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

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention relates to the field of nutrition and health management, and discloses a nutrition evaluation and prediction method and system based on big data, and the method comprises the following steps: firstly collecting the physiological data, diet record and activity data of a user, and carrying out the preprocessing of the data, and guaranteeing the data quality; dimensionality reduction is carried out through feature extraction and principal component analysis, and key features are extracted; the method comprises the following steps: evaluating daily nutritional requirements of a user by using a multivariable linear regression model, a Bayesian network and causal inference, predicting a nutritional requirement change trend of the user in a future period of time based on a long-short-term memory network, and optimizing daily diet selection of the user by using a Markov decision process; and finally, generating a personalized nutrition scheme by adopting linear programming and a genetic algorithm, and ensuring that the nutritional requirements and health targets of the user are met. According to the method, personalized and scientific diet suggestions can be provided, and the quality and effectiveness of individual nutrition management are improved.

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 relates to the technical field of logistics management, in particular to a warehouse material storage location distributing and sorting system based on a genetic algorithm, and the system comprises a data access and analysis module which is connected with a main database of a warehouse management system through a security protocol, synchronously updates the information of materials, orders and employees, carries out the establishment of a data index, and transmits the data index to a server; according to the invention, through the design of the sorting system and the combination of simulated annealing and a genetic algorithm, the intelligent configuration of the material storage position is effectively realized, the sorting path is optimized, the material carrying time and the manual error are reduced, the sorting accuracy is improved, the AGV is managed by adopting an asynchronous algorithm, the task scheduling is optimized, and the sorting efficiency is improved. The material processing speed is accelerated, the response efficiency is improved, the inventory configuration is dynamically adjusted, the market demand change is adapted, the high efficiency and the quick response capability of the inventory are kept, the automation and the intelligence of the inventory management are promoted, and the operation efficiency and the service quality of the whole warehouse are greatly improved.

The invention relates to the technical field of subgrade settlement prediction, in particular to a subgrade settlement prediction method based on deep learning, electronic equipment and a medium, the subgrade settlement prediction method provided by the invention adopts a subgrade settlement prediction model for prediction, and the model adopts the combination of a convolutional neural network and a bidirectional long-short term memory network. Spatio-temporal features are effectively captured, and the prediction precision and stability of the model are improved; by introducing a channel attention enhancement mechanism module, dynamic weighting is performed on different feature channels, the selection ability of the model to key features is improved, and the sensitivity of the model to subtle changes in data and the capture ability of the model to global features are enhanced; the model is trained through the genetic algorithm, the efficiency is improved, and the genetic algorithm can automatically find an optimal combination in a complex hyper-parameter space, so that the model can show good generalization ability on different roadbed settlement data sets, and the prediction precision of the model 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 an intelligent path planning method and system for an unmanned aerial vehicle. The method comprises the following steps: initializing related parameters and optimization algorithm parameters of the unmanned aerial vehicle; coding the path points of the unmanned aerial vehicle; calculating fitness values of all individuals in the population; generating a new population through selection, crossover and mutation operations; carrying out local search on individuals with better fitness in the population; dynamically adjusting algorithm parameters according to population fitness changes; and outputting the path with the highest fitness value as the optimal flight path of the unmanned aerial vehicle. According to the method, a local search mechanism and a dynamic adjustment strategy are introduced on the basis of a traditional genetic algorithm, the method is suitable for a combinatorial optimization problem, excellent performance is shown in unmanned aerial vehicle path planning, and a path with the shortest time can be efficiently found.

The invention discloses a renewable energy distribution optimization method based on a genetic algorithm. The method comprises the following steps: S1, generating an initial population; s2, defining a fitness function, and evaluating the fitness of each individual in the population; s3, an improved roulette selection method is adopted, and individuals are selected according to the fitness; s4, performing crossover operation on the selected individuals, and generating new individuals by adopting a multi-point crossover and uniform crossover combined method; s5, performing mutation operation on the new individuals, and adopting a self-adaptive mutation mechanism; s6, making a dynamic adjustment strategy; s7, new individuals generated through selection, intersection, variation and dynamic adjustment are used for replacing old individuals, and a new generation of population is formed; s8, repeating the steps S2-S7 until a preset termination condition is met; and S9, after a termination condition is met, outputting an individual with the highest fitness in the population as a final renewable energy source distribution scheme. Renewable energy distribution is optimized through the improved genetic algorithm, and efficient and stable distribution is achieved.

The invention relates to the field of unmanned aerial vehicle group control, in particular to an intelligent cooperative control method for an unmanned aerial vehicle group. Comprising the steps of receiving and preprocessing environment data, unmanned aerial vehicle state data and task data, and performing data fusion processing on the preprocessed data; performing unmanned aerial vehicle group task allocation by adopting a multi-objective optimization genetic algorithm to generate a task allocation scheme; generating an optimal path based on the task allocation scheme; the unmanned aerial vehicle executes a task according to the optimal path, and the flight path of the unmanned aerial vehicle is adjusted by using a dynamic obstacle avoidance function; a new optimal path is generated according to the current position of the unmanned aerial vehicle, the unmanned aerial vehicle continues to execute the task according to the new optimal path, and the unmanned aerial vehicle returns to the landing point after the task is completed and uploads the collected flight data and environment data. The problems that an existing selection mechanism usually depends too much on a single standard, and most of existing path planning methods only consider a static environment; and the problem that the unmanned aerial vehicle is easy to collide or deviate in the task execution process due to the lack of real-time obstacle avoidance capability is solved.

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 provides a method for solving the computing power resource utilization rate based on a genetic algorithm, and relates to the technical field of computing power resource utilization. Comprising the following steps of S1, performing population initialization, randomly generating a group of initial solutions, and representing a computing power resource allocation scheme by each solution; s2, adaptability evaluation: defining a fitness function to calculate the fitness value of each individual; s3, genetic operation: performing selection operation, crossover operation and mutation operation to generate a new offspring; s4, generating a new-generation population, and selecting the most excellent individuals to form the new-generation population; s5, performing iterative computation, iteratively executing the genetic operation and the new generation population generation step, and continuously updating the population until a termination condition is met; and S6, outputting a result, and identifying an optimal solution or a group of excellent solutions from the current population. By simulating natural selection and genetic mechanisms in the biological evolution process, allocation of computing power resources is optimized, so that the resource utilization rate and the system performance are improved.

The embodiment of the invention provides a verification method and device based on a genetic algorithm, and the method comprises the steps: randomly selecting a plurality of groups of initial key parameters from a preset key parameter value range, and calculating the fitness of each group of initial key parameters in a processor verification process, processing the plurality of groups of initial key parameters according to the fitness, a preset selection probability, a preset crossover probability and a preset mutation probability to finally obtain a plurality of groups of target key parameters, taking the plurality of groups of target key parameters as the plurality of groups of initial key parameters, executing the steps again until a preset termination condition is met, and stopping iteration to obtain a plurality of target key parameters; and calculating and outputting a processor verification coverage rate according to the plurality of groups of target key parameters and the key parameter value range. The target key parameters are obtained based on the genetic algorithm, local optimum is avoided, the parameters are continuously optimized in the iteration process, the high processor verification coverage rate can be obtained, and the processor verification efficiency and quality are 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 discloses an unmanned aerial vehicle cluster formation configuration and spectrum resource joint optimization method, system, device, medium and product, and relates to the field of unmanned aerial vehicle planning control, and the method comprises the steps: dispatching a plurality of unmanned aerial vehicle formations to execute a plurality of tasks under the conditions of given spectrum resources and limited number of unmanned aerial vehicles, constructing an optimization problem model by using a coupling relationship among the number of the unmanned aerial vehicle formations, a target sensing sequence and spectrum resource allocation; solving the optimization problem model by using an improved genetic algorithm, and outputting an optimal unmanned aerial vehicle cluster formation configuration and spectrum resource joint optimization scheme; according to the improved genetic algorithm, a simulated annealing algorithm is embedded into the genetic algorithm, a crossover operator of the genetic algorithm is adjusted, and an elite strategy and a dynamically adjustable mutation rate are introduced into mutation operation. According to the invention, joint planning of unmanned aerial vehicle task planning, unmanned aerial vehicle formation number distribution and bandwidth selection can be realized, and the method has higher global optimization capability.

The invention discloses a large-scale infrastructure intensive land-saving development potential analysis method and system, and the method comprises the following steps: 1) constructing a large-scale infrastructure stock land geospatial database based on Arcgis, and collecting the related element data of the large-scale infrastructure stock land; 2) extracting land suitable for optimizing stock of large infrastructures, and screening and extracting municipal public facility land and road and traffic facility land which are larger than 5 hectare in land scale and are not dangerous; 3) constructing an AHP-entropy weight method hybrid model, optimizing the weight by means of a genetic algorithm to establish a site selection evaluation system, and comprehensively evaluating the alternative land; 4) constructing a land development population service coverage model to obtain the number of each development land service population; and 5) guiding a land redevelopment time sequence in combination with the comprehensive score of the alternative land and the number of serviceable population. The method provides a path for improving the utilization efficiency of land resources, eliminating the neighborhood effect of large infrastructures and realizing high-quality development.

The invention relates to the technical field of aviation safety, and provides an NSGA-based flight ordering method, system, device, product and medium, and the method comprises the steps: obtaining the flight information of to-be-parked aircrafts, carrying out the random ordering of the to-be-parked aircrafts, and obtaining an initial population; calculating adaptive parameters so as to formulate optimization constraint conditions; under the constraint of an optimization constraint condition, performing multi-target optimization sorting on the initial population according to adaptive parameters, calculating the congestion degree of an approach sequence, performing elimination to obtain a first intermediate population, performing genetic algorithm screening to obtain a second intermediate population, and performing crossover variation to obtain a third intermediate population to obtain a target population; calculating a parking position selection parameter and a shortest taxiing path of the aircraft to be landed, thereby calculating a taxiway adaptability parameter; and calculating iteration adaptability parameters of the target population, and performing genetic algorithm iteration on the target population to obtain a target approach sequence. According to the invention, the scheduling efficiency of flight landing is effectively improved.

The invention provides a video monitoring point location layout method, system and device and a medium, and belongs to the technical field of video monitoring layout, and the method comprises the following steps: collecting geographic position information in a video monitoring target area and point location information of a built video probe, and processing the geographic position information and the point location information; calculating the coverage area of each video probe and the actual coverage area of all the video probes, and constructing a fitness evaluation function; based on point location information of a built video probe, a genetic algorithm is used, a population of monitoring configuration layout is initialized, fitness calculation is carried out, population selection and optimization of crossover and variation are carried out with the maximum fitness as the target, and the optimal monitoring configuration layout is obtained and applied to an actual monitoring system when no monitoring blind area exists. The video monitoring layout is optimized through the genetic algorithm, efficient configuration and blind area complementation are achieved, the monitoring coverage rate is increased, repeated investment is reduced, and the method flexibly adapts to monitoring demand changes.

The invention relates to the technical field of road engineering, in particular to a large language model-driven road change design method, which comprises the following steps of: 1) performing rough selection by using a large language model and a prompt project, and selecting a corridor zone; and 2) performing optimization through an improved artificial potential field MAPE algorithm. According to the method provided by the invention, by applying a natural language processing technology, automatic generation of a preliminary corridor belt design scheme is realized, the manpower and time cost of a route pre-selection stage is saved, a large number of alternative schemes can be produced to assist design, and the optimization effect is also higher than that of a relatively mature genetic algorithm; the method is a successful exploration of a large language model in intelligent road line selection application.

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 relates to an intelligent available link prediction and optimal selection method for unmanned cluster anti-interference communication. The method comprises the following steps: acquiring a multi-channel interference power matrix; an LSTM-KAN interference prediction model is established; predicting channel interference power of a plurality of time slots in the future by using an LSTM-KAN interference prediction model; channel selection is modeled as a dual-objective optimization model for minimizing channel switching times and minimizing channel average interference power; and solving the dual-objective optimization model based on a classification sorting genetic algorithm, improving a crossover variation strategy and an elitist retention strategy to increase the generation probability of a low-channel switching frequency solution, and retaining the low-channel switching frequency solution as much as possible in an iteration process, and finally generating an optimized channel switching and interference power solution. Compared with the prior art, the method has the advantages of remarkably reducing the channel switching times, improving the throughput and reducing the packet loss rate, and has important application value in resisting unmanned trunking communication in a strong electromagnetic interference environment.

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.

The invention provides a variable-frequency control method and system for noise of an exhaust system of a transformer substation room based on a genetic algorithm, and the method comprises the steps: collecting the temperature and noise data of the transformer substation room in real time through a distributed sensor network, and building a multi-target optimization model through the genetic algorithm in combination with a variable-frequency speed regulation technology; and the noise and the temperature are used as constraint conditions to be jointly solved with an optimization target. The genetic algorithm dynamically adjusts the operation frequency of the exhaust fan through the operations of population initialization, fitness evaluation, selection, crossover, variation and the like, so that the requirements of temperature control and noise minimization are balanced. The rotating speed of the exhaust fan can be adjusted in a self-adaptive mode. Compared with a traditional single-machine fixed wind speed or simple linkage control method, the technology can remarkably improve the comprehensive energy efficiency of the system, reduce energy waste and improve the operation environment of the transformer substation room. The technology is suitable for noise and temperature control requirements of various types of indoor transformer substations and similar scenes, and has high applicability and popularization value.

The invention relates to the technical field of artificial intelligence, in particular to a collection and transportation system optimization method based on spatial distribution characteristics of rural household garbage. The method comprises the following steps: (1) remote sensing image identification: identifying and extracting typical land images of a rural area by adopting a D-LinkNet model; (2) resident area partitioning: according to the resident space aggregation degree, dividing the resident area into three space aggregation forms of high aggregation, medium aggregation and dispersion by adopting an optimized hotspot analysis method; (3) collecting point site selection: constructing a relation function between the resident area and the garbage yield, clustering the resident points in the three spatial aggregation forms by adopting a multiple K-means clustering algorithm, setting a buffer distance, and optimizing and determining the position of the garbage collecting point; and (4) carrying out collection and transportation path optimization: based on the collection and transportation requirements of the three space aggregation form areas, taking the shortest collection and transportation path as a target, and adopting a genetic algorithm to solve an optimal garbage collection and transportation path scheme, so that scientific planning of a rural household garbage collection and transportation system is realized, and collection and transportation cost and environmental pollution are reduced.

The invention relates to a noise feature vector classification model construction method, a speech enhancement method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: coding penalty parameters and kernel function parameters of a support vector machine; taking the penalty parameter and the kernel function parameter as optimization variables of a genetic algorithm, setting an initial population, and constructing a fitness evaluation function based on classification accuracy; performing selection operation, crossover operation and mutation operation on the initial population, and updating an iterative population according to a fitness evaluation function to obtain an optimal penalty parameter and an optimal kernel function parameter; and training a support vector machine through the optimal penalty parameter and the optimal kernel function parameter, and constructing a noise feature vector classification model. In a noisy environment, the classification effect of the noise feature vector is improved under the condition that nonlinear and non-stationary noise exists, and the noise feature classification requirement in a complex sound signal processing scene is met.

The invention provides a potato disease and insect pest classification method, system and device and a storage medium, and belongs to the technical field of agriculture. Comprising the following steps: acquiring spectral data of potato leaves in a visible or near-infrared light region by using a hyperspectral instrument; the method comprises the following steps of: performing characteristic wavelength selection on preprocessed original data by using a competitive self-adaptive reweighted sampling method, a continuous projection algorithm and a genetic algorithm; a CARS-SPA-GA feature selection method is provided, that is, feature wavelengths selected by the first two algorithms are recombined and de-duplicated, and secondary feature selection is performed by GA. And inputting the characteristic wavelength into different types of classifiers, and performing parameter optimization by using a real number coding genetic algorithm. Classification of potato early blight and ladybird pests by using visible or near infrared spectrum data is feasible, and accuracy and detection efficiency of potato disease and insect pest classification can be greatly improved.

The invention relates to a node switching method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: obtaining scores corresponding to a plurality of performance indexes of a target physical machine; inputting the scores of the plurality of performance indexes into a multi-target genetic algorithm model, and obtaining a target solution output by a multi-target genetic algorithm; the target solution represents a scheduling scheme of the pod resource and comprises a target node corresponding to the pod resource; and switching the source node where the pod resource is located to the target node according to the target solution. According to the method, by considering a plurality of performance indexes, service continuity and non-interruption are ensured, high availability of the system is realized, and meanwhile, selection of the target node is more flexible.