379 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 relates to the technical field of ship power systems and energy management. The invention provides an energy optimization management method and system for an extended-range hybrid power ship. The method comprises the following steps: collecting ship multi-source heterogeneous data in real time, and constructing a multi-source heterogeneous data set; based on the multi-source heterogeneous data set, constructing a load prediction model based on a time sequence convolutional network and long and short term memory fusion, and dynamically outputting propulsive power demand probability distribution in a future navigation period; a multi-objective optimization model is established, and an improved multi-objective genetic algorithm is adopted to generate a Pareto frontier solution set; and on the basis of a rolling time domain optimization framework, in combination with real-time navigation situation awareness data, carrying out online correction on the Pareto solution set, and generating and executing an optimal energy distribution instruction set. The problems of an existing extended-range hybrid power ship energy management technology in the aspects of working condition adaptability, multi-energy coupling efficiency, real-time state sensing and predicting capacity and emission and economical efficiency balance are solved.

The invention discloses a low-carbon stability conjoint analysis method and system for underground engineering, and particularly relates to the technical field of engineering modeling optimization, and the method comprises the steps: obtaining rock-soil mechanical parameters, construction parameters and full-life-cycle carbon emission factor data of a target area; dividing a collaborative optimization region based on parameter fusion and identifying a conflict section; constructing a mechanical stability numerical model and a carbon emission quantitative model, outputting double indexes, and generating a double-target optimization decision space through dynamic weight distribution; traversing and screening candidate schemes meeting a safety threshold and carbon emission constraints, and extracting an anti-interference Pareto solution set in combination with geological sensitivity simulation; a low-carbon-stability balance scheme is generated through reverse coupling verification, and full-life-cycle low-carbon stability optimization support is provided for complex geological engineering.

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 discloses a multi-target demand response optimization method considering low carbon and user satisfaction, which belongs to the technical field of demand side management and optimization scheduling, and comprises the following steps: randomly generating an initial scheduling solution set containing a population 1 and a population 2, establishing a multi-target scheduling model simultaneously including power balance constraint, user comfort constraint and resource capacity constraint; an infeasible solution in the population 1 is processed through an adaptive penalty function, the population 2 is optimized through a feasibility priority strategy, and the population is evolved and updated in combination with crossover, variation, tournament selection and environment selection mechanisms; and evaluating a multi-target fitness value of a scheduling solution in each generation of iteration, screening a Pareto frontier solution set, continuously updating the Pareto frontier solution set until a termination condition is met, and selecting a compromise optimization scheduling scheme from the final Pareto solution set. According to the method, low carbon, cost and user satisfaction are integrated, a dual population optimization algorithm is adopted, resource coordinated scheduling is achieved, and system efficiency and environmental benefits are remarkably improved.

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 relates to the technical field of data processing, in particular to an electric power carbon emission intelligent analysis system based on a dual-carbon target, which comprises a quantum security acquisition engine, a dynamic privacy calculation fusion device, a quantum and classical mixed twin modeling module, a multi-agent quantum decision engine and a cross-modal causal auditing platform. The dynamic privacy calculation fusion device aggregates the distributed carbon intensity feature vectors in a ciphertext state, and dynamically adjusts the Laplace noise amplitude based on a polarization key fragmentation strategy; the quantum decision engine optimizes and maps the unit commitment to an Isin model to output a Pareto solution set, and generates dynamic block chain contract terms; a cross-modal auditing platform monitors a carbon footprint boundary, and when a prediction deviation exceeds a threshold value, quantum branch parameters are calibrated through an anti-fact gradient and contract terms are reconstructed. According to the system, privacy security is maintained through frequency binding of quantum noise amplitude and channel bit error rate, prediction time lag caused by source-load coupling is solved through anti-fact gradient closed-loop calibration, and the low-carbon scheduling precision of the power system is improved.

The invention relates to a multi-objective collaborative optimization hydroponic lettuce nutrient solution concentration time sequence dynamic regulation and control method. Compared with the prior art, the defect that an optimal nutrient solution concentration scheme needed by hydroponic crop time sequence dynamic growth interacting with the environment is difficult to determine is overcome. The method comprises the following steps: constructing a time sequence group photosynthetic rate prediction model and a time sequence group moisture utilization efficiency prediction model; constructing a multi-objective optimization model; solving a Pareto solution set of the multi-objective optimization model; constructing a nutrient solution concentration time sequence regulation and control model; dynamically regulating the concentration of the hydroponic lettuce nutrient solution in a time sequence. According to the method, a non-dominated sorting multi-target genetic algorithm is adopted to solve the multi-target problem of a time sequence group photosynthetic rate prediction model and a time sequence group water utilization efficiency prediction model, and an optimal balance regulation target value is obtained by combining a uniformly distributed population strategy, non-dominated sorting, crowding degree calculation, an elitist strategy and a population updating mechanism. The problem of dynamic regulation and control of the nutrient solution concentration for plant time sequence growth is effectively solved.

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 discloses a multi-target aircraft taxiing path optimization method, which belongs to the technical field of aircraft path planning, and comprises the following steps: S1, abstracting the spatial layout of an airport into a network model diagram; s2, constructing a space-time state network of the taxiing path of the aircraft; s3, performing multi-objective optimization on the taxiing path of the aircraft by adopting a two-stage Gurobi solving method; s4, obtaining a group of Pareto solution sets through an NSGA-II algorithm; s5, analyzing the Pareto solution set, and balancing the balance among multiple targets; s6, comparing the solution time and the solution quality of the two-stage Gurobi solution method and the improved NSGA-II algorithm; and S7, evaluating the performance difference between the first-arrival first-service strategy and the approach priority strategy, and the method has the beneficial effects that the sliding path optimization model is constructed based on the improved NSGA-II algorithm, so that the overall operation efficiency of the airport is improved through path optimization, and meanwhile, the scene sliding safety is further enhanced on the basis of reducing the sliding time and carbon emission.

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 discloses a short-term multi-objective optimal scheduling method for a hybrid pumped storage power station, which belongs to the technical field of optimal scheduling of a power system and comprises the following steps of: acquiring initial input data; taking minimization of residual load mean square deviation as a peak regulation target, and taking minimization of lower reservoir tail water level daily variable amplitude and minimization of lower reservoir dam water level daily variable amplitude as navigation targets to construct a multi-target optimization scheduling model; performing mutual exclusion processing on reversible unit power generation and water pumping flow constraints to generate an initial solution, correcting the initial solution, solving the multi-target optimization scheduling model to obtain a Pareto solution set, dividing a Pareto leading edge distribution space, and selecting a feasible scheduling scheme with the maximum peak valley difference reduction rate as an optimal scheduling scheme; and selecting a final scheduling scheme from all the subspaces. The problems that in the prior art, peak regulation operation and navigation demand dynamic matching is insufficient, and a multi-source constraint coupling processing mechanism is lacked are solved.

The invention discloses a multi-objective optimized intelligent job scheduling system and a scheduling method thereof, and belongs to the technical field of supercomputers. According to the scheduling method, a Pareto solution set in multiple targets is searched through a simulated annealing algorithm, simulated annealing algorithm search and job scheduling are combined, and an optimal job execution sequence is sought. According to the method, the simulated annealing algorithm and the job scheduling scene demand are combined, and the optimal scheduling mode is searched, so that the job scheduling is more intelligent, and the scheduling efficiency is improved to a certain extent. In the super computer sharing the burst buffer architecture, the burst buffer requirement of the job can be effectively utilized, the backfilling limitation existing in traditional scheduling can be broken through, and the job scheduling efficiency and the system resource utilization rate are improved.

The invention discloses a simulation method for urban flood emergency rescue path planning, and the method comprises the steps: carrying out the modeling of an urban road network based on an urban topographic map, rainfall data, existing water distribution and key position information; mIKE simulation software is used for establishing a hydrodynamic model to simulate the rainfall and flood spreading process of the city, and flood dynamic information, including the depth and flow velocity of the flood, at each position point in the flood disaster scene model is obtained; respectively constructing objective functions according to the shortest maximum time of arrival of the disaster relief point, the lowest driving risk of the rescue vehicle and the shortest total weighted rescue time; setting constraint conditions to ensure that all disaster relief points are covered, and preventing vehicles from being excessively distributed; based on the flood dynamic information, the objective function and the constraint condition, adopting an NSGA-II non-dominated sorting genetic algorithm to solve a Pareto solution set; and performing weighted analysis on a result in the Pareto solution set to obtain an optimal solution, and outputting an urban flood emergency rescue path plan.

The invention provides an unmanned aerial vehicle distribution center site selection method based on a fuzzy mathematical programming particle swarm algorithm, aims to complete position selection of a park distribution center under multi-target and complex constraint conditions under the background of low-altitude economy, intelligent logistics and unmanned distribution, and comprises the following steps: carrying out detailed modeling characterization on a park multi-unmanned aerial vehicle distribution center site selection problem; delivery time and operation cost targets are subjected to target reduction through fuzzy mathematical programming, solving under complex constraint conditions is facilitated, the particle swarm optimization optimizes the search process through an elitism strategy, a limited number of Pareto solutions are obtained through an epsilon-constraint method, convergence is gradually carried out to an optimal solution, and the optimal solution is obtained. The method is suitable for multi-unmanned-aerial-vehicle distribution in an unattended park, the load and cruising ability of the unmanned aerial vehicles, energy consumption, no-fly zone distribution and other scenes with complex constraint conditions are considered, and an efficient and accurate unmanned aerial vehicle distribution center site selection scheme can be provided.

The invention discloses a scheduling rule optimization method for a water-wind-light integrated system, and relates to the technical field of energy system scheduling. Comprising the following steps: designing a water-wind-light multi-energy complementary long-term scheduling graph, initializing upper and lower scheduling lines of the scheduling graph and deciding output parameters to calculate the monthly total generating capacity; designing a middle-term scheduling rule, and distributing the monthly total generating capacity to each day to obtain daily generating capacity; designing a short-term scheduling rule, and distributing the daily total power generation amount to each hour to obtain a day-ahead power generation plan; determining the load state of each hydropower station in the cascade hydropower stations, and constraining the hydraulic connection and power connection between different hydropower stations to obtain a real-time scheduling strategy; and performing updating iteration optimization on optimization variables of the multi-objective optimization scheduling model to obtain a Pareto solution set, and determining a water-wind-light integrated scheduling rule according to a compromise solution. According to the invention, the risks of power abandoning and load loss in the operation process can be reduced to the greatest extent.

The invention discloses a CMIES low-carbon operation scheme generation method based on variable reduction multi-objective evolutionary optimization, and belongs to the technical field of integrated energy systems. Aiming at the multi-target complex constraint characteristic of the CMIES system, a dynamic variable reduction mechanism driven by constraint correlation is provided, key variables are accurately recognized through correlation analysis and constraint gain indexes, and the number of feasible solutions and constraint processing efficiency are effectively improved; a two-stage coevolution strategy is adopted to be fused with random reduction and dynamic analysis, global exploration and local development capacity is balanced in combination with a main and auxiliary population multi-strategy cooperation mechanism, and a Pareto solution set which is high in quality and uniform in distribution is generated; by constructing a low-dimensional auxiliary optimization problem and an inherited updating mechanism, the calculation complexity of a high-dimensional space is reduced, and the algorithm efficiency is remarkably improved while the quality of a solution set is ensured.

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 discloses a server cluster load balancing cooperative processing system and method and electronic equipment, and relates to the technical field of servers, and the method comprises the steps: receiving node operation data sent by each edge node in a region; carrying out load prediction in the region according to the node operation data and a pre-trained neural network model to obtain a prediction result; determining a dynamic scheduling weight; solving calculation is carried out according to the multiple pre-stored node mapping schemes, preset solving parameters and a pre-established target function, and a multi-target Pareto solution set is obtained; performing preferential processing according to the Pareto solution set and the dynamic scheduling weight to obtain a target node mapping scheme; and according to the target node mapping scheme, controlling each edge node in the region to execute load balancing cooperative operation. The problem that the load balancing performance of the server cluster is poor is solved, and the server load balancing performance of the server cluster is improved by selecting a proper target node mapping scheme based on the node operation data collected by each edge node of the edge layer.

The invention discloses a sintering batching optimization scheme, and relates to the technical field of sintering ore batching control, and the technical key points are as follows: a random forest proxy model is used to predict the performance of finished ore, and a nonlinear mapping relationship between an independent variable and a dependent variable is used as a target function; carrying out Pareto optimal solution set search through non-dominated sorting and crowding distance calculation by adopting an NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm; calculating the weight of each target index by using an entropy weight method based on a Pareto solution set; weight is determined from a Pareto solution set generated by an NSGA-II algorithm, an optimal batching scheme closest to an ideal solution and far away from a negative ideal solution is screened out through a TOPSIS method, and multi-target comprehensive weighing is achieved; according to the method, sintering batching optimization based on finished ore performance feedback is realized, dynamic optimization of a batching plan can be effectively guided, the sinter quality control level is improved, the production cost is reduced, and technical support is provided for efficient and stable operation of a blast furnace ironmaking process.

The invention discloses a machining center lathe bed optimization method and device based on an integrated proxy model assisted evolutionary algorithm, and the method comprises the following steps: carrying out the three-dimensional modeling of an original pentahedral lathe bed, carrying out the static analysis and modal analysis of the original pentahedral lathe bed, and taking the quality of the lathe bed, the maximum static deformation and the first-order inherent frequency as optimization targets; selecting design variables, upper and lower limits of each design variable and a target function as constraints; obtaining corresponding test sample points between the design variables and the optimization targets through finite element analysis, and constructing a dynamic ensemble learning model by using the test sample points; and performing optimization solution to obtain a high-quality Pareto solution set by applying the previously established multi-objective optimization mathematical model of the pentahedron machining center lathe bed, selecting a high-quality solution, and performing simulation verification on an optimization result according to optimized structural parameters. According to the method, the number of expensive real fitness function evaluation times is effectively limited, the convergence of the algorithm and the diversity of the population are considered, and training data redundancy is prevented.

The invention discloses a coal washing equipment image processing intelligent monitoring system based on strategy optimization, relates to the technical field of coal washing equipment image processing, and constructs a closed-loop intelligent optimization system composed of an image acquisition module, a process monitoring module, a simulation modeling module, a multi-objective optimization module, a strategy scoring module, a control execution module, a feedback regulation module and the like. By introducing response difference calculation and a response space redistribution model, sparse processing is effectively carried out on a Pareto solution set, and the strategy distinction degree is improved; multi-factor dynamic scoring is carried out by combining image features, historical performance and response tracks, and it is guaranteed that the selected strategy has the optimal execution effect under the current working condition; the system also has model self-correction and scoring parameter self-adaption capabilities in operation, high-precision, steady-state and self-learning control of the whole process is realized, and the intelligent level and the operation reliability of the coal washing equipment are remarkably improved.

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 cloud manufacturing, in particular to a cloud manufacturing service combination and scheduling integrated optimization method. Comprising the steps that 1, a complex matching network of cloud manufacturing services and cloud manufacturing tasks is established, the cloud manufacturing services are divided into cloud manufacturing production services and cloud manufacturing logistics services, and the cloud manufacturing tasks also comprise cloud manufacturing production tasks and cloud manufacturing logistics tasks; 2, a cloud manufacturing service combination and scheduling integration optimization model is established, QoS of all cloud manufacturing services in the network is quantified through weighted summation of a service quality parameter and a service reliability parameter, the two parameters are dynamically described by a time domain, and an initial value is an evaluation value obtained in advance; and step 3, solving the integrated optimization model by adopting a multi-population co-evolution algorithm based on meta-knowledge adaptive migration to obtain a multi-objective optimization Pareto solution set, and decoding the multi-objective optimization Pareto solution set into an optimization scheduling scheme. And performing combined configuration and scheduling optimization on services in the cloud manufacturing platform.

The invention discloses an industrial park integrated energy system optimization method and system, and belongs to the technical field of building and energy system design optimizing.By determining constraint conditions of energy balance constraint, equipment capacity constraint and equipment operation constraint, load data in an industrial park are used as initial conditions, design parameters and meteorological data are input, and the optimal energy balance constraint, equipment capacity constraint and equipment operation constraint are determined; taking the hydrogen doping ratio as a decision variable, constructing a comprehensive energy system optimization model and determining a target function; solving the objective function by taking minimization of annual total cost, carbon emission and energy consumption of the integrated energy system as an objective, and outputting a Pareto solution set; and performing multi-target sorting on the Pareto solution set, screening out an optimal solution corresponding to the decision variable, and obtaining a real-time power condition of each device corresponding to the optimal solution. According to the comprehensive energy system optimization model constructed by the method, the use proportion of hydrogen energy can be adjusted under different operation conditions, so that the optimal energy efficiency and environmental protection effect are achieved.

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.