changes of each data stream in each superframe, and on the basis of an average expected age-of-information calculation formula for the data stream, modeling the problem of minimizing the overall average expected age of information into an integer programming model for time slot and channel allocation; constructing a three-dimensional conflict graph composed of a data-stream–link–time-slot three-dimensional tuple, converting a time-slot scheduling problem into a maximum weight independent set problem, and solving same to obtain a time-slot scheduling strategy; and on the basis of the time-slot scheduling strategy, adding a channel dimension to the data-stream–link–time-slot three-dimensional tuple to form a four-dimensional tuple and constructing a four-dimensional conflict graph, converting a channel allocation problem into a maximum weight independent set problem for solving, and solving same to obtain a channel allocation strategy. The optimization scheduling algorithm used in the present invention can reduce the age of information of a data stream and improve the real-time performance of a network.

The invention provides a multi-vehicle cooperative scheduling method and system for mobile robots, and relates to the technical field of intelligent storage scheduling, and the method comprises the steps: obtaining the real-time states and environment perception data of a plurality of mobile robots, carrying out the fusion processing, and combining with a pre-stored map to generate a global dynamic map; then identifying the key bottleneck region and acquiring feature data of the key bottleneck region, and performing three-dimensional modeling by using digital twinning to generate congestion prediction data; then dynamically distributing network bandwidth according to the congestion prediction data, and planning a conflict-free cooperative motion trajectory for each mobile robot based on the transmitted data to form an initial trajectory set; conflict detection and decoupling optimization are carried out on the set through a mixed integer programming model, and an optimized track set is generated; and finally, performing consensus verification on the set, generating a scheduling instruction block, analyzing a path instruction from the scheduling instruction block, and issuing the path instruction to each mobile robot for execution. According to the method, the cooperative operation efficiency and safety of the multiple mobile robots in a complex dynamic environment are improved.

This invention discloses a method for ranking reserve projects for new power grid infrastructure, comprising: constructing a ranking indicator system from five dimensions: power grid development, security and supply, service quality, efficiency and effectiveness, and green and low-carbon development; collecting raw indicator data and distinguishing indicator types; obtaining objective weights for the indicators through a combination of two objective weighting methods; constructing a matrix by standardization transformation and weight combination; calculating relative proximity to form an objective ranking; subsequently integrating expert rankings; obtaining a subjective consensus ranking by constructing a pairwise preference matrix and an integer programming model; and finally, calculating standardized scores and weighted fusion of the subjective and objective ranking results using a weighted aggregation method to determine the ranking position of the reserve projects. This method balances data objectivity and expert experience, covers the core requirements of projects from multiple dimensions, has a rigorous ranking logic, and effectively improves the scientificity and accuracy of ranking reserve projects for new power grid infrastructure.

The application discloses a feature placement method and system for multi-GPU sampling graph neural network training, uniformly models a sampling graph neural network training process, and obtains hardware topology information; in a pre-sampling process, the number of times of accessing node features is counted as node heat, the node features are sorted according to the node heat, the node features are divided into a plurality of feature blocks according to a single-block capacity, and the feature blocks are divided into hot blocks or cold blocks according to block heat; based on the feature block set obtained by the division and the obtained hardware topology information, a mixed integer programming model is constructed, with the minimum bottleneck link time in a feature extraction stage and the memory load imbalance degree as targets, the mixed integer programming model is solved, and the placement position, access path and link flow of the globally jointly optimized hot block are obtained. The application can obtain a high-quality feature placement scheme under a given hardware constraint, minimize the extraction time, and improve the resource utilization rate of a multi-GPU system.

The invention discloses an airport ground crew intelligent scheduling method and system based on a Bron-Kerbosch maximum clique algorithm, and the method comprises the steps: obtaining employee information and task information from an external system, and carrying out the data preprocessing and virtual employee supplement; splitting tasks according to manpower demands and generating an executable employee set; a time conflict matrix is constructed, and a Bron-Kerbosch maximum clique algorithm is executed; defining decision variables, constructing constraint conditions and setting a multi-objective hierarchical optimization function; calling a mixed integer programming solver to solve the model; and generating a distribution scheme and performance statistical data. According to the method, a composite task is decomposed into independent sub-tasks through intelligent task splitting, a time conflict matrix is constructed, a Bron-Kerbosch algorithm is adopted to identify a maximum clique, a multi-target mixed integer programming model is established to balance a task allocation rate and resource balance, and a virtual employee mechanism is designed to ensure system robustness.

Embodiments of the present application provide an LNG marine transportation path planning data processing method, device and electronic equipment. The method comprises: obtaining decision reference data associated with LNG marine transportation path planning, the decision reference data comprising at least annual transportation plan data, ship data, route data, and port state data; constructing a mixed integer programming model and defining a target function and a constraint function associated with LNG marine transportation path planning in the mixed integer programming model; and outputting LNG marine transportation path planning data based on the decision reference data through the mixed integer programming model. The technical solution of the embodiments of the present application can improve the accuracy of LNG marine transportation path planning data processing.

The invention provides a scheduling method for dual-objective optimization of a multi-agent flow shop under time-of-use electricity price, and relates to the technical field of intelligent manufacturing and production scheduling. According to the method, unified modeling is carried out on the total power cost TEC and the total customer completion time TCTA, and a dual-target mixed integer programming model is constructed; a double-node decision tree structure is constructed, and synchronous processing of task sorting and processing time interval distribution is achieved; analyzing the structural characteristics of the multi-agent flow shop under the constraint of time-of-use electricity price, designing a series of inequality constraints and multiple pruning rules, and compressing a search space on the premise of ensuring the integrity of a feasible solution; the method comprises the following steps of: performing dual-objective cooperative solution, embedding a branch and bound algorithm into an improved epsilon-constraint framework to perform iterative solution, accurately obtaining a Pareto frontier, and providing multiple optimal scheduling schemes balanced between cost and service quality for decision makers. According to the method, dual decisions of task sorting and cycle allocation can be realized, and the problems of decision model splitting, low solving algorithm efficiency and the like are solved.

The embodiment of the invention provides a boarding gate distribution method based on passenger portraits, and the method comprises the steps: carrying out the group feature recognition of passengers of at least one target flight based on multi-source data, and obtaining a multi-dimensional feature portrait vector of each target flight; on the basis of the multi-dimensional feature portrait vector of each target flight, predicting the consumption probability of each shop in the airport corresponding to passengers of each target flight; determining the total expected income of all shops in the coverage area of each boarding gate based on the consumption probability of each shop in the airport corresponding to the passengers of each target flight, the predicted passenger flow scale of the target flight, the space interception rate coefficient of each shop and the historical average passenger unit price; and constructing a multi-target mixed integer programming model based on the total expected income of all shops in the coverage area of each boarding gate, the walking distance from the security check gate to each boarding gate, the operation constraint parameter and the strategy weight factor, and determining a boarding gate corresponding to each target flight by using the multi-target mixed integer programming model.

The invention discloses a multi-GPU sampling type graph neural network training-oriented feature placement method and system, and the method comprises the steps: carrying out the unified modeling of a sampling type graph neural network training process, and obtaining hardware topology information; in the pre-sampling process, counting the access times of the node features as node popularity, sorting the node features according to the node popularity, dividing the node features into a plurality of feature blocks according to single block capacity, and dividing the feature blocks into hot blocks or cold blocks according to block popularity; on the basis of the feature block set obtained through division and the obtained hardware topological information, a mixed integer programming model with the purpose of minimizing bottleneck link time and video memory load imbalance in the feature extraction stage is constructed, and the mixed integer programming model is solved; and obtaining the placement position, the access path and the link flow of the hot block subjected to global joint optimization. According to the method, a high-quality feature placement scheme can be obtained under given hardware constraints, so that the extraction time is minimized, and the resource utilization rate of a multi-GPU system is improved.

This invention belongs to the field of power grid fault diagnosis technology and discloses a power grid fault diagnosis method and system based on differential evolutionary logic operations. The method includes: collecting the action information of protection devices and circuit breakers in the power system; filtering out a set of candidate elements that have experienced faults based on preset association rules between faulty elements and protection / circuit breaker actions; establishing a 0-1 integer programming model based on the determined protection action information, circuit breaker action information, and candidate element set; and solving the 0-1 integer programming model using an improved differential evolutionary algorithm based on logic operations to determine the fault state of the candidate elements. This invention directly encodes individuals using binary, eliminating the need for floating-point to binary conversion. It also constructs a binary mutation operator and an improved crossover operator based on logic operations and employs adaptive parameter adjustment to balance the population diversity and search efficiency of the algorithm.

The application discloses a site selection method and device, equipment, medium and product, and relates to the technical field of data processing. The site selection method comprises the following steps: acquiring grain flow stream data; determining a feasible time for each transportation node that meets a first constraint condition according to the grain flow stream data, wherein the first constraint condition indicates that the grain can be unloaded at the transportation node and the ship can safely sail on the waterway; establishing a multi-objective integer programming model, wherein an objective function of the model is used to realize the dual objectives of minimizing the time-related grain loss risk and the task overdue cost, and a second constraint condition of the model comprises a storage transportation channel feasibility constraint condition based on the transportation node and the grain storage institution and a grain unloading operation feasibility constraint condition based on the grain flow task requirement and the feasible time; generating a candidate site selection scheme according to the second constraint condition; and solving the multi-objective integer programming model based on the candidate site selection scheme to obtain a site selection scheme that minimizes the objective function value under the condition of meeting the second constraint condition. The application can balance the feasibility and economy of grain transportation.

The example embodiments are directed to methods and systems for generating a model of an optimum flow of materials within a supply network based on a relax mixed integer programming model. For example, the method may include generating, via a software application, a model of the supply network based on the data stored in the storage device, executing a MIP optimizer on the model to generate a configuration of a flow of materials within the supply network, wherein the MIP optimizer is instructed to relax discrete constraints within the supply network when generating the configuration, generating a graphical user interface that includes a plurality of graphical objects corresponding to a plurality of locations included in the configuration and directional edges between the plurality of graphical objects corresponding to the flow of the materials between the plurality of locations, and displaying the graphical user interface.

The invention discloses a source network load storage multi-time scale optimization scheduling method for high-proportion new energy consumption, and belongs to the technical field of power system optimization scheduling. The method comprises the following steps: S1, data input and preprocessing; s2, constructing a mixed integer programming model; s3, solving the model; s4, outputting the data; s5, generating a new energy uncertainty scene; s6, scene reduction and typical scene selection; s7, constructing an intra-day robust optimization model; s8, a robust optimization model is solved; s9, outputting a correction instruction; s10, constructing a model prediction control model; s11, performing distributed solution and instruction issuing; and S12, an abnormal working condition detection and reverse triggering mechanism. According to three-stage time scale closed-loop collaborative optimization, a day-ahead-day-real-time three-layer progressive and bidirectional feedback scheduling architecture is constructed, and the cross-time scale coordination problem caused by new energy fluctuation is effectively solved.

The invention discloses a tourist personalized travel path optimization method based on multi-mode transport capacity collaborative configuration, and belongs to the technical field of smart tourism and traffic optimization, and the method comprises the following steps: S1, obtaining multi-source basic data and personalized parameters of tourists; s2, constructing a multimode traffic space-time network model comprising spatial positions corresponding to different time points; s3, constructing a multi-target mixed integer programming model for simultaneously optimizing tourist experience and traffic enterprise operation efficiency; s4, obtaining a Pareto optimal solution meeting all constraint conditions by adopting an adaptive genetic algorithm so as to obtain an optimal path solution adaptive to the personalized requirements of tourists; and S5, obtaining a personalized travel itinerary through visual processing operation of converting the path solution into full-process details in a timeline form. By adopting the method, personalized travel route optimization of multi-mode transport capacity collaboration under the support of multi-source data is realized, and meanwhile, the use experience of tourists is improved through the visual travel itinerary.

The invention relates to the technical field of spacecraft task planning, operational research and mixed integer planning, in particular to a multi-scene multi-stage task planning method and device for manned lunar surface activities, and the method comprises the steps: constructing a plurality of to-be-planned tasks for the manned lunar surface activities into a task pool, and setting constraint parameters corresponding to the task pool; preprocessing the task pool and the constraint parameters to construct a mixed integer programming model; and inputting the mixed integer programming model into a preset mathematical solver to find a final task execution scheme. Therefore, the problems that global optimization is difficult to complete at one time in multiple scenes such as normal planning, sudden adjustment and emergency return voyage in the prior art, modeling solving needs to be conducted again every time scene switching is conducted, and efficiency is low are solved.

The invention relates to the technical field of production line scheduling control, and particularly discloses a flexible production line arrangement scheduling and communication method by using an improved simulated annealing algorithm, which comprises the following steps: carrying out mathematical modeling simulation on production scheduling of an assembly line, and converting a multi-target flexible job shop scheduling problem into a mixed integer programming model; solving the mixed integer programming model by using an improved simulated annealing algorithm; a Transform model of an encoder-decoder architecture is adopted, multi-modal feature fusion is achieved through position encoding, wavelet transformation and type embedding, and a production scheduling scheme is obtained and used for assembly simulation of the discrete mixed flow assembly line; designing a Chisel-based tunnel communication architecture, and combining TCP long connection with TLS bidirectional authentication; through deep integration of multi-target mathematical modeling, a feature-driven prediction model and a high-reliability communication system, breakthrough improvement of dynamic process scheduling efficiency and equipment task allocation capability is realized.