110 results about "Chromosome (genetic algorithm)" patented technology

The invention discloses a satellite beam scanning scheduling method and device and a storage medium. The method belongs to the field of satellite communication, and comprises the following steps: acquiring parameter characteristics of coverage areas of multiple beams of a satellite; inputting the parameter features into a pre-trained neural network model, and determining sub-bands adapted to the plurality of beams through the neural network model; a chromosome vector is initialized, an initial population is constructed, and the chromosome vector is used for indicating the beam position scanned by each beam of the satellite in each beam hopping time slot of the beam hopping period; constructing a fitness function; performing iterative optimization on the initial population according to a genetic algorithm, and determining an optimal chromosome vector; and according to the optimal chromosome vector, determining the beam position scanned by each beam in each beam hopping time slot of the beam hopping period. Therefore, coverage overlapping and sidelobe leakage interference of same-frequency beams are avoided to the maximum extent from the scanning time sequence dimension, and dual targets of sub-band adaptation and interference suppression are synchronously achieved.

The invention discloses an industrial chain typical power utilization mode extraction method fusing heredity and a K-Means clustering algorithm, and belongs to the technical field of power system data analysis and mode recognition. The method comprises the following steps: firstly, carrying out standardized preprocessing on power consumption data, wherein the standardized preprocessing comprises missing value filling, abnormal value processing, feature normalization and redundant feature elimination; then optimizing a K-Means initial clustering center by using a genetic algorithm, and obtaining an optimal initial centroid through chromosome coding, fitness function evaluation and genetic manipulation; and finally, K-Means clustering is executed, and typical power utilization characteristics of each cluster are extracted. According to the method, the problem that a traditional K-Means algorithm is sensitive to an initial value and is prone to falling into local optimum is effectively solved, the clustering quality is remarkably improved, the DB index is reduced by about 15%, and the extracted power consumption mode is highly matched with actual production characteristics. Experiments show that the load prediction error based on the extraction mode of the method is averagely reduced by more than 8%, and reliable technical support is provided for power load prediction, energy efficiency evaluation and intelligent scheduling.

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 bridge splicing part route optimization design method based on a constraint condition genetic algorithm, and relates to the technical field of bridge engineering design, and the method comprises the steps: building a hard constraint and soft constraint double-layer constraint system based on a standardized data set and a parameterized model in a step 2, and associating the system to fitness calculation of chromosomes; and based on a constraint processing result, integrating multi-objective optimization requirements, and constructing a fitness function. According to the method, a route is abstracted into a third-order cubic B-spline curve and parameterized coding is carried out, iteration is carried out in combination with a genetic algorithm, a design space can be globally explored in an optimizable control point range, and compared with traditional experience design, a better global solution can be found, and the problem of local optimum caused by experience limitation of engineers is avoided. The fitness function realizes multi-target quantitative integration through normalized indexes and adjustable weights, the weights of cost, smoothness and environmental influence can be flexibly adjusted according to project scenes, the problems of multi-target qualitative and difficult comparison in traditional design are solved, and the scheme adaptability is remarkably improved.

The invention provides a discrete mixed job production line scheduling method based on an improved genetic algorithm, and relates to the technical field of industrial automation and production scheduling, and the method comprises the steps: S1, carrying out the modeling and data input of a production line scheduling problem, constructing a flexible job shop scheduling model, defining a decision variable, a constraint condition and a target function, and inputting basic data; s2, generating an initial population by adopting a hybrid initialization strategy, wherein the initial population comprises randomly generated individuals and individuals generated based on a heuristic rule; s3, chromosome coding is carried out on the scheduling scheme in a two-segment coding mode, wherein a process sorting segment and a machine distribution segment are included; s4, the fitness is calculated, and individual selection is carried out by adopting a tournament selection method; s5, executing improved genetic operations including adaptive crossover and mutation operations; s6, carrying out local search on the elite individuals, wherein the local search comprises key path identification and neighborhood disturbance; and S7, judging a termination condition, if the termination condition is met, outputting an optimal scheduling scheme, otherwise, returning to the step S4.

The invention discloses a horizontal centrifuge real-time PID gain optimization control method based on an improved adaptive genetic algorithm. The method comprises the steps that a chromosome population is defined based on PID gain of a horizontal centrifuge; constructing a first fitness function based on the first index of the horizontal centrifuge, and calculating the fitness of individuals in the chromosome population; and screening parent individuals by using a champion for genetic generation of offspring, obtaining the crossover rate and mutation rate of the individuals based on the fitness of the individuals in the genetic process, repeating the genetic process until convergence, and applying the PID gain corresponding to the current optimal individual to control the horizontal centrifuge. In the process, the parent is screened, and the genetic crossover rate and the mutation rate are adaptively adjusted, so that the PID parameters are converged more quickly, and the robustness is high.

The invention relates to a multi-process-route-oriented production scheduling integrated optimization method, and relates to the technical field of flexible workshop multi-target scheduling. The method comprises the steps that firstly, a production line process planning and production scheduling integrated optimization model is constructed, the model adapts to a machining sequence flexible scene, and optimal process route determination and procedure machining equipment distribution are synchronously achieved; secondly, double optimization targets are set, namely, the maximum completion time is minimized, and the equipment utilization rate is maximized; thirdly, designing an optimization solution process based on a genetic algorithm framework, generating a chromosome population containing a process route selection machine allocation process sequence, and fusing double targets through an adaptive fitness evaluation mechanism; and finally, developing a visual interaction system in a matched manner, and dynamically outputting an optimal process route scheme and an equipment scheduling plan. The method can effectively solve the problems of long processing task completion time and unbalanced equipment utilization rate in a multi-process route scene, is suitable for a scheduling scene of a flexible job shop, and improves the overall operation efficiency of a production line.

The invention belongs to the technical field of production scheduling, and provides a genetic algorithm self-learning variation method and system for solving a scheduling problem, and the method comprises the steps: a chromosome initialization step: setting a workshop variable and a dynamic scheduling rule, and generating an initial solution through a greedy method heuristic strategy; and a chromosome iterative optimization step: iteratively optimizing the initial solution according to a genetic algorithm to obtain a variation result. According to the method, the GA is combined with the self-learning mutation method, and a single-task and batch-task moving mechanism of the same unit and cross units is adopted, so that self-adaptive adjustment of a chromosome structure is realized, the global optimization capability and scheduling efficiency of the algorithm are improved, the global search capability of the algorithm is enhanced, the convergence speed is increased, and the optimization result is improved; by replacing heuristic variation driven by traditional rules with self-learning variation, the complexity of customized development is reduced, the scheduling efficiency is improved, and meanwhile, the code quantity and the implementation cost are reduced.

The invention discloses an official vehicle scheduling and optimizing method and system for mixed type tasks, and belongs to the field of official vehicle intelligent scheduling. The method comprises the following steps: acquiring basic information of official business tasks, drivers and vehicles, constructing a task distance matrix, then establishing a scheduling model which takes the minimum total fuel consumption as a target and contains constraints such as grade matching and passenger-carrying upper limit, and setting genetic and simulated annealing algorithm parameters; constructing an initial scheme population through chromosome codes matched with task levels, and calculating the fitness value of each scheme; and taking the chromosome as a simulated annealing initial solution to complete local optimization, transmitting the solution into a genetic algorithm to carry out crossover and mutation iteration, and finally outputting an optimal scheduling scheme. According to the method, a hybrid algorithm framework is adopted, global search and local optimization capabilities are considered, the compliance, low oil consumption and high resource utilization rate of a scheduling scheme are realized, the problems of level mismatching, resource waste and algorithm limitation of traditional scheduling are solved, and the method is suitable for official car scheduling scenes of various organizations, enterprises and public institutions.

The invention belongs to the technical field of power system operation and planning, and discloses a power grid net load fluctuation scene generation method, system and device based on an ARIMA and Copula combined model and a medium, so as to solve the problem of poor scene generation accuracy. The method comprises the following steps: decomposing and reconstructing an original time sequence of the net load of the power grid by using discrete wavelet transform; taking permutation entropy minimization as an optimization target, adopting a variable chromosome length hybridization genetic algorithm to divide time segments for the low-frequency linear subsequences, and respectively establishing ARIMA models to generate linear trend scenes; establishing a joint probability distribution model of the high-frequency fluctuation subsequences at adjacent moments based on a Copula function, and deducing conditional probability distribution in combination with a Bayesian formula to generate a fluctuation scene; and the linear trend scene and the fluctuation scene are superposed to form an initial net load scene set, and a k-means clustering algorithm is adopted to reduce the initial net load scene set to obtain a representative scene set.

The present application relates to unmanned agricultural machinery technical field, specifically relates to a kind of full coverage field path planning method and device based on improved genetic algorithm.The method comprises: S1, constructs field environment map using grid method.S2, the free grid in field environment map is chromosomally encoded, and a plurality of initial chromosomes are randomly generated.S3, initial chromosome is adjusted, and legal chromosome is obtained.S4, construct fitness function based on multi-objective equilibrium criterion, determine the fitness of legal chromosome.S5, initial chromosome is evolved, and the next generation population is generated, as the parent population of next iteration.S6, determine whether to end this iteration, if yes, then the highest fitness chromosome in contemporary population is output for decoding, and full coverage path is obtained;If not, return to step S3.The present application can reduce repetitive work area and turning number, improve the field work efficiency and work quality of unmanned agricultural machinery.

The application relates to a mode filter, a topology optimization method and an ultrasonic guided wave damage imaging artifact suppression method, relates to the field of ultrasonic detection and imaging, and the topology optimization method comprises the following steps: acquiring initial design parameters, generating an initial population, each chromosome individual in the initial population is coded by a binary logic matrix representing material distribution and meeting design constraints; a population is optimized by using a genetic algorithm, an adaptability function in the genetic algorithm is constructed based on a mode purity index and an energy transmission index, the mode purity index is the ratio of in-plane displacement integral in a transmission area in finite element simulation with the mode filter to total displacement integral, and the energy transmission index is the ratio of relative energy at a transmission end in the finite element simulation with the mode filter to relative energy of an S0 mode; and final optimized material distribution of the mode filter is output. Compared with the prior art, the application has the advantages of effectively suppressing background artifacts, giving consideration to mode regulation performance and substrate structure integrity and the like.

The invention relates to the technical field of intelligent control of high-altitude operation robots, and discloses a dual-algorithm-driven multi-mode high-altitude operation system and an intelligent control method, and the method comprises the steps: obtaining environment sensing data and robot state parameters, and generating an operation task parameter set, a genetic algorithm optimization module is used for generating a global job task scheduling scheme through chromosome coding and fitness function evaluation, dynamically adjusting a job control strategy based on a reinforcement learning algorithm according to a state space, an action space and a reward function to generate a real-time control instruction, and realizing closed-loop cooperation of the genetic algorithm and the reinforcement learning algorithm; according to the invention, the intelligent control and decision-making problem of the aerial work robot is solved, and autonomous decision-making, path planning and multi-task efficient cooperation are realized.

The invention relates to the technical field of software development, and discloses a method, a device and equipment for arranging and testing laboratory orders, and a storage medium. The method comprises the following steps: according to a current order arrangement strategy generated based on a reinforcement learning model, performing chromosome coding on current laboratory order information, current experimenter information and current experimental equipment information to obtain a corresponding initial population; the fitness of individuals in the initial population is evaluated based on a current fitness function, the initial population is iteratively evolved by selecting and improving crossover and mutation operation of chromosomes, and the current fitness function is obtained based on examination parameters of laboratory orders predicted by a first AI model; and under the condition that a set genetic termination condition is met, an optimal laboratory order scheduling and testing scheme is obtained. Therefore, through the reinforcement learning model and the AI model and in combination with the genetic algorithm, comprehensive optimization of laboratory order arrangement and measurement is realized, and the order arrangement efficiency and the resource utilization rate are improved.

The application belongs to the technical field of mobile visual inspection planning, and specifically discloses a mobile visual inspection station-point joint clustering and sequence planning method. The method is a genetic algorithm based on an alternating evolution strategy, and comprises steps of data encoding / decoding, evolution strategy updating, individual selection, individual crossover, individual mutation, population updating and the like. The length of a chromosome is equal to the number of points participating in sequencing. The integer part of a gene represents a point sequence number, and the decimal part represents a station sequence number to which the point belongs. Point sequences belonging to the same station follow the order of appearance in the chromosome; station sequences follow the order of the first appearance position. The application can integrally realize station-point clustering and sequence planning, and improve visual inspection efficiency; the proposed evolution strategy also significantly reduces the running time of the planning program. The application is suitable for large and complex component mobile visual inspection and other similar scenarios.

This invention relates to the field of logistics distribution and route planning technology, and particularly to a method and system for periodic delivery route planning that balances driver workload and customer service order. The method includes: constructing a delivery plan for C customers over D days using a matrix-based chromosome encoding strategy; decoding chromosomes by inserting driver numbers into each row of the matrix, and constructing the daily delivery route for each driver based on the customer set and order obtained each day; performing population evolution based on a genetic algorithm framework, integrating driver workload balancing operators and customer service order balancing operators; calculating chromosome fitness in the population based on the periodic delivery route planning model, and using a tournament selection strategy to select chromosomes to form the next generation population. This invention better addresses the pain points of uneven driver workload and uneven customer service order in periodic delivery, and has certain practical reference value for optimizing the operation of periodic delivery systems.

This invention belongs to the technical field of aircraft manufacturing engineering and intelligent scheduling optimization. It discloses an aircraft final assembly scheduling method and equipment based on an improved genetic algorithm. The steps are as follows: (1) Construct a scheduling model based on process and worker data with the optimization objectives of minimizing the final assembly completion time and minimizing the number of workers involved; (2) Encode the process priority and worker skill slots obtained from the scheduling model using an improved genetic algorithm in the encoding stage, and use the encoding as chromosomes to form an initial population; (3) Perform event-driven decoding on the individuals in the initial population to obtain an initial scheduling scheme; (4) Calculate the fitness value of each chromosome according to the total completion time and the number of workers involved corresponding to the initial scheduling scheme, and screen all chromosomes according to the fitness value to obtain a new population, and then finally obtain the aircraft final assembly scheduling scheme. This invention realizes the collaborative optimization of completion time and the number of workers involved.

The application relates to the technical field of antenna array design, and provides a concentric circular array antenna design method based on hybrid DE-GA collaborative evolution, which comprises the following steps: S1, constructing a topological-geometric coupled concentric circular array radiation model; S2, constructing a hybrid coding chromosome and initializing a population; S3, based on the concentric circular array radiation model, performing a hybrid DE-GA collaborative evolution method on the hybrid coding chromosome in the initialized population; S4, calculating fitness and updating a global optimal solution; S5, judging whether a termination condition is met; if the maximum iteration number or error precision requirement is reached, outputting an optimal array element arrangement sequence and a circular ring radius sequence; otherwise, returning to step S3 and continuing iteration. The improved genetic algorithm and the improved differential evolution algorithm are used for collaborative evolution of discrete sparse variables and continuous radius variables respectively, so that global optimal matching of array topology and geometric structure is realized simultaneously in a single optimization process.

The application provides a farmland feature extraction method based on texture analysis, and belongs to the technical field of remote sensing data processing.The application extracts multi-direction and multi-scale gray level co-occurrence matrix texture feature parameters by constructing a block integral graph to form an initial texture feature vector, constructs a Riemann kernel function matrix by using Riemann geodesic distance calculation, and realizes texture feature dimension reduction by principal component projection in a Riemann manifold tangent space.The reduced texture feature vector is encoded as a genetic algorithm chromosome individual, and the optimal texture feature subset is obtained by fitness evaluation and evolution operation.The optimal texture feature subset of multi-temporal images is used to construct a hidden Markov model, and the optimal state sequence path is searched by using the Viterbi algorithm to realize farmland feature time sequence recognition.After morphological post-processing, farmland feature data is output, and the problem that high-dimensional feature redundancy leads to low feature classification accuracy during farmland remote sensing image texture feature extraction is solved.

The invention relates to the technical field of alfalfa screening, discloses a high-quality and high-yield alfalfa hybrid combination screening method fusing multiple technologies, provides a multi-dimensional coding and double-elite evolution mechanism, constructs an improved non-dominated sorting genetic algorithm-II, and provides a high-quality and high-yield alfalfa hybrid combination screening method by coding a hybrid combination into a continuous real number vector. A chromosome expression mode decoupled from a problem structure is constructed, a double-elite mechanism of elite retention and offset mating is introduced, the retention and heredity ability of a high-quality solution is improved, offset crossover and jitter operation is used, premature convergence is avoided, efficient global search of a strain combination space is achieved, and a high-quality solution is obtained. Meanwhile, a multi-omics association model of genes, metabolites and phenotypes is provided, transcriptome, metabolome and molecular marker data are comprehensively utilized, a multi-dimensional character prediction model is constructed, early-stage accurate screening of filial generations is achieved through correlation analysis and marker verification, the error selection rate is reduced, and the breeding period is shortened.

The invention provides an optimization method of a large-specification membrane electrode hot-press forming machine based on a multi-objective genetic algorithm, and relates to the technical field of optimization of membrane electrode hot-press forming machines. Comprising the following steps: acquiring external disturbance and operation data, and preprocessing the external disturbance and operation data into a unified feature tensor after Kalman filtering and adaptive gain compensation; constructing a coarse-layer global + fine-layer local double-layer population, and generating two batches of seven-dimensional chromosomes; performing rapid Pareto screening optimization on the coarse layer to obtain a first evaluation group, and performing layering decoupling evaluation on the fine layer to obtain a second evaluation group; completing cross-layer migration and target fusion by using the collaborative fitness, and outputting a multi-target fusion set; and determining target process parameters according to production strategy chromatography and issuing the target process parameters to the hot press to execute optimization. The problem that multi-target coupling and online self-adaptive hot-pressing global optimization cannot be realized in the prior art is solved.

This invention discloses an intelligent keel arrangement method for lightweight partition walls. The method includes the following steps: constructing a BIM model of the wall; arranging keels at openings in the partition wall located below the ceiling, and designating the walls between adjacent keels as sub-walls; randomly selecting initial spacing for each sub-wall to form an initial keel arrangement scheme library; calculating the fitness of each keel arrangement scheme in the initial keel arrangement scheme library; performing chromosome evolution on the schemes selected from the initial keel arrangement scheme library based on the fitness of the keel arrangement schemes to obtain an evolutionary chromosome library (GA); and selecting and outputting keel arrangement schemes from the evolutionary chromosome library. This invention, through a genetic algorithm, automatically avoids openings and beam edges, reducing the amount of keels needed for reinforcement around openings and beam edges, saving costs, ensuring the aesthetics of the wall surface, improving the efficiency of detailed design, and saving detailed design time.

The invention relates to a software deployment optimization method based on a genetic algorithm, and the method comprises the steps: converting a service module of to-be-deployed software into a chromosome structure, representing a deployment position through a gene, carrying conflict sensitive information, and reflecting resource competition and dependency coupling deployment constraints; a self-rejection mechanism is executed after chromosome generation or genetic manipulation, potential conflicts are coordinated through gene fine tuning or local migration, and unrejected individuals do not enter subsequent evolution; after deployment position variation, dynamically monitoring service cascade change, suspending evolution and generating a substitution individual when a jump condition is met, and expanding a nonlinear deployment strategy; a dominant service is identified in evolution, and when the deployment position of the dominant service continuously changes, a structure inversion strategy is adopted to drive overall layout adjustment with the service as a core gene. And a genetic crossover stage: according to a deployment semantic consistency comparison structure, prohibiting direct crossover of conflicting individuals, adopting local window crossover and setting a protection area for a dominant service area to guarantee strategy integrity.

The invention provides a network security area boundary threat mimicry protection method and system, and relates to the technical field of network security, and the method comprises the steps: extracting a flow quintuple and load features through a deep packet inspection engine; inputting the quantity feature vector set into a graph neural network behavior pattern mining model, decoding to obtain an attacker target asset priority sequence and a potential attack path graph, and generating a dynamic service camouflage fingerprint matrix and a response bait sequence through parameter set mapping; configuration of a migration condition register is completed, and a programmable mimicry service node cluster is constructed; presetting a malicious request induction template in the response bait sequence, performing selective response delay and content tampering operation on an attacker request, and aggregating to generate an attacker tactical technology process feature chain; and driving the genetic algorithm population library to carry out iterative crossover variation to generate a new generation of protection strategy chromosomes. The attack residence time is obviously prolonged, the decoy success rate is improved, and the protection strategy can be automatically evolved according to the attack mode.

The invention discloses a hybrid production flexible assembly job shop scheduling method considering multi-assembly sequence change and transportation tasks based on a Q-Learning model algorithm. The method comprises the following steps: S1, establishing a scheduling model taking total production completion time, total inventory time and total manpower cost as optimization targets; s2, providing a Q-Learning memetic algorithm to solve the scheduling model, designing a five-layer segmented mixed chromosome coding structure and a two-subgeneration chromosome updating method, and introducing a variable neighborhood search strategy and an elite retention strategy; s3, adaptively adjusting the cross range of the chromosomes through Q-Learning to improve the algorithm efficiency; and S4, through comparison with other three algorithms, validity and robustness of the proposed algorithm are verified. Based on the scheduling method provided by the invention, the processing and assembling sequences of product parts can be optimized simultaneously in the scheduling process of multi-product mixed production and consideration of transportation tasks, and meanwhile, machines, workers and AGV resources are reasonably allocated, so that the production cycle of products is effectively shortened.

The application discloses a plant and animal evaluation method based on a genetic algorithm, and the method comprises the following steps: determining initial genes to be evaluated according to evaluation indexes, and determining a chromosome where the initial genes are located as an initial chromosome; setting a preset mutation rate for a position where each gene on the initial chromosome is located, and increasing the preset mutation rate corresponding to the position where the initial gene is located to obtain a target mutation rate; performing genetic simulation on the initial chromosome according to a preset crossover rate, the preset mutation rate and the target mutation rate to obtain a target chromosome; inputting gene coding of the target chromosome into a preset neural network model; determining an evaluation score of the target chromosome in the preset neural network model; and if the evaluation score is higher than a preset threshold, determining that a gene code corresponding to the evaluation score is a high-quality gene. According to the method, genetic simulation is performed in a directional manner according to a target mutation direction, and the problem that the number of iterations is too large and the target solution is not high-quality in the prior art is solved.

The application discloses a genetic algorithm optimization method for a stacker picking sequence, comprising: obtaining a set of goods to be delivered out of a warehouse, the goods having a goods identifier, a goods location coordinate, a segment number and a priority; determining a single loading constraint based on the segment number and a loading segment number of a loading platform to limit the total segment number of the single loading goods to be less than the loading segment number; taking the goods identifier as a gene, initializing a population and setting a priority segment at the front end of an initial chromosome to preferentially arrange the first priority gene; in an iteration process, constructing an adaptability function based on a path length and a loading segment utilization rate, performing selection and performing cross and / or mutation to update the population, while keeping the first priority gene from participating in the cross and / or mutation; outputting an optimal solution when the iteration terminates, and performing a pass-by-pass split on the optimal solution according to the single loading constraint, and performing priority checking and adjustment pass by pass to ensure that the first priority gene is divided into the previous pass, thereby improving the executability and comprehensive efficiency of the delivery operation.

The invention relates to a mode filter, a topological optimization method and an ultrasonic guided wave damage imaging artifact suppression method, and relates to the field of ultrasonic detection and imagines.The topological optimization method comprises the steps that initial design parameters are obtained, and an initial population is generated; each chromosome individual in the initial population is formed by coding a binary logic matrix which represents material distribution and meets design constraints; a genetic algorithm is adopted for population optimization, a fitness function in the genetic algorithm is constructed based on a mode purity index and an energy transmission index, and the mode purity index is a ratio of a transmission region in-plane displacement integral to a total displacement integral under finite element simulation added with a mode filter; the energy transmission index is a ratio of relative energy of a transmission end under finite element simulation added with a mode filter to relative energy of an S0 mode; and outputting the final optimized material distribution of the mode filter. Compared with the prior art, the method has the advantages that background artifacts are effectively inhibited, and the mode regulation and control performance and the matrix structure integrity are both considered.

The application discloses a reservoir optimal scheduling method based on an improved adaptive genetic algorithm, which comprises the following steps: 1, calculating the section inflow under the condition of no water storage and scheduling; 2, dividing the discharge flow; 3, identifying the rising, falling and flat period intervals of the inflow; 4, encoding each interval section in sequence; 5, judging the time interval of each period, obtaining the corresponding chromosome section for decoding, combining the predicted inflow process and the starting optimal discharge process and water level change process starting from the starting regulation water level; 6, calculating the fitness of each individual according to the discharge process; 7, judging the convergence; 8, genetic operation; 9, control parameters; 10, repeating steps 5 to 9 until the conditions are met; and the technical problems that the reading of data in the reservoir optimal scheduling method consumes a large amount of CPU, memory and the like and cannot meet the principle that the gate opening degree cannot be changed too frequently in scheduling are solved.

The invention discloses an air compressor system scheduling method and device, electronic equipment and a storage medium. The method comprises the steps that S1, the current state parameter of a system is obtained, S2, the difference value P between the current pressure and the target pressure is calculated, and P is converted into the total system output flow difference O needed for reaching the target pressure; s3, taking the total output flow difference O of the system as an optimization target, and generating an optimal scheduling strategy based on a genetic algorithm; chromosome codes of the genetic algorithm are used for representing the starting number of the variable frequency air compressors, the output level of each variable frequency air compressor and the starting number of the power frequency air compressors; and S4, according to the optimal scheduling strategy, starting and stopping of the variable-frequency air compressor and the power-frequency air compressor and the output level of the variable-frequency air compressor are controlled, and the system pressure approaches the target pressure. According to the method, the start-stop number of the frequency converters and the power frequency machines and the specific output level of each frequency converter can be decided according to the real-time pressure difference, the system pressure can rapidly and stably reach the target value, and meanwhile the overall energy efficiency of the system is improved.