401 results about "Mutation (genetic algorithm)" patented technology

The invention discloses a UUV cluster dynamic task planning method based on a multi-target genetic algorithm, a program, equipment and a storage medium, and belongs to the field of underwater multi-UUV cooperative detection of multiple targets. According to the method, firstly, for path planning of regional task points, chromosome representation is completed through sequential coding, chromosomes are selected and ranked randomly, and the chromosome with the highest fitness value is selected from each group; then, in each group of the current population, selecting an optimal parent individual to carry out crossover and mutation operations so as to improve the quality of offspring individuals, and carrying out updating to obtain a next-generation population; and finally, obtaining a plurality of optimal individuals, and outputting a task planning path result. Dividing the search area into a plurality of task sub-areas, and completing the dynamic task planning of the UUV cluster according to the target distribution condition of the sub-areas and the condition of each UUV. According to the method, the search strategy can be adjusted in real time according to the real-time detection result and the environment change, so that the target distribution non-uniformity and the cluster efficiency difference are effectively relieved.

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 workshop scheduling method and system fusing a decision tree and a genetic algorithm. The method comprises the following steps: firstly, collecting operation process and machine information through an ERP system and extracting related features; generating a scheduling scheme by using historical orders of an ERP system or manually added orders, and constructing a training set training decision tree to accurately judge machine allocation conflicts; constructing a multi-target flexible job shop scheduling model, and constructing a target function based on a hierarchical Pareto dominance relationship; and secondly, realizing job-shop scheduling scheme coding by adopting double-layer chromosome coding, carrying out selection, intersection and mutation operations in combination with a genetic algorithm, carrying out conflict detection and repair by utilizing a decision tree, solving an objective function, continuously iterating until a convergence condition is met, and outputting a Pareto optimal solution. According to the workshop scheduling method, the scheduling efficiency and feasibility are improved by establishing a data model of a multi-target flexible job workshop scheduling problem and through dynamic conflict detection, hierarchical multi-target optimization and a closed-loop feedback mechanism.

The invention relates to the technical field of water conservancy projects, in particular to a construction scheduling method for collaborative linkage of cross-basin water conservancy projects, which comprises the following steps of: constructing a space-time water conservancy topological network to realize multi-source data fusion and spatial association and provide a basis for accurate scheduling; potential contradictions between construction and hydrological regulation and control are found in advance by recognizing conflict areas and performing quantitative analysis; a space-time safety window strategy set is formulated, the construction period is optimized in combination with risk prediction, and operation safety and efficiency are guaranteed; through task decomposition and collaborative factor allocation, an atomic operation unit and a dependency relationship are defined, and the cross-project linkage capability is improved; by generating a preliminary scheduling scheme, and through a resource-collaborative double-constraint graph and a genetic algorithm, efficient resource configuration and conflict minimization are realized; through dynamic risk monitoring and scheduling scheme reconstruction, hydrological abrupt change and construction deviation are quickly responded, cooperative factors are triggered for rebinding, and scheme adaptability and robustness are ensured.

The invention provides a battery pack thermal management system control method based on an improved adaptive genetic algorithm, and the method comprises the steps: building a battery pack thermal model considering the thermal coupling effect between battery modules, and constructing a dynamic change model of the temperature of the battery modules, obtaining temperature models of head and tail battery modules in the battery pack according to the dynamic change model of the temperature of the battery modules; constructing a multi-objective optimization function which comprehensively considers the energy consumption of the cooling system, the temperature control precision of the battery pack and the temperature uniformity of the battery module; an improved adaptive genetic algorithm is obtained by introducing an adaptive crossover variation mechanism, an elitist retention strategy and a dynamic penalty function; based on an improved adaptive genetic algorithm, solving the multi-objective optimization function to obtain an optimal coolant flow rate; the optimal cooling liquid flow speed is converted into a control instruction of the electronic water pump rotating speed through the water pump characteristic curve, and real-time adjustment of the electronic water pump rotating speed is achieved. According to the invention, accurate control of the battery pack thermal management system can be realized.

A k-parallel row sorting problem solving method considering multiple channels relates to the technical field of disassembly line layout, and mainly comprises the following steps: determining an objective function, calculating channel coordinate information in a region, generating a population P1 and randomly generating a population P2 by using a greedy strategy, combining the population P1 and the population P2 into an initial population P, calculating the fitness of all solutions in the initial population, and obtaining a k-parallel row sorting problem; screening an elite solution, selecting solutions except the elite solution in the initial population based on the fitness by using a roulette mechanism to execute genetic circulation, sequentially carrying out crossover and mutation operation based on Q-learning, combining the population with the elite solution after mutation with the population P1 and the population P2 to update the population, according to the maximum number of iterations, it is judged that iterative calculation continues or a result is output; according to the variable domain genetic algorithm based on Q-learning, an optimal solution for solving kPROPP can be provided in a short time, the solving efficiency of the variable domain genetic algorithm is greatly superior to that of a conventional accurate solver, the solving effect of the variable domain genetic algorithm is superior to that of other methods, and reliable support is provided for solving the problem of parallel layout planning.

The invention discloses an oil reservoir injection-production optimization method based on an improved multi-objective particle swarm algorithm, and belongs to the technical field of oil and gas field development. According to the method, an attention mechanism, a Transform and a long-short-term memory network are introduced based on a relational graph convolutional network, geological features and production system information are combined, inter-well water content prediction is taken as a target, an HSTMF model is constructed, an injection-production well production system does not need to be adjusted, the calculated amount is greatly reduced, and the prediction precision is remarkably superior to that of a traditional method; in the aspect of optimization algorithms, a Cauchy disturbance variation mechanism is introduced into a genetic algorithm and combined with a particle swarm optimization algorithm to form a new optimization strategy, a real-time closed-loop collaborative oil reservoir management framework is constructed in combination with the moisture content prediction capability of an HSTMF model, the net present value and the total oil production are effectively increased, the total moisture content is reduced, and good practical application value is shown.

The invention relates to a path planning method fusing D * Lite, simulated annealing and a genetic algorithm. The path planning method comprises the following steps: S1, acquiring map environment information by using an RGB-D camera; s2, modeling is carried out on map environment information; s3, initializing parameters of the D * Lite and a genetic algorithm; s4, performing genetic algorithm population initialization fused with a D * Lite algorithm; s5, the fitness value of the initialized population is calculated, and path redundant point optimization is carried out; s6, applying the generated population to a selection operator of a fusion simulated annealing algorithm, and introducing an elitism strategy; s7, applying the population subjected to operator selection to adaptive crossover and mutation operators; s8, the fitness values of all individuals of the population are calculated again, and path redundant points are optimized again; and S9, judging whether the path meets an optimal condition or not, and outputting an optimal solution after iteration. The method not only has strong global search capability in a complex environment, but also improves local search capability, has good universality and robustness, and meets actual requirements.

The invention relates to the technical field of electric power detection, in particular to an electric power inspection robot detection method and system based on machine vision. According to the method, the three-dimensional model of the target area is constructed by pre-collecting the basic data of the area, the historical defect data is obtained to mark the detection key area, the initial path and the key detection point are optimized by using the genetic algorithm, redundant collection points are removed, and the accuracy of the visual detection path is improved; meanwhile, visual data are analyzed in real time through a path acquisition collaborative analysis module, an optimization instruction is generated to dynamically adjust a path, and the problems of low efficiency and missing detection caused by independent operation of vision and navigation are avoided; the visual perception module collects detection dynamic data of definition, vibration acceleration, environment wind speed and environment light, the environment adaptability module analyzes and outputs a comprehensive fluctuation value, a corresponding fluctuation value interval is obtained, a corresponding environment adaptability regulation and control instruction is generated, and influences caused by motion blur and environment sudden change are avoided.

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 relates to a visual-language model cue word evolution generation method based on a genetic algorithm, which comprises the following steps of: randomly generating different cue words under a target task to construct an initial cue word set, preprocessing the randomly generated cue words, and reserving N groups of cue words as an initial population; and designing a multi-dimensional fitness evaluation function to evaluate the performance quality of each group of cue words in a vision-language task, and selecting high-quality cue word individuals based on an elitism strategy. Performing crossover and mutation operation on the selected high-quality cue word individuals by referring to a genetic algorithm to generate a new-generation cue word population; and finally, carrying out iterative optimization for multiple times until a preset termination condition is met, and outputting an optimal cue word set. By the adoption of the visual-language model cue word evolution generation method based on the genetic algorithm, high-quality and diversified cue words can be automatically generated, the performance of a visual-language model is remarkably improved, and meanwhile the visual-language model cue word evolution generation method has good interpretability and adaptability.

The invention discloses an edge air defense node task allocation method and system based on a genetic algorithm and a contract net method. The method specifically comprises the following steps: establishing a target threat degree estimation and task allocation model; the method comprises the following steps: calculating a multi-platform cooperative task allocation problem by adopting an improved mixed single parent genetic algorithm, through genetic operator design of integer coding + dynamic double populations, tournament selection + reverse-order crossover + adaptive variation and combining elite pool updating of a simulated annealing criterion and a mutation strategy triggered by concentration; aiming at the problem when an air defense platform encounters an emergency situation during task execution, an improved contract net method is adopted, and dynamic air defense task redistribution is realized by expanding a contract protocol process, introducing a multi-Agent collaborative architecture, designing a consistent auction algorithm and a dynamic bidding correction rule and combining a load-sensitive contract exchange mechanism. According to the method, multi-platform cooperative air defense task allocation can be rapidly and effectively carried out, the method dynamically adapts to environmental changes, and the combat effectiveness and combat efficiency of the multi-platform cooperative air defense tasks are improved.

The invention discloses a method for dispatching automatic guided vehicles in a parallel arrangement container terminal. The method comprises the following steps: collecting container terminal AGV dispatching parameters; establishing an AGV scheduling optimization model; solving the AGV scheduling optimization model; and outputting an AGV scheduling scheme. The AGV scheduling optimization model with minimum AGV power consumption and minimum operation completion time as optimization objectives is constructed by considering the influence of AGV path conflicts and the charging process on actual operation, so that collaborative optimization of AGV charging, task assignment and path planning problems is realized, and the method has the characteristics of high universality and wide coverage. According to the method, the improved adaptive genetic algorithm is adopted, and adaptive adjustment of crossover and mutation operators gives consideration to population diversity and convergence rate at the same time. And meanwhile, conflict-free path planning is carried out by adopting a space-time A * algorithm, so that collaborative optimization of AGV task scheduling and path planning is realized, the prediction precision is high, the convergence is fast, and the workload is small.

The invention relates to a comprehensive energy system optimization scheduling method based on an improved particle swarm algorithm, and belongs to the technical field of comprehensive energy system optimization scheduling. The method comprises the steps of constructing an operation model of an integrated energy system with electrical cold and heat as main energy flows, constructing double-target integrated energy system optimization scheduling with the lowest operation cost and the optimal power supply reliability, and solving a multi-target problem through an improved particle swarm algorithm on the premise of meeting power balance constraints and equipment operation constraints. Self-adaptive variation and variable inertia factors are introduced on the basis of a traditional particle swarm algorithm, the self-adaptive variation refers to a variation thought in a genetic algorithm, and variation operation expands a population search space which is continuously reduced in iteration, so that particles can jump out of the position of an optimal value which is searched previously, search is carried out in a larger space, and the search efficiency is improved. The population diversity is maintained, and the possibility of searching the optimal value by the algorithm is improved. And the comprehensive energy system can give full play to economy and reliability under constraint conditions.

The invention discloses a temperature and humidity sensor data high-dimensional feature compression method based on quantum approximate optimization, and the method comprises the steps: inputting a temperature and humidity sensor time sequence data matrix, carrying out the quantum entanglement coding, and generating a quantum state; constructing a variable component sub-circuit of a quantum approximate optimization algorithm based on the quantum state, and dynamically updating circuit parameters through a genetic algorithm to obtain an output quantum state; performing projection measurement on the output quantum state to generate a candidate feature subset, updating a genetic algorithm population through quantum interference, and executing a dynamic crossover operation to generate a new feature subset; calculating a mutation probability by using a quantum gradient, executing an adaptive mutation operation, and optimizing a feature subset; the nonlinear relation between temperature and humidity data features is captured through quantum entanglement coding and quantum interference, dynamic parameter optimization and quantum gradient variation are combined, robust feature selection in a high-noise environment is achieved, feature compression efficiency and precision are improved, and the method is suitable for high-dimensional data processing in an Internet of Things scene.

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

The invention discloses an ultrahigh-toughness nickel-based corrosion-resistant alloy component design method based on machine learning, and belongs to the field of alloy component design. The method comprises the following steps: defining a target of alloy component optimization; an alloy strength prediction model is constructed through a machine learning algorithm and trained, and a trained strength prediction model is obtained through testing; thermodynamic calculation is conducted on multiple groups of alloy components in the target of alloy component optimization, and the precipitated phase volume fraction VF and the average radius R are obtained; inputting the calculated precipitated phase volume fraction VF and average radius R into the trained strength prediction model to obtain the strength value of each combination of alloy components, and performing fitness screening and sorting; the alloy components with high fitness are reserved, the other alloy components are subjected to crossover and mutation operation through a genetic algorithm and then subjected to thermodynamic calculation again with the alloy components with high fitness, and the corresponding precipitated phase volume fraction VF and the average radius R are obtained; and the operation is repeated until the preset maximum number of iterations is reached or the convergence condition is met, and the alloy component with the optimal fitness is selected as the ultrahigh-toughness nickel-based corrosion-resistant alloy component. According to the method, thermodynamic calculation and machine learning are combined to reversely design the nickel-based corrosion-resistant alloy, and efficient optimization of components of the ultrahigh-toughness corrosion-resistant alloy is achieved through a genetic preferential algorithm.

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

The invention belongs to the technical field of indoor positioning, and discloses a Bluetooth path loss model parameter adaptive calibration method and system. According to the method, the historical learning mechanism and the hybrid optimization strategy are fused, so that the global search capability and the local optimization precision are considered while the dynamic updating of the path loss model parameters is realized. Compared with an existing calibration method of a fixed parameter or a single optimization algorithm, the method can automatically correct the model parameters according to the environment change, and solves the problem that the precision of a traditional model is reduced under the conditions of multipath effect, shielding interference and environment sudden change. A hybrid optimization framework of a genetic algorithm and a particle swarm algorithm is introduced, so that a parameter optimization process obtains a high-quality initial value in a global search stage, rapid convergence is realized in a local fine adjustment stage, and the calibration efficiency and precision are remarkably improved. And meanwhile, a historical learning mechanism is added, so that the model has a time memory characteristic, smooth mutation of historical parameter information can be fused, and the stability and robustness of the method in a complex dynamic environment are improved.

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 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 a robot path planning method in a man-machine cooperation environment, and the method specifically comprises the steps: collecting the three-dimensional point cloud data of an assembly environment, calibrating the relation between coordinate systems in the assembly environment, and recognizing an obstacle region; a robot joint envelope body is constructed according to joint connecting rod parameters of the robot and the three-dimensional model, and robot size description and collision detection are simplified; spatial grid non-uniform discretization is carried out on the assembly environment according to the barrier distribution density, grids are coded, the assembly environment is divided into grid spaces with different coarse and fine granularities, and the path search efficiency is improved; a fitness function is designed, a target path search algorithm is established based on a genetic algorithm framework, crossover and mutation operations of adaptive probability are considered, rapid high-quality population iteration is realized, and robot path planning is completed through the target path search algorithm. The robot path planning difficulty in a complex environment is improved, and the quality and smoothness of robot path planning can be improved.

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

The invention discloses 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 discloses a collaborative formation scheduling method based on a distributed optimization algorithm, and relates to the technical field of production scheduling, and the method comprises the steps: dividing a total production task into a plurality of sub-tasks, monitoring the actual utilization rate and actual completion time of each sub-task for the allocated and used resources in real time in the production process, and when an abnormal node is monitored, sending the abnormal node to a server; extracting a direct preorder subtask set as an initial population, calculating a fitness value of an initial scheduling scheme of each subtask in the initial population based on a genetic algorithm, selecting an optimal parent from the initial population through non-dominated grade sorting and crowding degree calculation, and selecting the optimal parent from the initial population; and carrying out crossover and mutation operation and then selecting the scheduling scheme with the maximum fitness value. Based on a real-time monitoring and dynamic adjustment mechanism, abnormal conditions occurring in the production process can be quickly responded, the scheduling scheme is optimized through the genetic algorithm, and the limitation of a traditional static scheduling strategy in a complex production environment is effectively solved.

The invention relates to the technical field of intelligent breeding and logistics optimization control, and solves the technical problems of high energy consumption, unstable efficiency, unbalanced distribution, lack of an intelligent scheduling mechanism and the like in feed conveying of a multi-storey pig farm. The method comprises the following steps: acquiring static parameters (physical characteristics of feed, physical attributes of a conveying system and a pig house structure) and dynamic parameters (real-time feeding requirements, equipment and material states and external environment factors); establishing a multi-objective optimization model of a collaborative optimization energy consumption model E (x), a time model T (x) and a conveying balance degree model U (x); solving by adopting a genetic algorithm with a special design crossover and mutation operator to obtain an optimal scheduling scheme; in the execution process, a closed-loop self-learning calibration mechanism is started, actual power is measured through a current sensor, and when the deviation between predicted energy consumption and actual energy consumption exceeds a preset threshold value, efficiency parameters in the energy consumption model are reversely corrected through a gradient descent method; the weight coefficients of the three models are dynamically adjusted according to the real-time electricity price and the inventory state. The system adopts a three-layer architecture of a perception and data acquisition layer, a decision and control core layer and an execution layer. According to the method, multi-target collaborative optimization and intelligent adaptive scheduling are realized, the total energy consumption is effectively reduced, the transmission time is shortened, the distribution balance degree is improved, and the energy consumption prediction accuracy is remarkably improved.

The method comprises the following steps: obtaining a power quality signal containing noise; selecting permutation entropy as an adaptive function of genetic algorithm, calling variational mode decomposition through genetic algorithm, and iteratively optimizing a penalty factor α and a decomposition mode number k of the variational mode decomposition to determine optimal parameters; decomposing signal data into k mode components through the variational mode decomposition, and determining effective mode components and noise mode components through a correlation coefficient; for improved wavelet threshold, a parameter-adjustable threshold function is proposed, and the concept of wavelet energy entropy is introduced into the threshold function; the noise mode components are denoised through the improved wavelet threshold, and the effective mode components and the denoised noise mode components are reconstructed to obtain a denoised power quality disturbance signal. The method can effectively remove noise interference while retaining singular information of mutation points of the collected signal, and provides help for subsequent analysis and treatment of the power quality disturbance signal.

The invention discloses an underwater multi-target routing method based on an improved non-dominated sorting genetic algorithm. The method comprises the following steps: firstly, arranging sensor nodes in a target water area, networking, and encoding a feasible path from a source node to a target node by adopting a segmented structure; secondly, constructing a routing multi-target fitness function, and introducing a dynamic weighting function to form a comprehensive fitness index; and finally, grading the population by using an improved non-dominated sorting genetic algorithm, and exploring a better path through crossover and mutation operations. And designing a reference point elite selection strategy, improving the coverage of the solution set in the target space, and outputting an optimal routing path until the algorithm converges. According to the method, the dynamic balance among different optimization targets can be realized, and the link quality is considered while the energy consumption and the time delay are reduced, so that the network routing performance is improved.

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 a method for generating an infrared adversarial patch (Compatite Block Patch Generation Method) based on evolutionary optimization, and the method is used for interfering an infrared target detector. The method comprises the following steps that firstly, a population is initialized, each individual is a particle and comprises parameters such as the position, the rotation angle and the color, and the parameters are used for representing the spatial form and texture characteristics of a patch; secondly, generating an adversarial patch in a complex shape by superposing a plurality of basic square patch units, mapping the adversarial patch to the surface of a three-dimensional vehicle model, generating an infrared image through a three-dimensional rendering engine (such as Pytorch3D), then detecting the rendered image by using a target detection model (such as YOLOv5), calculating a loss function formed by detection confidence and patch smoothness, and finally obtaining a target detection result; taking as a fitness evaluation index; then, mutation, crossover and selection operations are carried out by adopting a genetic algorithm, the population is iteratively optimized, and the adversarial performance of the patch is gradually improved; and finally, when a set termination condition (such as the maximum number of iterations or a fitness threshold) is reached, outputting the optimal patch individual, and realizing the strongest interference on the detector. According to the method, the interference capability to an infrared target detector is remarkably improved, and an efficient infrared confrontation sample generation scheme suitable for a three-dimensional model is provided.