97results about How to "Strong global search capability" patented technology

The invention discloses a regional risk prediction method and system based on toxic gas diffusion analysis, and provides a regional risk prediction method based on toxic gas diffusion analysis on the basis of source item traceability prediction of a leakage source. The content of the invention mainly relates to development and improvement of a gas diffusion model, data acquisition and processing, construction of a risk prediction model, uncertainty analysis, research of an emergency response strategy, case analysis and verification and system integration and demonstration application, and through comprehensive consideration of factors such as gas properties, meteorological conditions and topographic features, prediction accuracy is improved, and the risk of the gas diffusion model is predicted. Scientific basis is provided for risk management, and practicability and popularization of the prediction system are finally realized.

The invention belongs to the technical field of intelligent control, and discloses a building energy-saving intelligent integrated control system and method. Comprising the steps of collecting building comprehensive data of a building internal space area; preprocessing the building comprehensive data to obtain standard building data; establishing a building dynamic model according to the standard building data, and predicting by using the dynamic model to obtain an energy consumption change curve and a comfort change curve of the internal space area of the building in a future fixed time period; solving to obtain a series of optimal control strategy sequences by taking the energy consumption change curve and the comfort change curve of the internal space area of the building in the future fixed time period as constraints; the optimal control strategy sequence obtained through solving is distributed to all actuators, and integrated intelligent control over heating equipment, refrigeration equipment, ventilation equipment, illumination equipment and the like is achieved; not only is the energy-saving level of the building improved, but also a comfortable environment is created for the indoor space, and the comprehensive performance of the building is comprehensively enhanced.

The invention relates to an unknown dynamic environment mobile robot path planning method based on reinforcement learning, and belongs to the technical field of path planning. According to the method, a two-layer reinforcement learning structure is adopted, a bottom layer module learns global path planning, and an ellipse specific and precise strategy and a GliEF-Dyna-Q algorithm are provided in the early stage; the high-level module learns an obstacle avoidance rule to complete local dynamic obstacle avoidance; according to the GliEF-Dyna-Q algorithm, elliptic geometric knowledge is introduced to design an elliptic knowledge special and precise strategy to narrow the sampling range, a reward mechanism and path search are constrained and optimized through global and local perspectives, and an improved SA-epsilon greedy strategy is fused into Dyna-Q to achieve efficient global path planning. The high-level module provides a brand-new obstacle avoidance behavior reward mechanism and a more adaptive value function updating method so as to carry out comprehensive local obstacle avoidance activities. According to the invention, the robot can more efficiently realize obstacle avoidance and complete optimal path driving in a dynamic environment.

The invention discloses a method and system for optimizing nonlinear energy vibration absorber arrangement based on a particle swarm optimization algorithm, and belongs to the technical field of nonlinear energy vibration absorber arrangement method.According to design parameters of a nonlinear vibration absorber, the mass, damping coefficient and nonlinear rigidity parameters of the vibration absorber are determined, and according to physical characteristics and stress conditions of a stand pipe, the nonlinear energy vibration absorber arrangement is optimized. Establishing a coupling kinetic equation of the riser and the vibration absorbers, describing vibration behaviors under the action of ocean currents and waves, setting initial values of the number, the position and the speed of particles, defining a fitness function for evaluating the position quality of each particle, and calculating vibration responses of the riser system under different vibration absorber arrangement conditions through a time domain minimum residual value method. Taking the minimum vibration amplitude as a target, calculating the fitness value of the position of each particle, iteratively updating the position and speed of each particle according to the updating rule of the particle swarm algorithm, and gradually approaching the optimal solution.

The invention discloses a structure optimization design method based on a particle swarm optimization algorithm, aiming at a super high-rise building, an initial structure parameter matrix is generated by establishing an optimization model, setting a target function and taking structure layer displacement, an inter-layer displacement angle and top layer peak resultant acceleration as constraint conditions; a wind vibration response characteristic matrix is obtained by adopting a frequency domain calculation method in combination with structural dynamic characteristics and a wind tunnel test result, and an equivalent static wind load matrix and an acceleration response matrix are calculated. A combined algorithm of an optimization criterion method and a particle swarm algorithm is used for performing multiple iterations, wind load and acceleration response data are recalculated by initializing and updating related parameters of a particle swarm, calculating a fitness value, iteratively updating a position and a speed matrix, and finally an optimal solution matrix is output according to convergence judgment. Therefore, a final design scheme of the structure is determined, and comprehensive optimization of wind resistance and economic benefits of the structure is realized.

The invention relates to a micro-grid multi-objective optimization scheduling method and device, and belongs to the technical field of micro-grid optimization scheduling, and the method comprises the following steps: determining the structure of a micro-grid system, and collecting the equipment parameters of each distributed power supply; establishing a micro-grid optimization scheduling model according to the characteristics of the micro-grid and the collected equipment parameters, wherein the micro-grid optimization scheduling model takes minimization of user operation cost and environment cost as an objective function; generating an initial population of the MOPSO algorithm by using a chaotic equation, and improving the value of a learning factor by using a dynamic adjustment parameter to obtain an improved MOPSO algorithm; and performing optimization solution on the micro-grid optimization scheduling model by using an improved MOPSO algorithm to obtain the output power of each distributed power supply. According to the invention, the cost and environmental factors of the micro-grid are considered at the same time, and effective scheduling and management of the micro-grid are realized.

The invention provides an electromyographic signal denoising method based on RIME-SVMD (Reactive International Mobile Equipment-Space Vector Multiple Data). The method comprises the following steps: S1, acquiring an original electromyographic signal, and adaptively iteratively searching an optimal balance parameter of an SVMD algorithm through an RIME algorithm; s2, carrying out successive variational mode decomposition on the original electromyographic signals by using SVMD to obtain decomposed IMF; S3, calculating NCC corresponding to each IMF, and by taking 0.3 as a standard, determining a part greater than 0.3 as a useful signal, and determining a part less than 0.3 as a useless signal. S4, denoising reconstruction is carried out on the signals with the NCC larger than 0.3, and the electromyographic signals obtained after denoising processing are obtained. According to the RIME-SVMD-based electromyographic signal denoising method disclosed by the invention, the SVMD maximum equilibrium parameter is solved through the RIME, the SVMD is used for decomposing the original electromyographic signals, and the wavelet threshold method is combined for denoising the useful signals, so that the characteristics of the original electromyographic signals are better reserved for subsequent analysis and judgment.

The invention relates to a special assembly workshop machine and AGV combined scheduling optimization method considering optimal path selection, and the method comprises the following steps: initializing configuration parameters according to a workshop assembly process; a general assembly workshop machine and AGV scheduling scheme is taken as a solution, and a machine and AGV combined scheduling multi-target optimization model considering an AGV transportation optimal path is established; designing a multi-objective learning type artificial bee colony algorithm; and optimizing the scheduling scheme through iterative computation, and finally obtaining a plurality of optimized scheduling schemes which accord with actual production. Compared with a traditional scheduling mode, the method considers the selection of the optimal path during AGV transportation, greatly improves the scheduling efficiency and accuracy of the special assembly workshop, reduces the scheduling time cost and energy cost while providing a reasonable and effective scheduling scheme for workshop scheduling, and improves the scheduling efficiency. The overall production efficiency of an assembly workshop is improved, the order delay condition is reduced, and consumption reduction and efficiency improvement of enterprises are achieved.

The invention discloses an arc-shaped magnetic integrated coupler for a wireless energy supply system and a parameter optimization method, and belongs to the technical field of wireless power transmission. The invention aims to solve the problem that coupling between resonant inductors of a coupler and a main power transmission coil and coupling between the resonant inductors influence the transmission efficiency of a system. According to the coupler, the inner ends of a first transmitting coil P1 and a second transmitting coil P2 are oppositely arranged to form a transmitting end arc-shaped coil, and the inner ends of a first receiving coil S1 and a second receiving coil S2 are oppositely arranged to form a receiving end arc-shaped coil; due to the layout of the integrated resonance compensation inductance coil and the corresponding transmitting end arc coil or receiving end arc coil, the current direction of the integrated resonance compensation inductance coil is perpendicular to the current direction of the transmitting coil or the receiving coil where the integrated resonance compensation inductance coil is located; and at most one integrated resonance compensation inductance coil is arranged on each transmitting coil or each receiving coil. According to the invention, decoupling between the transmission coil and the resonant inductor can be realized.

The invention belongs to the field of digital twinning, and provides a construction method of a crown block motion characteristic digital twinning body, which comprises the following steps of: establishing a Lagrange classical analytical model according to a relation between crown block kinematics and dynamics, and acquiring crown block motion state data in a real working condition environment in real time; through finite element analysis (FEA) and an on-line sensor technology, crown block operation data are collected, a three-dimensional model is established, and simulation is carried out, so that dynamic characteristics such as displacement, speed and swing angle are obtained. And 3, carrying out standardization processing on the collected data, extracting typical motion characteristics by adopting clustering analysis, and calculating the average speed, displacement and acceleration of each cluster. And finally, constructing an equivalent model based on a radial basis function (RBF) neural network, and optimizing system parameters in combination with a particle swarm optimization (PSO) algorithm. The method has strong adaptability and global search capability, can effectively process a complex nonlinear system, and provides a new idea for real-time monitoring and optimization of crown block motion characteristics.

The invention provides a fault diagnosis method for an underwater vector propeller, and relates to the technical field of fault diagnosis. The method specifically comprises the following steps: generating a historical fault data set; preprocessing the historical fault data set; constructing a fusion model of the multi-source heterogeneous data; inputting the trained data into a data fusion model, and outputting a fusion result; inputting data from the training set into three hybrid deep learning models combined with a self-attention module for training, then taking prediction results of the three hybrid deep learning models as input of ensemble learning, and generating a final prediction result by using a weighted ensemble strategy; adding a white whale optimization algorithm of a dynamic pinhole imaging strategy to optimize hyper-parameters in the neural network model; and performing fault diagnosis. According to the technical scheme, the problems that in the prior art, an underwater vehicle is low in reliability and safety, high in maintenance cost and short in equipment service life are solved.

The invention discloses a bridge point cloud extraction method based on evolution gradient search, and relates to the technical field of data processing, and the method comprises the steps: employing a clustering algorithm to carry out the denoising of a bridge surface point cloud; preliminarily extracting the de-noised bridge floor point cloud based on a multi-thread and minimum heap data structure, and meanwhile, setting a perpendicularity density constraint to further extract the bridge floor point cloud; a gradient-based optimization algorithm is combined with an evolutionary strategy to serve as a complementary algorithm to be incorporated into a unified framework, and the extracted bridge deck point cloud is optimized by alternately adopting a gradient updating method and an evolutionary updating method, so that the finally extracted bridge deck point cloud is obtained. According to the method, the processing precision of the bridge deck point cloud is improved, the main body structure of the bridge can be more effectively identified and separated from the complex point cloud data by introducing the evolution gradient search strategy, and meanwhile, the interference of noise and redundant information on a final result is remarkably reduced.

The invention discloses a sensor layout optimization method based on an NSGA-II algorithm, and the method comprises the following steps: firstly, dividing a population into different frontier layers through a fast non-dominated sorting NSGA-II algorithm, and determining and calculating the degree of congestion or the degree of congestion distance to maintain the diversity of frontier layer individuals; secondly, the operation of the NSGA-II algorithm is dynamically adjusted by adopting self-adaptive crossover and mutation operation, so that the adaptability and efficiency of the algorithm are improved; and finally, selecting a better Pareto frontier solution set through selection operation, and providing a group of multi-objective optimal solutions. According to the sensor layout optimization method based on the NSGA-II algorithm, the global search capability is high, exploration in a solution space can be wide, and a potential optimal solution can be found.

The invention discloses an electromagnetic pump and an optimization method, and particularly relates to the technical field of electromagnetic pumps.The electromagnetic pump comprises an inner stator iron core, a first heat insulation layer, a first stainless steel pipe, second stainless steel and a second heat insulation layer which are sequentially and coaxially arranged from inside to outside, and a winding, a cooling pipeline and a plurality of outer stator iron cores are arranged on the outer side of the second heat insulation layer; the outer stator iron cores are uniformly arranged along the radial direction of the electromagnetic pump; the winding is wound on the periphery of the second heat insulation layer; the cooling pipeline is arranged between the winding and the second heat insulation layer and is in contact with the outer stator iron core; the copper pipe is used for cooling the outer stator iron core, the winding and the second heat insulation layer; design parameters of the cooling pipeline are optimized and selected through a genetic algorithm in combination with a finite element model. The technical problem that a conventional electromagnetic pump is low in liquid metal conveying efficiency is solved, the electromagnetic pump capable of efficiently conveying the high-temperature liquid metal is provided, and the conveying efficiency of the electromagnetic pump is greatly improved.

The invention relates to an optimization design method of a multi-winding regulation and control transformer for flexible interconnection, which comprises the following steps: 1, according to a single-target optimization design mathematical model, constructing an optimization target function with the purpose of minimizing the sum of the material cost of the multi-winding transformer and the ten-year operation cost; 2, determining required constraint conditions according to basic electrical parameters of the multi-winding regulation and control transformer for flexible interconnection; under the action of the obtained optimization objective function, punishing a trend violating constraints by adopting a punishment function, so that non-constraint optimization is gradually close to a feasible region, a constraint optimization problem of the distribution transformer is adjusted into an unconstraint optimization problem, and an optimization design model of the amplitude-phase adjustment type multi-winding transformer for flexible interconnection is obtained; and 3, solving the optimal design model, obtained in the step 2, of the multi-winding regulation and control transformer for flexible interconnection by using a particle swarm optimization algorithm. According to the method, the local optimal solution and the global optimal solution of the individual can be solved, and higher solution precision can be obtained.

The invention discloses a particle swarm algorithm-based single intelligent metasurface beam forming method, which comprises the following steps of: firstly, generating an IRS position phase shift vector and a BS position beam forming vector by using a phase regulation matrix at an IRS position and a BS position, taking the generated vector as a single particle, then randomly generating a particle swarm, initializing each particle, calculating the fitness of each particle, and finally forming a single intelligent metasurface beam according to the particle swarm. And updating individual experience and group experience from the fitness, continuously carrying out iterative search to enable an optimal solution with the maximum reachable rate at the user, and recording the optimal solution. According to the method, large-scale global search can be carried out, local search can be finely carried out, the possibility of falling into a local optimal solution is reduced, excellent performance is shown in the non-convex optimization problem, and good performance is achieved in solving the joint optimization IRS and BS beam forming problem.

The invention provides an antibody screening method and system based on a differential evolution algorithm and an artificial intelligence model. The method comprises the following steps: S1, acquiring data of an antibody-antigen compound sequence and affinity; s2, encoding the antibody and antigen sequences by using a language model, and converting the encoded antibody and antigen sequences into a matrix M; s3, training a machine learning model by using the matrix M and the affinity parameters to obtain a trained optimal machine learning model; s4, predicting the antigen affinity of the antibody by utilizing the trained optimal machine learning model; and S5, carrying out affinity optimization on the antibody sequence based on a differential evolution algorithm, and carrying out further filtration by predicting the druggability of the antibody so as to screen the antibody with high affinity and good drug properties.

The invention discloses an integrated positioning method based on spatial spectrum estimation and map matching, and provides a two-stage communication sensing integrated positioning method based on spatial spectrum estimation and map matching to construct a communication sensing integrated cellular system model based on orthogonal frequency division multiplexing. The devices in the system can send and receive communication signal waveforms at the same time so as to complete single-target device positioning. In particular, it is considered that an orthogonal frequency division multiplexing scheme is used for communication signal transmission, and thus the result is compatible with 5G and later cellular networks. In this arrangement, a two-stage positioning frame is proposed. In the first stage, the parallelism, global optimization and simplicity of a genetic algorithm are utilized, a traditional multi-signal classification algorithm is improved, and finally joint estimation of the distance, the direction of arrival (AOA) and the pitch angle of arrival (EOA) of a target is achieved.

The invention discloses a permanent magnet synchronous motor speed regulation control method based on an improved hawk algorithm. The method comprises the following steps: acquiring permanent magnet synchronous motor data; inputting the permanent magnet synchronous motor data into a pre-trained permanent magnet synchronous motor speed regulation control model, and outputting a speed regulation result of the permanent magnet synchronous motor; and the permanent magnet synchronous motor speed regulation control model is trained by using an improved Tianying algorithm. According to the method, the problem of controller parameter adjustment in a permanent magnet synchronous motor speed regulation control model is solved, the improved hawk optimization algorithm is introduced, the algorithm realizes optimal configuration of global search and local search capabilities by virtue of the characteristics of rapid convergence, high global search capability and high solving precision, and the optimal configuration of the global search and local search capabilities is realized in the parameter optimization process. A dynamic simplex method strategy and an annealing simulation-gold sine updating strategy are utilized to expand the optimal selection range, the algorithm shows excellent performance, and the labor intensity of researchers for adjusting parameters is remarkably relieved.

The invention discloses an agricultural machinery intelligent scheduling method and system based on yield prediction, and belongs to the technical field of intelligent agricultural machinery, and the method comprises the steps of data collection, data preprocessing, crop yield prediction, agricultural machinery intelligent scheduling and comprehensive agricultural machinery scheduling. According to the method, crop yield prediction is carried out by adopting the time convolution feature enhanced gated circulation network, so that the method has better long and short term time sequence dependence modeling capability, key features can be adaptively enhanced, the stability and accuracy of crop yield prediction are improved, and the reliability of subsequent agricultural machinery intelligent scheduling is improved; the discrete particle swarm optimization algorithm is adopted to perform agricultural machinery intelligent scheduling, so that the method has relatively strong global search capability, can adapt to different agricultural production environments and scheduling requirements, has significant advantages in large-scale agricultural production, and improves agricultural machinery scheduling efficiency; comprehensive agricultural machinery intelligent scheduling based on yield prediction is adopted, crop yield difference is fully considered, agricultural machinery operation is reasonably arranged, and resource waste is effectively avoided.

The invention discloses a medium and low voltage power distribution network fault reconstruction method based on a particle swarm algorithm. The method comprises the following steps: S101, obtaining a target power distribution network management data set; step S102, determining an island division range by taking each DG as a center and taking the capacity of the DG as a constraint condition, dividing load grades into a first-level load, a second-level load and a third-level load, and numbering load nodes in each divided area from high to low; step S103, according to the target power distribution network management data set, using an optimization algorithm based on a particle swarm, taking the minimum network loss as a target, and taking the bearing capacity of the DG as a constraint condition to carry out islanding solving on the DG, and obtaining load points contained in each islanding and a switch required to be disconnected; and S104, fault reconstruction is carried out according to the island division solving result, and a reconstructed network topology structure and a system fault reconstruction result are obtained.

The invention relates to the technical field of 4D track prediction, in particular to a 4D track prediction method for optimizing a BP neural network based on an improved sparrow algorithm, and the method comprises the following steps: S1, determining a topological structure of the BP neural network; s2, preprocessing the 4D track data; s3, generating an initial weight and a threshold value by using sine chaotic mapping; s4, calculating population fitness by adopting SSA, and updating individual positions according to foraging and anti-predation behaviors of a population of SSA; s5, judging an algorithm termination condition: iterating to the maximum number of iterations or less than or equal to the set convergence precision, if so, continuing the next step, and if not, returning to the step S4 to continue iteration; and S6, substituting the optimal weight and threshold obtained by algorithm optimization into BP for correction and updating, and performing training and simulation prediction again. By combining the sparrow algorithm and the BP neural network, the complex nonlinear relation in the 4D track data can be more accurately captured, and the precision of 4D track prediction can be effectively improved.

The invention relates to a variable-density high-vacuum multi-layer heat insulation structure and an optimization method thereof, and the optimization method comprises the following steps: determining design parameters and performance indexes of the variable-density high-vacuum multi-layer heat insulation structure, and carrying out the functional expression of each performance index based on the design parameters, so as to obtain a simulation function of each performance index; and based on the simulation function of each performance index, establishing an optimal arrangement problem of the variable-density high-vacuum multi-layer heat insulation structure, and solving the optimal arrangement problem by adopting a genetic algorithm to realize optimization of the variable-density high-vacuum multi-layer heat insulation structure. When the genetic algorithm is adopted to solve the optimal arrangement problem, population individuals are generated based on the number of heat insulation layers among all reflecting screens of the variable-density high-vacuum multi-layer heat insulation structure, and a heat flow density function of the variable-density high-vacuum multi-layer heat insulation structure serves as a fitness function. Compared with the prior art, the method has the advantages of effectively improving the heat insulation performance, being high in efficiency and the like.

According to the wind-light storage coordinated configuration method based on the K-means and the simulated annealing genetic algorithm, the K-means algorithm is adopted to process initial data, then a wind-light storage configuration model is constructed, and finally the simulated annealing genetic algorithm is adopted to solve. According to the simulated annealing genetic algorithm, the advantage of strong local search capability of the simulated annealing algorithm and the advantage of strong global search capability of the genetic algorithm are combined, and the advantages can be mutually complemented, so that the precision and optimization speed of the algorithm are improved. By testing the wind and light energy storage configuration of a certain park, a good result is finally obtained, the investment cost is the lowest while wind and light curtailment is reduced, and an effective planning scheme is provided for the micro-grid.

The application provides a UWB and PDR fusion positioning method and system in an indoor shielding environment and a medium, and belongs to the technical field of indoor positioning and multi-source information fusion. The method comprises the following steps: calculating a UWB positioning track and a PDR positioning track. Taking translation, rotation, step scaling and scale change as combined parameters, a track alignment error function is constructed by using the Euclidean distance of the two tracks at each timestamp. An improved vole optimization algorithm is used to solve the optimal combined parameters. An initial population with uniform distribution is generated by mapping a Sobol difference sequence. In the iteration, the UWB track is used as the reference, the PDR track is temporarily transformed by using the combined parameters, the Euclidean distance is calculated, and the vole with the smallest distance is selected to breed the next generation. After reaching the maximum number of iterations, the PDR track is corrected by using the optimal combined parameters at one time, and the UWB track is fused and output. The application does not require a priori model, can simultaneously suppress UWB non-line-of-sight drift and PDR cumulative error, and has high positioning accuracy.

The invention discloses a method for identifying thermal runaway reaction kinetic parameters of a high-nickel ternary battery based on DSC (differential scanning calorimetry) multi-exothermic peak heat flow curves, which is characterized by comprising the following steps of: establishing a chemical reaction kinetic model, and identifying the thermal runaway reaction kinetic parameters of the high-nickel ternary battery based on the DSC multi-exothermic peak heat flow curves at different temperature rise rates; and realizing identification of the thermal runaway reaction kinetic parameters of the high-nickel ternary battery by applying a Kissinger equation and a particle swarm optimization algorithm. According to the technical scheme of the method, necessary reaction kinetic parameters can be provided for thermal runaway modeling of the high-nickel ternary battery, compared with a traditional genetic algorithm, the convergence speed is higher, meanwhile, the global search capability is high, thermal contribution decoupling between different reactions of a battery material in the thermal runaway process is achieved, and the thermal runaway modeling efficiency is improved. And improvement means can be researched aiming at high-yield heat reaction among materials, and the risk of thermal runaway of the high-nickel ternary battery is reduced.

The invention relates to the field of computer network optimization and controller load balancing, and discloses a controller load balancing method based on hierarchical load awareness and adaptive heredity, which comprises the following steps: S1, constructing an SDN network model, establishing a mapping matrix of a switch and a controller, establishing a switch flow model, and establishing a flow model of the controller; s2, establishing a load model of the controller, setting a dynamic load formula and a static load formula of the controller, and calculating the total load of the controller; s3, establishing a layered load sensing model, and determining whether to trigger load balancing or not according to a preset threshold value; s4, optimizing a mapping relation between the switch and the controller by using an adaptive genetic algorithm to realize dynamic load balancing; and S5, setting an algorithm ending condition, outputting an optimal mapping scheme, and carrying out load balancing according to the scheme.

The invention provides a consultation service system based on big data collection and analysis, and the system comprises a data collection module which is used for obtaining consultation service data from a plurality of data sources, and obtaining original consultation service data; the data cleaning and preprocessing module is used for cleaning and preprocessing the collected original consultation service data to obtain preprocessed data; the data analysis and mining module is used for analyzing and mining the preprocessed data to obtain feature data; and the decision support module is used for generating a decision suggestion report by utilizing the feature data. According to the technical scheme, the data acquisition module, the data cleaning and preprocessing module, the data analysis and mining module and the decision support module are arranged, so that the quality and efficiency of consultation service are improved.

The invention provides a substation project multi-objective optimization method and device. The method comprises the following steps: acquiring a process set, a process preposition and postposition logic relation, selectable construction modes of each process and corresponding parameter information; introducing the logical relationship as a constraint, taking a construction mode selection result as a discrete decision variable, and constructing a multi-objective optimization model including a construction period, a cost present value, quality and a carbon emission objective; an improved swarm intelligence algorithm is adopted for iterative solution and layered sampling to generate an initial population and establish an external file, a target function is calculated in iteration and feasibility is judged, non-dominated sorting and congestion degree are executed after merging, the external file is updated, and a leader individual is selected; and candidate positions are generated based on nonlinear convergence control and hunting, random search and spiral bubble net updating, discretization and constraint restoration are performed after fusion, and an external file non-dominated solution set is output. According to the technical scheme, multi-target scheme optimization is achieved, and scheme generation efficiency and evaluation quality are improved.

The invention relates to the technical field of automatic control or adjustment, and further relates to an efficient separation method for preparing an n-alkane phase change material from coal-based oil. The method comprises the following steps: 1, pretreating coal-based oil, decomposing components of the coal-based oil by utilizing a multi-stage pyrolysis reaction, and calculating the concentrations of various products; 2, inputting the concentration of each product into the optimization model; the optimization model optimizes the reaction temperature and the reaction pressure of the separation tower to obtain an optimized reaction temperature and an optimized reaction pressure; 3, based on the optimized reaction temperature and the optimized reaction pressure, solvent selection, extraction temperature and extraction pressure in the supercritical fluid extraction process are optimized, and the extraction efficiency is maximized. The separation conditions are optimized through the technologies such as the multi-stage pyrolytic reaction, the Langmuir adsorption model, the supercritical fluid extraction and the particle swarm optimization algorithm, the separation efficiency and the purity of the n-alkanes are improved, and the energy consumption is reduced.