402 results about "Selection (genetic algorithm)" patented technology

The invention discloses a network edge monitoring and early warning method based on video image AI analysis, and the method comprises the following steps: S1, obtaining video data, processing the video data, and generating an image frame sequence; s2, analyzing an image frame sequence, and extracting space and time features; s3, taking the target as a hypergraph node, constructing a hyperedge based on space and time features, and dynamically adjusting hyperedge connection by using a genetic algorithm; s4, constructing a multi-layer hypergraph Transform network, extracting multi-scale spatial-temporal characteristics, and calculating a semantic relationship between nodes; s5, setting a butterfly optimization algorithm initial population, and dynamically optimizing model parameters through global and local search; s6, constructing an anomaly detection model, identifying an abnormal behavior, and feeding back a result to optimize model parameters and hyperedge selection; and S7, deploying the model at an edge node, triggering early warning when an abnormal behavior is detected, and pushing information to a management platform. According to the invention, through video image AI analysis, accurate detection and real-time early warning of abnormal behaviors in a network edge scene are realized.

The invention discloses a method for intelligently regulating and controlling production parameters in a fruit concentrated juice production process, which comprises the following steps of: acquiring multi-dimensional process parameters such as temperature, pressure, flow, concentration, equipment state and the like in real time through a multi-channel sensor network, and forming a standardized data sequence after filtering, normalization and drift correction; extracting stage features by using technologies such as a sliding window and Fourier transform, and inputting the stage features into the lightweight classification model to realize production stage identification; in combination with an identification result, dynamically calling a corresponding multi-target optimization sub-model, and realizing nonlinear prediction and optimal solution selection of process parameter setting by adopting an LSTM and a multi-target genetic algorithm; on the basis of real-time feedback, the performance of the model is automatically evaluated, self-adaptive adjustment and optimization of the optimization algorithm are achieved through reinforcement learning and an incremental updating mechanism, multi-target collaborative optimization, self-adaptive adjustment and model switching in the production process can be achieved, and the consistency of production efficiency and product quality is improved.

The invention belongs to the technical field of industrial intelligence, and relates to a deep reinforcement learning-based non-correlation parallel machine scheduling method, which comprises the following steps of: constructing a mathematical model suitable for non-correlation parallel machine scheduling; the machining time of each workpiece on each heterogeneous machine is collected, and normalization processing is carried out; initializing a genetic algorithm scheduling population and a depth Q network; constructing a deep reinforcement learning training framework; expressing a state vector by using the average fitness, the optimal fitness and the optimal individual code; operating parameters of the genetic algorithm are controlled by using the action space, and parameters of the deep Q network are updated by using a reward function; and dynamically controlling operator selection in a genetic algorithm iteration process by using the trained deep reinforcement learning model to obtain an optimal scheduling solution and realize scheduling of the non-correlation parallel machine. According to the method, a deep reinforcement learning algorithm is provided for solving similar problems in the manufacturing industry production scheduling field by analyzing a non-correlation parallel machine scheduling problem data model, and the production efficiency is improved.

The invention discloses a ship power system optimization scheduling method and system based on a genetic algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining real-time operation parameters of a ship power system, building an initial population of the genetic algorithm based on the operation parameters, and carrying out the optimization scheduling of the ship power system based on an energy consumption characteristic index and an emission characteristic index. And calculating the comprehensive fitness value of each individual, performing selection operation, interlace operation and mutation operation on the initial population to generate a new population, repeatedly performing population iterative optimization until a preset condition is met, outputting a navigational speed adjustment parameter combination, a generator set start-stop decision sequence and a power distribution scheme, and generating a ship power system control instruction set. According to the ship power system optimization scheduling method and system based on the genetic algorithm provided by the invention, the operation efficiency of the ship power system is improved, the energy consumption is reduced, and the carbon emission is reduced.

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention relates to an intelligent power grid cooperative scheduling method for automobile access, and relates to the field of electric automobile charging scheduling and intelligent power grid optimization. The method comprises the following steps: acquiring multi-source real-time data of an electric vehicle, a power grid and a charging station, constructing a collaborative scheduling graph structure, and performing multi-target optimization to generate an initial charging guide strategy and a charging station resource allocation scheme; then, reinforcement learning iteratively optimizes the guide strategy to dynamically adapt to environmental changes; further optimizing charging time and power parameters through a dynamic adaptive optimization algorithm, and realizing charging station congestion early warning and selection suggestions in combination with a probability prediction algorithm; a charging station power distribution strategy is optimized based on early warning information, fine optimization is carried out by adopting a genetic algorithm, and a scheduling scheme is evaluated and adjusted in real time through a feedback control algorithm. According to the invention, dynamic, refined and global optimization of electric vehicle charging scheduling is realized, the stability of a power grid, the operation efficiency of a charging station and the charging experience of a user are improved, and new energy consumption is promoted.

The invention relates to the technical field of air conditioner fan blade design optimization, and discloses an air conditioner fan blade optimization method and system based on a BP neural network and a GA algorithm. The method comprises the following steps: obtaining initial geometric parameters and performance data of a fan blade, and cleaning and standardizing the initial geometric parameters and the performance data to form a standard data set; a BP neural network is used for training to obtain a fan blade performance prediction model; a genetic algorithm is applied to optimize the prediction model, and a new design parameter population is generated through genetic operations such as selection, crossover and variation; the optimized parameters are input into a CAD system to generate a candidate fan blade model, numerical simulation is carried out, and performance indexes of the candidate fan blade model are calculated; and screening excellent individuals based on a multi-objective optimization method, iteratively executing optimization and simulation processes until convergence, and finally outputting an optimal fan blade design. According to the method, the fast prediction of the neural network and the global search capability of the genetic algorithm are combined, the dependence of traditional optimization on high-frequency numerical simulation is reduced, and the design efficiency and quality are improved.

The invention discloses a digital automobile maintenance and repair market intelligent operation system, particularly relates to the field of automobile maintenance and repair, and comprises a database, a vehicle information acquisition module, a data preprocessing module, a repair auxiliary diagnosis module, a work order intelligent scheduling module and a user information terminal. The maintenance auxiliary diagnosis module constructs a fault diagnosis model based on a vehicle data processing text and a historical maintenance case library, the accuracy and efficiency of fault diagnosis are improved, a maintenance work order is generated according to a diagnosis result and an association rule table, accurate selection of accessories needed by maintenance is ensured, and the maintenance time and cost are reduced; according to the intelligent work order scheduling module, the objective function and the genetic algorithm are constructed, related variables are comprehensively considered, reasonable distribution of maintenance work orders is achieved, resource utilization of maintenance enterprises is effectively balanced, work order waiting time and total maintenance time are shortened, and overall operation efficiency is improved.

The invention provides an evaluation method and system based on soil buffer capacity, and relates to the technical field of soil evaluation.The method comprises the steps that a microbial community diversity index is coded to serve as a genotype of a genetic algorithm; randomly generating an initial population, wherein each individual represents one environment factor combination; calculating the fitness value of each individual, carrying out selection, crossover and mutation operations, and repeating genetic operations until a termination condition is met so as to obtain a final key environment factor combination; obtaining microorganism activity data according to the soil respiration rate in the microorganism sample; and according to the final key environment factor combination, the microbial activity data and the trained neural network model, predicting a soil buffer capability value. According to the method, the buffer capacity of the soil can be evaluated more accurately.

The invention discloses a ground-air cooperative fire barrier opening method based on digital twinborn and neural networks, and belongs to the technical field of forest steppe fire prevention. Geographic information, weather and fire monitoring data are collected and preprocessed, and a digital twinborn model is constructed to simulate meteorological elements. And then constructing an evaluation index system to evaluate the fire risk, and dividing risk grades. An isolation belt is planned by adopting a genetic algorithm, and a parameter combination is explored through selection, crossing and mutation operations. During air-ground collaborative operation, the unmanned aerial vehicle guides construction, the helicopter lifts materials, and the scheme is monitored and adjusted in real time. The fire retardant effect is evaluated through simulation and actual data, the efficiency and the cost effectiveness are set up, and all links are optimized according to results. According to the method, the fire risk is accurately assessed, the isolation belt is scientifically planned, the ground-air collaborative operation efficiency is improved, dynamic monitoring and optimization are realized, the cost is reduced, and an effective means is provided for forest steppe fire prevention and control.

Disclosed are a genomic mating method for Huaxi cattle based on whole genome single nucleotide polymorphism (SNP) information and an application thereof. The method includes the following specific steps: step 1, extracting deoxyribonucleic acid (DNA) from to-be-hybridized Huaxi cattle individuals for genotyping; step 2, performing genotype data imputation to obtain high-density chip data; step 3, calculating an additive genetic relationship matrix, utilizing genomic best linear unbiased prediction (GBLUP) to obtain genomic estimated breeding values of five important economic traits of a to-be-hybridized Huaxi cattle population, and calculating a comprehensive selection index of the individuals; and step 4, using a genetic algorithm to construct a population optimal mating combination list. In the present invention, the breeding cost is greatly saved and an inbreeding level of offspring populations is reduced.

The invention discloses an improved NSGAII-based satellite ground station optimization layout method and device, and relates to the technical field of satellite ground station optimization layout, and the improved NSGAII-based satellite ground station optimization layout method mainly comprises the steps: generating an initial population according to a satellite ground station optimization layout problem parameter, and an optimization evaluation system is constructed, an improved non-dominated sorting genetic algorithm II is utilized to obtain a preliminary offspring population, a dynamic congestion degree selection mechanism is utilized to obtain an offspring population until the maximum number of iterations is reached, and a final optimization result is obtained. By implementing the satellite ground station optimization layout method and equipment based on the improved NSGAII provided by the invention, the problem of ground station layout optimization under double indexes of satellite-ground connectivity and ground station flow load balance can be solved.

The invention discloses a reconfigurable flexible job shop scheduling optimization method with secondary clamping constraint, and relates to the technical field of intelligent manufacturing and production optimization. The method comprises the following steps of: 1) establishing a mixed integer linear programming model considering a reconfigurable flexible job shop scheduling problem of secondary clamping by taking minimization of maximum completion time and minimum number of chemical workers as targets; 2) designing a three-segment coding mode and a decoding mode corresponding to the mixed integer linear programming model based on process sorting, machine selection and worker selection; and 3) based on the three-segment coding mode and the decoding mode, adopting an improved multi-target genetic algorithm to solve an optimal scheduling scheme of the mixed integer linear programming model. According to the method, processing machine selection, auxiliary module selection, processing sequence sorting and secondary clamping worker selection of a manufacturing workshop can be considered at the same time, the workshop production efficiency is improved, and the method has the advantages of being good in model performance, small in result fluctuation and high in stability.

The invention relates to the technical field of intelligent manufacturing, and particularly discloses a space-time constraint modeling-based multi-AGV-mechanical arm collaborative carrying system, which comprises a space-time constraint modeling module, a task decomposition and priority evaluation module, a collaborative path planning module, a dynamic collision prediction and adjustment module and an energy consumption and efficiency optimization module, according to the method, the total energy consumption, the total duration and the congestion index are subjected to normalization weighting, the unified fitness function is constructed, and the genetic algorithm and the particle swarm optimization are adopted for joint optimization, so that energy consumption minimization and operation period minimization can be taken into consideration at the same time, and a high-congestion section is actively avoided on path selection. According to the multi-target fusion, side effects caused by single index optimization are avoided, the sustainable operation capacity of the system is improved, the actual time length fed back after task execution, energy consumption and collision near-loss information are improved, model parameters are updated through a reinforcement learning or online incremental learning module, and self-evolution of a strategy is achieved.

The invention discloses an unmanned driving extreme case generation and verification method based on a simulation engine. The method comprises the following steps: S1, constructing a high-fidelity virtual simulation environment; s2, initializing a parameterized model generated by an extreme driving case; s3, generating an initial extreme driving case population based on a genetic algorithm; s4, collecting test result data of the unmanned driving system; s5, quantifying the test effect of each extreme driving case; s6, based on a fitness evaluation result, performing selection, crossover and mutation operation through a genetic algorithm; and S7, the extreme driving case population generated by evolution is subjected to unmanned driving system testing again in the simulation environment, S4 to S6 are executed repeatedly until a preset termination condition is reached, and the termination condition comprises that the scene diversity reaches a target value or the fitness is converged. According to the invention, the generation efficiency and coverage range of the extreme driving scene are improved.

The invention belongs to the technical field of petroleum and natural gas drilling engineering, and particularly relates to a borehole friction coefficient segmented inversion method based on a genetic algorithm and string mechanics, which comprises the following steps of: acquiring specified data of a construction well, dividing a borehole of the construction well to be predicted into well sections with fixed lengths, defining depth ranges of the well sections, and setting a global search range of a friction coefficient; initializing the population size, the maximum number of iterations, the selection probability, the crossover probability and the mutation probability of the genetic algorithm, and performing downward inversion section by section from a wellhead section: initializing a candidate friction coefficient population of a current well section, setting a sectional friction coefficient, calculating a hook load at the tail of the current section, and calculating the maximum number of iterations; iterative optimization is carried out until a termination condition is met, and then the optimal friction coefficient of the section is output and fixed; and iteratively executing until inversion of all well sections is completed, outputting a full-borehole segmented friction coefficient profile, and realizing intelligent segmented inversion of the borehole friction coefficient by combining a tubular column mechanical model and a genetic algorithm, so that the prediction precision of the friction coefficient is improved, and the well drilling design and construction are optimized.

The invention belongs to the field of heat exchange equipment design, particularly relates to a heat exchanger optimization design system based on a genetic algorithm, and aims at solving the problem that heat exchange equipment designed by an existing design system for designing a shell-and-tube heat exchanger is low in performance. The heat exchanger optimization design system based on the genetic algorithm comprises a design index input module and a heat exchanger parameter optimization module. According to the system, seven key design parameters including the tube pass number, the tube outer diameter, the heat exchange tube length, the tube inner flow speed, the shell side flow speed, the tube wall thickness and the heat exchange tube number serve as optimization variables, and a fitness function with the heat transfer coefficient and pressure drop ratio as the target is established. A hybrid coding strategy is introduced to carry out differentiation processing on continuous parameters and integer parameters, an improved genetic algorithm is adopted to realize iterative evolution of a population through intelligent optimization operations such as coding, selection, crossover and variation, and finally, a global optimal parameter combination meeting engineering constraint conditions is converged.

The invention discloses a wind power generation tower drum steel-concrete composite structure design method based on genetic algorithm optimization. The method specifically comprises the steps that operation data and design parameters of different types of wind power generation tower drums under different working conditions are obtained to construct an original data set; secondly, determining a design target function and quantifying, and setting each target weight coefficient; a genetic algorithm population is initialized, and individuals are represented by design parameter codes; decoding individuals of the population to obtain an actual design scheme, performing structural mechanical analysis, and calculating mechanical property indexes; calculating an individual fitness value according to the target function and the weight coefficient; new individuals are generated through selection, crossover and mutation operation, whether termination conditions are met or not is judged, if yes, the design scheme corresponding to the individual with the highest fitness is output, and if not, iterative optimization continues. According to the method, the genetic algorithm is used for iterative optimization, the comprehensive performance of the design scheme of the steel-concrete composite structure of the wind power generation tower under multiple targets is effectively improved, and a better design result is obtained.

The invention discloses a heterogeneous cluster task scheduling fusion method based on Q learning and a genetic algorithm, and belongs to the technical field of intelligent scheduling. Collaborative optimization is carried out through a dynamic feedback mechanism of Q-Learning and the global search capability of the genetic algorithm: in an initialization stage, a cluster state space and an action space are defined, and a multi-Q-value table and an initial population are generated; during task scheduling, nodes are selected based on a # imgabs0 #-greedy strategy, reward values are calculated, and a Q value table is updated; meanwhile, the scheduling scheme is coded into chromosomes, a new population is generated through roulette selection, crossover and variation, and a Q value table is optimized; in the iteration process, the convergence is judged according to the Q value change or the population fitness change, and the strategy is dynamically adjusted. According to the method, the advantages of double algorithms are fused, local optimum is avoided, the task processing efficiency and the resource utilization rate are remarkably improved, adaptive strategy updating during cluster state change is supported, and the method is suitable for an efficient task scheduling scene of a large-scale heterogeneous cluster.

The invention discloses a tunnel smooth blasting parameter selection method, which aims at the tunnel back-break and back-break problem and comprises the following steps of: constructing a blasting parameter optimization model by determining the back-break and back-break amount minimization as a target function and taking the tunnel section size, the lithology grade and the geological condition as constraint conditions; an improved genetic algorithm which introduces dynamic constraint, hierarchical coding and multi-stage fitness evaluation is utilized, and optimized blasting parameters under the minimum back break amount are screened out through continuous selection, intersection and mutation operations. Compared with a traditional genetic algorithm, global convergence and parameter practicability are improved, and meanwhile the problems of premature convergence and invalid solutions of the traditional genetic algorithm are solved.

The invention discloses a photovoltaic power generation power prediction and power system scheduling method and a system for realizing the method. The method comprises four steps of data acquisition and preprocessing, similar day selection and training set construction, GA (Genetic Algorithm)-fuzzy RBF (Radial Basis Function) neural network modeling and power scheduling plan generation. The method comprises the following steps: selecting a meteorological variable with high correlation degree through a Spearman rank correlation coefficient, and constructing a time sequence feature; similar days are utilized to construct training samples, and the generalization ability of the model is improved; a fuzzy RBF network optimized by GA is adopted to enhance the nonlinear modeling precision; and inputting a prediction result into the power system simulation model, and generating an optimal scheduling instruction by adopting dynamic programming. According to the method, the photovoltaic power prediction accuracy and scheduling efficiency can be remarkably improved, the power grid stability is enhanced, and the new energy consumption capability is promoted.

The invention provides a redundant backup method and system for a communication bus in track type gravity energy storage, and relates to the technical field of intelligent control, and the method comprises the steps: carrying out the dynamic optimization of decision parameters in a fuzzy logic decision mechanism through employing a genetic algorithm based on an obtained original link state data set, and generating an optimized decision parameter set; according to the genetic algorithm, historical fault state data and a corresponding final switching decision are used as training samples, and decision parameters are iteratively optimized through selection, crossover and mutation operations; and based on the generated optimized decision parameter set, performing fuzzy logic decision processing on the currently collected state data of the main and standby links, and when the health state evaluation value of the main link is lower than a preset threshold value, triggering a millisecond switching instruction. According to the invention, fault processing of the communication link can be completed without manual intervention, and the intelligent level of the system is improved.

The invention discloses an unmanned aerial vehicle cluster task allocation method based on a large language model optimization genetic algorithm, and belongs to the field of computers. The method comprises the following steps: setting a specific chromosome coding mode; generating a multi-constraint initial population; calculating fitness to quantify the advantages and disadvantages of individual genes of the population; when the optimal individual meets the requirement or the maximum iteration round is reached, ending; retaining the optimal individual as a filial generation; generating a batch of new filial generation individuals by the large language model, and fusing the new filial generation individuals with the current filial generation population; calling an optimized large language model to analyze individual chromosome semantics, and outputting an evolutionary potential score; obtaining an individual comprehensive selection probability by integrating the fitness and the evolution potential score, and executing a selection operation; and selecting individuals based on the individual comprehensive selection probability to carry out crossover and mutation operation to generate offspring. The large language model is embedded into the core link of the genetic algorithm, and the algorithm efficiency is improved by improving the population diversity of the genetic algorithm in the unmanned aerial vehicle cluster task allocation scene.

The invention discloses a Xinanjiang model parameter automatic calibration method based on a dual-objective optimization genetic algorithm, relates to the technical field of genetic algorithms and Xinanjiang models, and aims to solve the problems that a Xinanjiang three-water-source model has many parameters, many influence factors and large solution space, and a traditional method cannot well solve the problems. M points are randomly selected from a search space of Xinanjiang model parameters, the m points are coded, and a population is initialized; establishing a target function based on the error index, and obtaining the fitness of population individuals through the target function; carrying out evolution operation on the population through selection, recombination and variation to obtain breeding offspring; and performing reinsertion operation on the obtained breeding offspring to complete automatic calibration of the parameters of the Xinanjiang model, and completing the automatic calibration of the parameters of the Xinanjiang model. Individuals most suitable for the environment are obtained, and flood forecasting and water resource management are carried out.

The invention discloses a workshop layout optimization method for a discrete manufacturing system, and aims to construct a multi-row facility layout mathematical model considering a channel loading and unloading point mechanism aiming at the process storage behavior and cross-row logistics path problems in the manufacturing process. According to the method, on the basis of analysis of workshop parameters and logistics paths, reasonable assumed conditions are set, decision variables such as facility space positions and arrangement relations are defined, a constraint system meeting uniqueness, non-overlapping performance and boundary limitation of facilities is established, and material handling cost minimization is taken as a target. And a Manhattan distance and a Floyd algorithm are adopted to measure the same-row transportation distance and the inter-row transportation distance respectively. In order to solve the model, an improved genetic algorithm fusing bidding selection and a random immigrant mechanism is designed, and the algorithm convergence performance and the global search capability are improved. According to the method, the inter-bank logistics cost can be remarkably reduced, the compactness of the facility layout and the path continuity are optimized, and the method is suitable for manufacturing scenes with process storage characteristics such as welding, assembling and warehousing transfer.

The invention provides an AI-based urban governance application system intelligent generation method and system, and belongs to the technical field of application system generation. The method comprises the following steps: acquiring urban governance multi-source data, and extracting spatio-temporal characteristics to construct a digital twinborn model; converting front and rear end components into gene sequences through gene coding, and constructing a component gene pool; inputting urban governance scene generation component requirements, and constructing a multi-target fitness function and a genetic operator by using a multi-target genetic algorithm; establishing a reinforcement learning agent in combination with an optimization target of the function, and performing collaborative search and local optimization through a genetic operator and the agent to obtain an optimal component combination; optimal component combinations are distributed through a federated learning framework, evaluation model parameters are aggregated, and a component quality evaluation standard is constructed; operating data are collected on line, the using effect is evaluated, and the component gene pool and the digital twinborn model are updated. According to the invention, the assembly selection intelligence of the urban governance application system is realized, and the assembly selection search efficiency is improved.

The invention discloses an electronic circuit energy efficiency optimization design method based on a genetic algorithm, and the method comprises the following steps: S1, carrying out the modeling of a to-be-optimized electronic circuit, forming a mixed type chromosome, and initializing a genetic algorithm population; s2, constructing a fitness function based on a genetic algorithm population; s3, executing a selection operation on the current population, and replacing a parent by a child with high adaptability; s4, performing crossover operation on the parent individuals to generate new offspring individuals; s5, performing mutation operation on the offspring individuals to form new individuals; s6, performing circuit simulation on the new individual after the genetic manipulation is executed, and inputting a simulation result into a fitness function for evaluation; s7, updating the population according to the fitness score, and entering next-generation evolution; and S8, configuring an individual parameter which meets a convergence condition and has the highest output fitness score. Based on a local competition algorithm, a double-layer block recombination strategy and a Pareto frontier algorithm are fused, and energy efficiency optimization of the electronic circuit is achieved.

The invention relates to a communication network base station deployment method and system based on a bilevel programming model. The communication network base station deployment method based on the bilevel programming model comprises the following steps: generating a plurality of alternative measurement and control stations in a discretization manner in a station distribution area according to a preset station distribution area and a satellite elevation angle threshold value; based on ephemeris data of a target satellite, calculating a visible time window between each alternative measurement and control site and the target satellite, and generating a visible arc segment set; based on a genetic algorithm, M measurement and control stations are iteratively selected from the alternative measurement and control stations to serve as a station combination, and M is a preset station distribution number; according to the communication network base station deployment method and system based on the bilevel programming model, the alternative stations are generated at the grid points by leaving the station distribution area, and the station distribution problem is converted into the permutation and combination problem for selecting the stations. And a nested genetic algorithm is applied to finish specific selection of sites.

The invention discloses a dielectric constant prediction method and system and a network training method and system. The method comprises the following steps: acquiring material characteristics of a to-be-tested material; constructing a plurality of descriptors according to the material characteristics of the to-be-tested material; screening the plurality of descriptors through a genetic algorithm, determining M main descriptors, and determining the descriptors except the main descriptors as other descriptors; and inputting the main descriptor and other descriptors into a preset dielectric constant prediction model, and outputting dielectric constant prediction data. The dielectric constant of the microwave high-frequency material is efficiently and accurately predicted through the machine learning technology. Compared with a traditional neural network model and descriptor selection method, the prediction precision, the model efficiency and the generalization ability are remarkably improved, and the problem that high-dimensional descriptors are not matched with small data sets is solved.

The invention relates to a low-temperature high-strength steel lining polytetrafluoroethylene material based on a novel plasticizer and a preparation method of the low-temperature high-strength steel lining polytetrafluoroethylene material, and belongs to the field of anticorrosive materials. The method comprises the following steps: constructing a multi-dimensional associated training set by taking a historical plasticizer component ratio, tensile strength, elongation at break and temperature resistance indexes of a material and sizes and functional parameters of different products as input; by defining a multi-objective fitness function, synthesizing the material strength, low-temperature toughness and cost economy, utilizing the global search capability of genetic algorithm selection, crossover and mutation operation, and combining with a simulated annealing algorithm, the local optimization characteristic of collaborative iterative optimization is realized by dynamically adjusting the inferior solution accepting probability, and the optimal proportioning scheme of the plasticizer is gradually approached. According to the SA algorithm, global exploration and local development are balanced through a temperature attenuation mechanism, the GA premature convergence problem is effectively avoided, the robustness of a matching scheme is improved, and the efficiency and precision of material performance optimization are remarkably improved.