248 results about "Pareto solution" patented technology

The invention relates to the technical field of collaborative optimization, in particular to a geometric parameter collaborative optimization method of a taper hole machining cutter, which comprises the following steps: by constructing a high-fidelity digital twin model, integrating multi-physics field coupling and machine tool dynamic characteristics based on a finite element method, generating a geometric parameter-performance data mapping set and training and calculating an agent model; outputting a Pareto solution set through multi-target global optimization; and constructing a constraint range based on the solution set, and calling a digital twin model to carry out local optimization to obtain an optimal geometric parameter combination. The method comprises a self-correction mechanism: correcting a material constitutive relation and a friction coefficient through experimental data; staged adaptive learning, NSGA-II and DBSCAN clustering are adopted, and the efficiency is optimized along with the method; and a Pareto stability index and transfer learning are introduced, so that the result robustness and the cross-task reusability are improved. According to the method, high-precision and high-efficiency geometric parameter collaborative optimization of the taper hole machining tool can be realized.

The invention discloses a carbon dioxide mineralization storage dynamic intelligent regulation and control and permeation enhancement optimization method and system. The optimization method comprises the following steps: collecting field monitoring injection parameters and related data of reaction products in a mineralization storage process in real time; according to injection parameters monitored on site and related data of reaction products, two optimization objective functions of mineralization rate and free CO2 volume are formed; constructing a mineralization sequestration multi-objective optimization model, and screening out an optimal injection parameter set value from the Pareto solution set to obtain an optimal condition parameter; optimal injection parameters in the Pareto optimal solution set are input into the constructed field enhancement regulation and control module, and control variables are adjusted in real time according to real-time changes of reservoir response, mineralization reaction process and injection working conditions; and fracturing transformation is conducted on the target storage rock mass, the seepage enhancement effect of the target storage rock mass is quantitatively evaluated, an injection scheme is dynamically updated based on the transformed reservoir parameters, and the mineralization regulation and control system is enhanced.

The invention relates to a clustering and entropy-guided reentrant hybrid flow shop scheduling method. The method comprises the following steps: step 1, establishing a problem model; step 2, setting algorithm operation parameters; 3, adopting an initialization strategy to generate an exploration population and a development population; 4, judging whether a first-stage termination condition is met or not, if not, executing a first-stage evolutionary strategy and an updating strategy on the exploration population and the development population, and otherwise, executing the step 5; 5, constructing an elite population; 6, judging whether a second-stage termination condition is met or not, and if not, executing a second-stage evolutionary strategy on the elite population; otherwise, outputting a Pareto solution set; and 7, updating the elite population. According to the method, dynamic balance of global exploration and local development is realized, and the solution distribution can be improved while the solution set convergence is ensured, so that the completion time and the total energy consumption are reduced, the production cost is reduced, and the workshop scheduling efficiency is improved.

The invention provides a centrifugal pump performance optimization method and device, computer equipment and a storage medium, and belongs to the field of centrifugal pump design. The method comprises the steps that a parameterized model of a centrifugal pump impeller is established to define the geometrical shape of the centrifugal pump impeller, a design space is determined, and a plurality of sample points are generated; for each sample point, performing computational fluid dynamics simulation, adjusting inlet pressure, monitoring a ratio of simulation lift to rated lift, determining critical cavitation pressure corresponding to the sample point, and storing flow field data to construct a data set; training an agent model by using the data set; the lift, the efficiency and the critical cavitation pressure serve as targets, design variables serve as optimization variables, performance is evaluated through an agent model, global optimization is conducted through a multi-target optimization algorithm, and a target design scheme is selected from a Pareto solution set obtained through optimization. Therefore, the centrifugal pump performance under different design variables can be quickly and accurately predicted, multiple targets are effectively balanced, and a comprehensive optimization solution is provided.

The invention relates to the technical field of production process intelligent regulation and control, in particular to an intelligent regulation and control method and system for an instant gelatin production process, and the method specifically comprises the following steps: firstly, collecting process parameters such as reaction kettle temperature, acid-base concentration and the like in a production line sensor and control system to form production batch data; then, an optimization model and an intelligent regulation and control strategy are constructed, a dynamic incidence matrix is constructed through time-varying mutual information entropy, multiple types of features are fused to generate a process state descriptor, a constraint dominating relation and a population initialization strategy are optimized, and a self-adaptive genetic manipulation adjustment mechanism and a state-guided search and constraint processing method are designed; iteratively executing an improved NSGA-II algorithm, obtaining an optimized Pareto solution set, and evaluating diversity; and finally, selecting an optimal solution through a multi-criterion decision and a dynamic regulation and control strategy, and converting the optimal solution into an executable process parameter set value. According to the method, the accuracy and the high efficiency of the production process can be improved by realizing intelligent optimization regulation and control of the process parameters.

The invention provides an exercise load multi-objective optimization method based on an anti-gravity rehabilitation treadmill. The exercise load multi-objective optimization method comprises the steps of obtaining gait kinematics data, plantar pressure distribution data, electromyographic signal data and pain scores of lower limbs of a patient; based on the data, a gait asymmetry index and a myoelectricity activation sequence consistency index are screened out, the gait asymmetry index and the myoelectricity activation sequence consistency index are combined with pain scores to serve as key features, and a function grading model is constructed to obtain function grades; establishing a multi-objective optimization problem by taking minimization of a pain score, maximization of joint activity and electromyographic activation sequence consistency and minimization of muscle fatigue as objectives and taking non-overrun of articular cartilage contact stress as a constraint; an NSGA-III algorithm is adopted to solve the problem, and a Pareto optimal solution set of the weight reduction proportion and the speed of the treadmill is obtained; through a PPO reinforcement learning strategy network, taking the real-time state of a patient as input, and selecting and dynamically adjusting treadmill parameters in a Pareto solution set; and issuing the adjusted parameters to a treadmill execution mechanism in real time.

The invention discloses a fast charging station planning method based on urban dynamic traffic demand prediction and correction, and relates to the technical field of electric vehicle fast charging station planning, and the method comprises the steps: S1, constructing a traffic topology connection matrix according to urban road network topology, and recognizing traffic hub nodes and long-distance nodes as candidate nodes; s2, proposing three dynamic factors including social demands, user public praise and demand elasticity, and correcting the electric vehicle traffic demand prediction value of each path unit; s3, constructing a multi-target fast charging station capacity optimization model taking the minimum planning total cost and prediction error as targets, wherein the multi-target fast charging station capacity optimization model comprises fast charging station self constraints and power grid adaptation constraints; s4, solving the model by adopting a non-dominated genetic sorting algorithm to obtain a Pareto solution set, and selecting a planning result according to the demand of a decision maker; according to the method, the problems of blind site selection and static demand prediction in traditional planning are solved, economy and power grid safety are considered, planning scientificity and decision-making flexibility are improved, and supply and demand of charging resources can be effectively balanced.

The invention relates to a vibration suppression-oriented electromagnetic actuator data driving collaborative optimization design method, and belongs to the technical field of electromechanical equipment optimization design. Sample data are collected through Box-Behnken experimental design, a Gaussian process regression agent model is constructed based on the data, and a nonlinear complex mapping relation between each objective function and a design parameter is accurately represented. The influence degree of the design parameters is quantitatively evaluated through sensitivity analysis, the main design variables and the secondary design variables are distinguished accordingly, and the optimization efficiency is improved. Multi-objective optimization is carried out for the main design variables, and a design scheme with the optimal comprehensive performance is selected from a Pareto solution set through an objective decision-making mechanism based on an ideal point method in combination with the optimization result of the secondary design variables. According to the method, limitation of a traditional physical model is broken through through data-driven modeling, global optimization balance of electromagnetic performance, loss and volume is realized, and an efficient and quantifiable evaluation method is provided for design of the high-performance electromagnetic actuator.

The invention relates to the technical field of lithium ion battery thermal management, and discloses a lithium ion battery liquid cooling and heating management system and a multi-objective optimization design method thereof. The method comprises the steps that an electricity-heat-flow coupling simulation model is established and verified; determining design variables and sampling to obtain a performance index sample set; training an agent model to establish rapid mapping from variables to indexes; and carrying out multi-objective optimization based on the proxy model to obtain a Pareto solution set and deciding a final scheme. The system is an entity designed by adopting the method, and comprises a battery module, a bottom liquid cooling plate with a built-in liquid cooling pipe and an independent liquid cooling pipe surrounding the outer side of the module. By integrating simulation, an agent model and multi-objective optimization, the problem that multi-objectives such as temperature uniformity, energy consumption and structure compactness in liquid cooling system design are difficult to collaboratively optimize is solved, the crossing from experience trial and error to automatic global optimization is realized, the designed double-loop cooling system ensures heat dissipation safety, and the design efficiency is improved. The energy consumption and the material cost are obviously reduced.

The invention discloses a multi-objective reinforcement learning man-machine cooperative assembly task allocation method based on neighborhood parameter migration, and the method comprises the steps: building a mathematical model which aims at minimizing the physiological fatigue accumulated value of a human operator and minimizing the maximum completion time for a multi-objective optimization problem of task allocation in a man-machine cooperative assembly system; a multi-target problem is decomposed into N standard sub-problems by adopting a weighting and decomposition strategy, and training is accelerated through a neighborhood parameter migration strategy; each sub-problem is solved based on a near-end strategy optimization algorithm of an Actor-Critic framework, Gaussian noise is added to an Actor network to simulate environment uncertainty, an action mask mechanism is introduced to process priority constraints of assembly tasks, and it is ensured that a generated task allocation scheme is always feasible. According to the method, the convergence speed and diversity of the Pareto solution set can be remarkably improved, and the assembly efficiency and operator fatigue are effectively balanced.

The invention discloses a capacity configuration method and system for an off-grid wind and light storage combined hydrogen production system, and belongs to the technical field of renewable energy and hydrogen energy utilization. The method comprises the steps that a multi-time-scale coupling mathematical model including a wind power generation system, a photovoltaic power generation system, an energy storage system and an alkaline electrolytic cell is constructed, and a multi-target optimization model is constructed with the minimization of the annual wind and light abandoning rate, the minimization of the leveling hydrogen production cost and the minimization of the cold start proportion of the electrolytic cell as targets; and solving the multi-objective optimization model by adopting a multi-objective marine predator algorithm to obtain a Pareto solution set, determining an objective function entropy weight of the multi-objective optimization model by utilizing an improved entropy weight method, and selecting an optimal compromise solution as a system capacity configuration scheme. According to the method, the problem that the influence of wind and light output multi-time scale coupling, energy storage, dynamic response characteristics of the electrolytic cell and cold start of the electrolytic cell is ignored in the existing technical scheme is solved, and the purposes of efficiently absorbing wind and light resource fluctuation by the off-grid hydrogen production system, enhancing the operation reliability and optimizing the economic performance are achieved.

The invention relates to the technical field of intelligent manufacturing, in particular to an efficient and energy-saving flexible job shop scheduling method considering adjustment time. Comprising the steps of 1, initializing an initial population of an MTCP-SPEA algorithm; 2, executing a spatial awareness environment selection strategy; step 3, judging whether the current running time of the MTCP-SPEA algorithm reaches half of the total running time, if so, executing step 6, and otherwise, executing step 4; 4, a crossover operator and a mutation operator are adopted to evolve the current population, and current non-dominated individuals are selected to form a Pareto solution set; 5, executing local search operation and returning to the step 2; and step 6, executing a multi-thread-based constrained programming auxiliary optimization method to generate an improved elite set, screening out all non-dominated individuals from the improved elite set to form an optimal solution set, and outputting the optimal solution set. According to the method, the problem of efficient and energy-saving flexible job shop scheduling optimization considering time adjustment is effectively solved.

The present application relates to the technical field of medical informatization, and more particularly to a dynamic scheduling optimization method and device for emergency digital twin simulation, and a medium, the method comprising: obtaining initial state information of each emergency patient; based on the initial state information of each emergency patient, using a digital twin method and a fusion mechanism, obtaining a fused twin state vector; based on the fused twin state vector, obtaining a historical evolution sample set through a historical evolution model; based on the historical evolution sample set, predicting the evolution curve of the illness grade and risk probability of each emergency patient through a time series prediction model; based on the evolution curve, within a future preset short-term window time, through adaptive scheduling of hospital resources and emergency patients, to meet the requirements of a comprehensive cost function, obtaining an optimal adaptive scheduling scheme for hospital resources and emergency patients; continuously optimizing the Pareto solution set of the emergency service intelligent scheduling strategy, and providing a global perspective and intelligent optimization scheduling scheme for emergency services.

The invention relates to an excavator trajectory planning method based on an improved multi-objective particle swarm algorithm, and belongs to the technical field of engineering machinery trajectory planning. The method comprises the following steps: establishing a forward kinematics model through a D-H homogeneous coordinate transformation method; based on the forward kinematics model, establishing an inverse kinematics model through a geometric method; setting six path points in the track according to the mining track; based on an inverse kinematics model, converting the space coordinates of the six path points into space angles of joints; interpolation is carried out on the joint path points through a five-time B spline curve, and a track is generated; determining an optimization objective function according to actual application requirements, and setting constraint conditions according to a specific excavator type; and finally, the optimization problem is solved through an IADEMOPSO algorithm, an optimal Pareto solution is selected by using an average optimal evaluation formula, and an optimal track is generated. The track planning of the excavator can be realized.

The invention discloses a mining area water resource optimal configuration method, which comprises the following steps: acquiring related data of mining area water resources, including water resources at a water supply end and water use sources at a water demand end; giving a water supply quality sequence and a water supply priority, and determining a graded and quality-divided water distribution rule according to the water supply quality sequence and the water supply priority; constructing a mining area water resource optimal configuration model; solving the mining area water resource optimal configuration model by using a multi-objective genetic algorithm to obtain a Pareto solution, namely an optimal configuration scheme of water resources; a mining area water resource optimal configuration model is constructed, an economic target is directly associated with water supply economic benefits, an environment target focuses on mine water displacement, and a fair target is measured by means of a Gini coefficient, so that consideration on constraint conditions is more comprehensive compared with the prior art; the feasibility and stability of a model solving result in practical application can be ensured, and the technical problem that the comprehensive utilization efficiency of water resources in a mining area is not high in the prior art is solved.

The invention provides an axial flux motor multi-objective optimization method based on an improved homing religious algorithm. Firstly, key structure parameters are screened through sensitivity analysis, and the optimization range of the key structure parameters is determined; then, sample data is generated by adopting improved Latin hypercube sampling, and a data set is constructed through finite element simulation; then, performing automatic optimization on smooth factors of the generalized regression neural network based on a He-horse optimization algorithm, and establishing a high-precision agent model; improving a horizon algorithm by adopting Tent chaotic mapping, non-dominated sorting and congestion degree distance, and combining the horizon algorithm with the proxy model to carry out multi-objective optimization to generate a Pareto solution set; and finally, selecting an optimal motor parameter according to the target weight. According to the method, parameterized finite element analysis, an optimization agent model and an improved multi-target algorithm are deeply fused, so that the global optimization capability is ensured, the optimization efficiency and precision are remarkably improved, and an effective solution is provided for rapid and accurate design of the axial flux motor.

The invention discloses a building air conditioner energy consumption-electric charge-comfort level intelligent control method based on MMOE and ParetoMTL, and the method comprises the following steps: (1) collecting the cooling load data of a large commercial building air conditioner, carrying out the abnormal value detection, and completing the normalization preprocessing; (2) constructing an environment-price-behavior multi-source time sequence data set and performing grading standardization; (3) dividing a training set, a verification set and a test set according to building grouping; (4) completing feature fusion and realizing task decoupling under cooperative constraint by using an MMOE mechanism; (5) jointly training a three-branch expert network, an MLP gating network and a PCGrad shared parameter optimization model; (6) generating a multi-task prediction and Pareto solution set on the test set; and (7) evaluating prediction performance and trade-off efficiency by using a plurality of indexes. According to the method, multi-target conflict, physical consistency and Pareto solution set missing difficulty can be effectively solved, and a new scheme is provided for demand response HVAC intelligent control.

The invention belongs to the technical field of aviation fire extinguishing, and provides a forest and grassland aviation fire extinguishing multi-objective optimization configuration parameter generation method, which comprises the following steps: inputting fire scene data; calculating a fire scene spreading coefficient and a time point when the aviation fire extinguisher arrives at an operation point; establishing an aviation fire extinguishing model, a fire extinguishing area correction model and an aviation fire extinguishing resource demand model; constructing an aviation fire extinguishing multi-objective optimization model to obtain an aviation fire extinguishing Pareto solution set and a corresponding aviation fire extinguisher set; the aviation fire extinguishing Pareto solution set is analyzed, and a multi-target aviation fire extinguishing Pareto solution set is constructed; and determining a final aviation fire extinguishing Pareto solution and a corresponding aviation fire extinguisher set as aviation fire extinguishing configuration parameters according to the shortest fire extinguishing time and / or the minimum fire passing area. According to the method, the high efficiency of an integer linear programming method and the high precision of a complex analysis model are organically combined, the solving efficiency is effectively improved while the precision is ensured, and the method has very high practical application value.

The invention discloses a multi-objective collaborative optimization pipe network automatic transmission and distribution control method, and belongs to the technical field of process control and system automation, and the method comprises the steps: obtaining pipe network operation data, generating robust features through adversarial domain adaptation training, monitoring environment mutation in real time, building a dynamic multi-objective optimization model through the robust features, and carrying out the optimization of the dynamic multi-objective optimization model. And generating a Pareto solution set by adopting a dynamic multi-objective evolutionary algorithm, and finally selecting an optimal control solution in combination with a collaborative balance index and a preference function. According to the method, data field differences are eliminated by adopting an antagonistic domain adaptation technology, robust features insensitive to working condition changes are obtained, and an optimal equilibrium solution among energy consumption, pressure safety and service quality can be tracked and determined in real time when a pipe network load and an operating environment dynamically change in combination with a dynamic multi-objective evolutionary algorithm responding to environment mutation, so that the service quality of the pipe network is improved. And the real-time performance, the robustness and the multi-target collaborative optimization level of automatic transmission and distribution control of the pipe network are improved.

The invention provides a three-graded concrete mix proportion optimization method, device and equipment and a storage medium, and the method comprises the steps: obtaining multiple groups of sample data of three-graded concrete, and obtaining a training set based on all sample data; obtaining an associated physical equation set and an initial PINN model, determining a total loss function based on the associated physical equation set and the training set, and training the initial PINN model based on the total loss function to obtain a target PINN model; constructing a target function group based on the target PINN model, determining a target constraint condition, generating a Pareto solution set of the target function group through a non-dominated sorting genetic algorithm based on the target constraint condition and the target PINN model, determining the optimal mix proportion of the three-graded concrete based on the Pareto solution set, and outputting the optimal mix proportion. According to the technical scheme of the embodiment of the invention, the associated physical equation set is embedded into the target PINN model, effective training can be realized by using less sample data, and an unreasonable Pareto solution can be obtained by avoiding f, so that the required cost and the required time are reduced.

The invention discloses a multi-target robust optimization method for gulf nutritive salt pollution treatment, and the method comprises the following steps: S1, carrying out the global sensitivity analysis through a Sobol variance analysis method, setting a threshold value, and screening out a high-sensitivity parameter; s2, adopting a Latin hypercube sampling method for the high-sensitivity parameters to generate a plurality of disturbance scene parameter combinations, and constructing to obtain a unified disturbance sample space; s3, constructing a multi-target optimization model for nutrient salt pollution treatment, and determining water quality optimization and treatment cost minimization targets; s4, calling an NSGA-II algorithm to respectively solve a multi-objective optimization problem, and obtaining a Pareto solution set under different disturbance scenes; s5, calculating an expected value and a standard deviation of each solution under the representative disturbance sample and a gradient change rate under decision disturbance; s6, evaluating the robustness of each candidate solution to obtain a representative solution set; s7, checking target function response performance of each representative solution in a non-original disturbance scene; and S8, outputting an optimal scheme for treating the nutritive salt pollution of the watershed gulf.

The invention discloses an intelligent production line energy consumption optimization decision-making system based on digital twinning, and the system comprises a physical sensing layer which is used for collecting the production line data of a physical production line in real time; the data fusion layer is used for performing treatment and feature extraction on the collected production line data and outputting structured data; the model construction layer is used for constructing and updating a digital twinborn model and an energy consumption model based on the structured data; the optimization decision-making layer is used for constructing a multi-objective optimization function according to the production takt time, the load fluctuation among the workstations, the adjustment cost and the total production energy consumption, and based on the simulation result of the digital twin model, the optimization decision-making layer is established by taking the production takt time, the minimization of the load fluctuation among the workstations, the minimization of the adjustment cost and the minimization of the total production energy consumption as optimization objectives; solving by adopting a multi-objective optimization algorithm, and outputting a Pareto solution set comprising a plurality of non-dominated solutions; and the application execution layer is used for receiving the optimization scheme selected from the Pareto solution set.

The invention provides a green concrete mix proportion optimization method and system, and relates to the technical field of mix proportion optimizing.The green concrete mix proportion optimization method comprises the steps that firstly, the dosage of cement, fly ash, slag powder, water and a water reducing agent serves as variables, the material cost and the predicted compressive strength serve as targets, and a Pareto solution set is generated through a multi-target optimization algorithm to serve as a preliminary scheme pool; thirdly, calculating the carbon cost efficiency of other schemes by taking the scheme with the lowest cost as a benchmark, and screening out a potential optimization scheme; then, through Monte Carlo simulation, considering the fluctuation of the water demand ratio of the fly ash, the activity index of the slag powder and the water-reducing rate of the water reducing agent, and evaluating the variable coefficient of the strength of each scheme so as to measure the robustness; and finally, carrying out weighted scoring on the normalized carbon cost efficiency and variation coefficient, and sorting according to a comprehensive score to select an optimal mix proportion. According to the method, the economical efficiency, the environmental protection property and the production robustness are comprehensively optimized on the premise of ensuring the performance of the concrete.

The invention belongs to the technical field of active power distribution network optimization scheduling, and discloses an active power distribution network day-ahead scheduling method, system and device based on an improved NSGA-II algorithm, and a medium, so as to solve the problem that the existing scheduling method is difficult to consider multiple targets such as operation economy, new energy consumption and voltage safety at the same time. The method comprises the following steps: establishing a day-ahead scheduling model taking minimization of the total operation cost of the power distribution network, minimization of the new energy abandoned electric quantity and minimization of the system voltage deviation as optimization objectives, and setting corresponding power grid operation constraint conditions; carrying out iterative solution on the day-ahead scheduling model by adopting an improved NSGA-II algorithm to obtain a Pareto solution set; and on the basis of the Pareto solution set, a compromise solution is selected through a fuzzy decision method to serve as a day-ahead scheduling scheme. Compared with a traditional single-target or empirical scheduling method, on the premise that the voltage safety is ensured, the system operation cost can be remarkably reduced, and the utilization rate of renewable energy sources can be improved.

The invention relates to a micro-grid photovoltaic energy storage capacity optimization method based on double-layer multi-target collaborative decision, and the method comprises the following steps: 1, analyzing the structure and operation mode of a new energy micro-grid in a rural region, and constructing a capacity optimization configuration model of an optical storage system; step 2, optimizing an objective function by an inner layer and an outer layer; the inner-layer optimization target is to minimize the daily volatility of the complementary power generation system and minimize the daily peak-valley difference; 3, solving an optical storage capacity optimal configuration model: obtaining an optimal Pareto solution set of the target function by adopting a quantum particle swarm algorithm; 4, adopting an interactive multi-criterion decision based on compromise solution selection; 5, outputting a capacity configuration scheme with the optimal comprehensive efficiency; the method has the advantages that intermittency and peak regulation capacity are comprehensively considered, stable power supply is ensured, power supply reliability is improved, energy utilization efficiency is improved, system construction and operation cost is reduced, and economic benefits are improved.

The invention discloses a motor multi-objective optimization design method and system based on a Kriging agent model. Comprising the following steps: constructing an initial data set through an experimental design method and finite element simulation calculation, so as to train an initial Kriging agent model which takes a motor design variable combination as an input variable and takes motor performance as an output response; the proxy model serves as a target function, a multi-target optimization problem is solved, the proxy model is updated, and an updating strategy is as follows: after each round of optimization is finished, a high-error solution in a current Pareto solution set is screened based on a Kriging model prediction mean square error, a finite element response of the high-error solution is obtained and supplemented to a data set, and the proxy model is updated and trained; and repeating the process until the optimization result converges. According to the method, high-uncertainty region samples are selectively supplemented, the calculation cost is remarkably reduced while the local prediction precision of the proxy model at the Pareto leading edge is improved, and the method has good practicability and economical efficiency.

The invention provides a solid hydrogen storage single tank structure optimization method and device based on a physical information neural network, and relates to the field of intelligent hydrogen storage. The method comprises the following steps: acquiring structural parameters and multi-source data of a hydrogen storage tank body; constructing a full-parameter space training set based on the multi-source data; based on the full-parameter space training set, constructing a multi-physics field agent model by embedding a multi-physics field control partial differential equation set; inputting the structural parameters of the hydrogen storage tank into the multi-physics field agent model, and outputting a Pareto solution set through the multi-physics field agent model based on the optimization decision; and determining a target weight corresponding to each optimization decision through an entropy weight method, and obtaining an optimized target structure parameter combination from the Pareto solution set based on the target weight in combination with a double-base-point method. The problem that a solid hydrogen storage system lacks a refined regulation and control mechanism in structural design due to the fact that the nonlinear effect and the multi-factor coupling phenomenon in the hydrogen storage process cannot be accurately described only by means of a traditional physical model is solved.

The invention relates to a multi-target logistics distribution path optimization method based on AI, and the method comprises the following steps: obtaining order data, road network data and vehicle data of logistics distribution of a customer point, extracting basic information from the three types of data, and constructing the multi-dimensional features of the customer point; inputting the multi-dimensional features of the customer points into a pre-trained graph attention network for feature embedding, generating a distribution network feature graph fusing customer demands and a road network relationship, inputting a pre-trained reinforcement learning agent, and generating an initial path population with quality and diversity balance; performing iterative optimization on the initial path population based on a Memetic algorithm framework; and calculating the fitness of each individual on a plurality of preset optimization objectives, maintaining a Pareto solution set by adopting a multi-objective evolutionary algorithm with reference points, and recommending a final path scheme from the Pareto solution set based on user preferences. The method has the effect of remarkably improving the quality, efficiency and dynamic adaptability of path optimization.

The invention relates to the technical field of data processing, and discloses a construction management method and system for intelligent engineering. The method comprises the steps that construction data containing mileage stake number indexes are collected through special detection equipment, a linear coupling constraint model of four processes is established, an improved hummingbird algorithm is used for optimizing three targets of resource allocation, safety risk and quality control, a Pareto solution set is generated, and dynamic scheduling based on mileage progress is achieved. The technical problems of insufficient data acquisition precision, inaccurate process constraint modeling, low pertinence of a multi-target optimization algorithm and lack of dynamic scheduling capability in the existing railway engineering construction management technology are solved, and the intelligence level and the multi-process collaborative optimization effect of railway engineering construction management are improved.

The invention relates to the technical field of medical informatization, in particular to a dynamic scheduling optimization method and device for emergency digital twin simulation and a medium, and the method comprises the steps: obtaining the initial state information of each emergency patient; based on the initial state information of each emergency patient, adopting a digital twinning method and a fusion mechanism to obtain a fused twinning state vector; based on the fused twin state vector, obtaining a historical evolution sample set through a historical evolution model; on the basis of the historical evolution sample set, through a time sequence prediction model, predicting an evolution curve of the condition level and the risk probability of each emergency patient; on the basis of the evolution curve, in the future preset short-term window time, through adaptive scheduling of hospital resources and emergency patients, the requirements of a comprehensive cost function are met, and an optimal adaptive scheduling scheme of the hospital resources and the emergency patients is obtained; and a Pareto solution set of the emergency service intelligent scheduling strategy is continuously optimized and generated, and a global perspective and an intelligent optimization scheduling scheme are provided for emergency services.