324 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 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 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 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 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 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 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 invention discloses a permanent magnet brushless direct current motor and a parameter optimization method thereof. A rotor adopts a regular hexagon structure, and permanent magnets are alternately embedded into the rotor. Seven parameters such as the maximum thickness of a permanent magnet, a pole-arc coefficient and the width of a stator notch are selected for optimization, air gap flux density, no-load back electromotive force and no-load cogging torque are taken as optimization targets, a response surface model of a multi-objective function is constructed, parameter weights of the multi-objective function are determined in combination with a CRITIC weight analysis method, the motor parameters are optimized by adopting an improved MOEA / D algorithm, and the optimal parameters of the multi-objective function are obtained. And carrying out crossover variation on the population by directional variation guided by electromagnetic characteristics, and designing a weight vector optimization Chebyshev aggregation method to find an optimal iteration population. According to the weight in the Chebyshev aggregation function designed by the invention, the problems that the traditional Chebyshev aggregation method is not smooth enough for the continuous MOP problem and the obtained solution is not uniform are solved, and the distribution uniformity of the Pareto solution set is optimized. The method has obvious advantages in the aspects of convergence speed, solution set distribution and engineering feasibility.

The present invention discloses a microgrid multi-objective optimization method and system based on an improved SABO algorithm, relating to the field of energy management technology for microgrids. The method comprises the following steps: establishing a microgrid multi-objective optimization scheduling model based on distributed power sources in the microgrid; introducing a chaotic mapping mechanism, an elite-guided development strategy, and a golden sine algorithm into the subtraction average optimization algorithm to obtain an improved subtraction average optimization algorithm; constructing a multi-objective improved subtraction average optimizer based on the improved subtraction average optimization, using the multi-objective improved subtraction average optimizer to solve the multi-objective optimization scheduling model, and performing energy scheduling on the microgrid based on the solution results. The present invention uses the improved multi-objective SABO to solve the microgrid multi-objective optimization scheduling model, thereby ensuring a uniform distribution of the Pareto solution set and improving the calculation speed, thereby achieving efficient multi-objective optimization scheduling of the microgrid.

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 coal-fired unit dynamic entropy weight optimization control method and system fusing NSGA-II and PSO. The method and system are suitable for collaborative operation optimization of a machine system, a furnace system and an electric system of a large coal-fired power generation unit. The method comprises the steps that a simulation model based on a thermodynamic system structure is established, and correction is carried out in combination with unit performance test data; constructing a multi-objective optimization function including power supply coal consumption, station service power consumption rate and waste heat utilization rate; a dynamic entropy weight distribution mechanism is introduced to carry out real-time adaptive adjustment on the target weight; iteratively solving a Pareto solution set by adopting an NSGA-II and PSO fused multi-target hybrid optimization algorithm; performing satisfaction evaluation on the candidate solutions through a fuzzy membership function, and selecting an optimal solution scheme; and finally, the optimization control parameters are issued to an execution mechanism through a DCS system, and optimization strategy closed-loop control is realized. Compared with an existing method, the method can achieve coordinated optimization among multiple targets, has high self-adaptive capacity and energy-saving effect, and is suitable for operation control requirements under complex working conditions.

The invention discloses an electric power system dispatching method and device based on Pareto optimization, terminal equipment and a storage medium, and belongs to the field of electric power system dispatching.The method comprises the steps that unit power data, unit cost data, node power data and branch power data of a to-be-dispatched system are obtained; constructing a power system scheduling model and corresponding security constraints according to the unit power data, the unit cost data, the node power data and the branch power data; solving the power system dispatching model to obtain a Pareto solution set according to the unit power data, the unit cost data, the node power data and the branch power data under the security constraint by taking the minimum sum of the unit operation cost, the carbon emission and the valley-peak difference as a target; calculating the relative proximity of each Pareto solution according to the Pareto solution set; and scheduling the system to be scheduled by taking the Pareto solution corresponding to the minimum relative proximity as a scheduling strategy parameter. By implementing the method, the multi-target scheduling requirement of the power system is met.

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.