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

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 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.

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 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 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 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 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 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 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.

The invention discloses an iron-carbon-based catalyst preparation parameter selection method. The method comprises the following steps: acquiring a plurality of groups of iron-carbon-based catalyst preparation parameters, and performing pretreatment to obtain a plurality of groups of pretreated iron-carbon-based catalyst preparation parameters; constructing a multi-task learning model; training the multi-task learning model by using the plurality of groups of preprocessed iron-carbon-based catalyst preparation parameters to obtain a mapping relationship between each group of iron-carbon-based catalyst preparation parameters and a catalyst performance value; and iteratively optimizing each group of iron-carbon-based catalyst preparation parameters by utilizing a genetic algorithm according to the mapping relationship between each group of iron-carbon-based catalyst preparation parameters and the catalyst performance value to obtain an optimal iron-carbon-based catalyst preparation parameter scheme. The method is high in parameter selection efficiency, and the prepared catalyst is good in performance.

The invention provides a cotton shortage irrigation decision-making system based on a genetic algorithm and reinforcement learning, and the system simulates information such as growth and yield of crops by using an irrigation prescription given by a decision-making algorithm and by means of a crop growth model. The method aims to solve the problems of high labor cost, excessive irrigation, low crop yield, low economic benefit and the like in the existing irrigation decision. In order to achieve the target, a genetic algorithm is used for optimizing a target function (yield), and it is guaranteed that water consumption is lower than the total irrigation amount provided by a local farm in previous years. The invention also introduces a Q-learning algorithm in reinforcement learning (RL) to solve the manual parameter selection problem of the genetic algorithm. According to the method, the genetic algorithm is used for quickly finding the optimal irrigation prescription of cotton shortage irrigation, a large amount of labor cost and water cost are saved, economic benefits are improved, and the quality of irrigation decisions is improved.

The invention discloses a polar region environment-oriented two-stage micro-siting optimization method for a wind generating set, and relates to the technical field of polar region renewable energy planning and wind power generation, the optimization method comprises the following steps: polar region environment parameter initialization and scene modeling; the first stage is grid primary selection based on mixed integer linear programming; the second stage is coordinate refinement based on a multi-population genetic algorithm; and scheme output and efficiency evaluation. According to the method, a sector reweighting mechanism of extremely cold correction parameters is realized through reweighting of biochemical parameters and scenes in an environment special for a polar region, the extremely cold correction parameters are directly embedded into a wind regime scene set and a power model, and the problem that a conventional land model can obviously underestimate or overestimate single-machine power and wake flow propagation distance under extremely low temperature and low roughness conditions, so that the reliability of the model is improved is solved. Therefore, the power prediction and wake flow loss evaluation are more practical under the polar region conditions such as the south pole, so that the engineering reliability of site selection decision and the accuracy of generating capacity prediction are improved.

The invention relates to an intelligent decision-making method and system for server customization demand matching. The method comprises the steps of obtaining a server customization demand input by a user; constructing a multi-dimensional demand portrait based on the server customization demand, wherein the demand portrait comprises a quantitative index and a configuration constraint condition; according to the demand portrait, executing configuration compatibility check, and identifying whether a compatibility problem exists or not; when the compatibility check is passed, a candidate configuration scheme is generated by adopting a multi-target genetic algorithm based on the demand portrait passing the compatibility check; and sorting and screening the candidate configuration schemes based on user preference data in the demand portraits passing the compatibility check to obtain a recommended scheme list, and transmitting the recommended scheme list to a display interface for a user to select and confirm, the recommended scheme comprising a component list, a price, a performance index and a recommendation reason. The method has the effect of improving the accuracy of server customization recommendation.

The invention relates to an edge computing application scheduling and issuing method and device based on storage resource awareness, and the method comprises the steps: obtaining the storage resource information of an edge node and the performance data of a heterogeneous storage device, and predicting the storage demand and IO bottleneck of the edge node based on a machine learning algorithm; based on a prediction result, adopting reinforcement learning and a multi-target genetic algorithm to select an optimal edge node; and executing application task scheduling by adopting the optimal edge node, and adjusting resource allocation and task migration according to task requirements and node loads. According to the method, by dynamically sensing the storage capacity and performance of the edge nodes and the characteristics of heterogeneous storage equipment, node selection is optimized in combination with reinforcement learning and a multi-target genetic algorithm, task delay is remarkably reduced, and the resource utilization rate is increased. A dynamic resource allocation and task migration mechanism ensures load balance and adapts to a complex network environment. The feedback optimization based on the execution effect further improves the scheduling accuracy and the system robustness, and has the remarkable advantages of high efficiency and intellectualization.

This invention provides a method for optimizing the hybridization and selection of high-iron strains in black fungus based on big data analysis. It relates to the fields of big data analysis and black fungus variety breeding. The method includes: Step S1: Collecting basic data of strains, hybridization process data, culture environment parameters, and external reference data, and performing standardization and structuring processing; Step S2: Encoding and optimizing hybridization combinations using a biased random bond genetic algorithm, combined with a dual-elite evolutionary mechanism and dynamic parameter adjustment strategy to generate the optimal hybridization scheme; Step S3: Constructing a dynamic environmental model using a model-based hybrid inverse reinforcement learning algorithm, searching for the optimal culture strategy that maximizes iron content in a virtual environment; Step S4: Constructing a multi-index comprehensive scoring system based on the analytic hierarchy process (AHP), identifying abnormal samples and triggering model backtracking adjustments, outputting high-performance strains for the next round of breeding. This invention significantly improves breeding efficiency and the accuracy of strain selection.

The invention provides a curved surface circuit jet printing droplet spreading prediction method for optimizing a BP neural network (GA-BP) based on a genetic algorithm, and belongs to the technical field of inkjet printing process parameter optimization and curved surface electronic manufacturing. A high-precision prediction model is constructed for solving the problem that spreading behaviors of micro-droplets on a cylindrical surface substrate are complex due to coupling influences of material physical properties, substrate characteristics and process parameters. The dynamic viscosity, the surface tension, the density, the surface wettability, the curvature radius, the impact speed and the temperature serve as input, the circumferential and axial maximum spreading diameters serve as output, and training data are obtained through high-speed camera shooting or CFD simulation; globally optimizing the initial weight and threshold of the BP network by adopting a genetic algorithm, and simulating binary crossover and Gaussian mutation strategies by taking the reciprocal of a verification set MSE as a fitness function and combining roulette selection; finally, the GA-BP model obtained through training meets the conditions that R2 is larger than or equal to 0.92 and RMSE is smaller than or equal to 0.05, and real-time prediction of the spreading behavior under the new process condition can be achieved. The method effectively overcomes the problems that an empirical formula is poor in applicability and low in numerical simulation efficiency, and traditional BP is prone to sinking and local minimum and the like, and has high precision, strong generalization and engineering practicability.

The invention discloses a database index optimization method, system and equipment based on artificial intelligence. The method specifically comprises the steps of collecting performance data of a database in real time; performing preprocessing and feature extraction on the performance data to obtain a spatial-temporal feature matrix; taking the spatial-temporal characteristic matrix as input, performing index combination selection by using a hybrid solver formed by a quantum annealing algorithm and a classical genetic algorithm, and generating an index optimization strategy; on the basis of an index optimization strategy, potential performance risks are predicted according to an index change influence evaluation model, and an optimization decision explanation is generated through an SHAP value; and executing index change operation according to the index optimization strategy, evaluating database performance after index change, and feeding back to the hybrid solver for updating and optimization. According to the method, the database index is automatically and intelligently optimized, the database performance is improved, and the manual intervention cost is reduced.

The invention relates to the technical field of big data mining and analysis, and discloses a charging pile intelligent site selection method and system based on big data analysis, and the method comprises the steps: obtaining traffic space-time big data, and generating a road network topological structure; determining a shortest path matrix based on the road network topological structure, generating an importance score in combination with a graph theory algorithm, and comparing the importance score with a preset score threshold; carrying out simulation evolution based on a comparison result, generating a propagation coefficient and an aggregation coefficient of the node, and obtaining a transmission influence value; acquiring spatial load data, and outputting a site selection scheme set in combination with a genetic algorithm; obtaining road network structure data of the scheme set, and determining a preliminary ranking; acquiring data of the ranking scheme to determine a to-be-optimized scheme, and updating by using the prediction model; and calculating a time expectation value based on the updated data, and outputting a final site selection scheme by combining a preset time threshold value for judgment. According to the method, the road network integrity and the dynamic association effect are quantitatively evaluated through big data analysis, and global optimization of charging pile site selection is realized.

The invention discloses a game optimization solving method for a multi-user power control problem in a wireless network, and the method comprises the following steps: building a multi-user power control game model, and arranging a joint strategy vector x according to the transmission power decision of a user; designing a fitness function F (x); selecting an elite individual with F (x) closest to 0 in the genetic algorithm population; and updating a local search radius by adopting a cosine annealing algorithm, performing optimization comparison on elite individuals in combination with a particle swarm optimization algorithm, forming a new generation of population in combination with new individuals obtained by selection, crossover and variation, and outputting an individual with F (x) closest to 0 after iteration, namely a Nash equilibrium solution as a final power control decision. According to the method, a game problem is converted into an optimization problem by designing a specific fitness function, so that the calculation complexity and the implementation threshold are remarkably reduced; and the method has low requirements on the property of a cost function, does not need to be continuous or differentiable, has relatively high robustness and universality, and can efficiently solve a continuous game problem.

The invention discloses an internal combustion engine performance multi-objective optimization method based on data driving and a genetic algorithm, and belongs to the technical field of internal combustion engines. The method comprises the following steps that performance data of power, combustion, emission and the like of the internal combustion engine under different working conditions are collected, and an internal combustion engine performance data set is constructed; training a machine learning model based on the data set, and establishing an internal combustion engine performance prediction model; a genetic algorithm is adopted, an initial population is generated within a preset internal combustion engine working condition range, and fitness values of population individuals are rapidly calculated with the help of the internal combustion engine performance prediction model; and continuously iteratively optimizing the population through the steps of intra-population selection, crossover, variation, fitness calculation, evaluation and the like until the population meets an expected convergence condition or reaches the maximum number of iterations, thereby obtaining a Pareto optimal solution set for multi-objective optimization of the performance of the internal combustion engine. According to the method, rapid evaluation and multi-target performance optimization of the engine performance can be realized, and the number of experiments and the development period are greatly reduced.

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.

Aiming at the construction of an automatic warehouse logistics system of a digital workshop production line, the invention provides a single-load automatic guided vehicle workshop path planning method based on directed acyclic graph modeling, and the method comprises the functions of task selection, task execution sequence planning and the like. The method comprises a fusion algorithm framework based on a binary discrete particle swarm optimization algorithm and a genetic algorithm component, and can solve and fully consider the diversity of task selection strategies in a short time according to information such as known task arrival time and transportation starting point, so that normal production is ensured; and the task execution sequence with the shortest total driving distance is the optimal (or close to the optimal) task execution sequence.

The invention provides a non-stationary hydrological time sequence prediction method based on WA-GA-BP, and the method comprises the steps: carrying out the recognition of a non-stationary hydrological time sequence through wavelet analysis, separating a deterministic component from a random component, building a genetic BP neural network for sub-components, carrying out the prediction, and superposing the prediction results of the sub-components, and obtaining a final prediction result. The method is applied to the prediction of four groups of hydrological time sequences, and compared with other two common hydrological time sequence prediction models AR (p) and GM (1, 1), the result shows that the wavelet analysis and genetic algorithm coupling improved BP neural network model can effectively improve the prediction precision and stability, the prediction result of the WA-GA-BP model meets the long-term prediction precision requirement of water regimen, and the prediction result of the WA-GA-BP model meets the long-term prediction precision requirement of the water regimen. Compared with AR (p) and GM (1, 1) models, the method has higher accuracy. An example verifies that the WA-GA-BP model not only can solve the problem that the prediction precision is influenced by the non-stationarity, but also can solve the problem that the prediction stability is influenced by the model parameter selection randomness, and has reasonability and feasibility when being applied to the long-term prediction of the non-stationary hydrological time series.