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

The invention provides a coal mining digital twinning method based on multi-level dynamic deviation correction and AI optimization. The method comprises the steps of monitoring data acquisition and dynamic preprocessing, and performing denoising and space consistency detection on data; constructing and pre-processing the coal mine digital twinborn body; generating a nonlinear deviation field by adopting a high nonlinear variable interpolation and model mapping method; performing Kalman filtering error analysis and correction under the continuous analysis step, and marking an optimization model state in combination with a space mutation step; performing staged sliding window long-time-sequence fluctuation analysis, and classifying and marking regions according to an absolute value of an error and fluctuation; performing multi-level weighted error optimization based on a genetic algorithm, and adjusting material parameters and boundary conditions; carrying out material constitutive rectification based on dynamic Bayesian, and gradually enhancing the adaptability and accuracy of the model; and constructing a hybrid network structure AI optimization prediction model based on deep data driving, capturing the dynamic change of the material constitutive and the characteristic difference of the model constitutive, and realizing the precise digital twinborn simulation of the whole coal mining process.

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 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 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 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 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 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 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 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 a garment hanging production scheduling method and system considering a transportation distance. The scheduling method comprises the following steps of obtaining and processing information of a garment hanging production workshop to obtain a worker proficiency coefficient matrix and a station distance matrix of the hanging workshop, and obtaining a worker proficiency coefficient matrix and a station distance matrix of the hanging workshop by constructing an optimization objective function, adding a punishment mechanism and defining constraint conditions. And establishing a mathematical model of garment hanger production scheduling considering the transportation distance, and introducing an improved non-dominated sorting genetic algorithm for solving to obtain a scheme of garment hanger production scheduling. According to the multi-objective optimization algorithm adopted by the invention, in combination with the actual layout and production condition of a clothing hanging workshop, aiming at clothing hanging production scheduling, initialization strategies such as optimization of a hanging line clothes hanger running route and introduction of path optimization are fully considered, and improved crossover mutation operation is adopted, so that the number of optimization iterations is small, and the optimization efficiency is high. The method can effectively avoid falling into a local optimal solution, is high in multi-objective optimization capability, and is high in quality of an obtained solution.

The invention relates to the field of computers, in particular to an NIWPSO + CNN + LSTM + Attention-based power load prediction method, which comprises the following steps of: acquiring power data, and performing preprocessing and standardization operation on the power data; the preprocessed data are used as input, and a CNN-LSTM-Attention model is constructed; carrying out hyper-parameter optimization on the CNN-LSTM-Attention model by utilizing an NIWPSO (Non-International World Weighted Particle Swarm Optimization) algorithm; setting a residual error monitoring and dynamic updating mechanism, monitoring the residual error in real time during model prediction, and automatically triggering a retraining process once the residual error exceeds a preset threshold value; a nonlinear variation inertia weight strategy is innovatively adopted, dynamic adjustment is carried out according to an algorithm iteration process and a particle search state, a PSO algorithm is prevented from being caught in a locally optimal solution too early, meanwhile, a mutation thought of a genetic algorithm is used for reference, mutation operation is carried out on particles in a one-dimensional mode, the algorithm is assisted to efficiently obtain a globally optimal solution in a complex solution space, and model parameter optimization is guaranteed.

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 micro spring washer production method based on genetic algorithm optimization, and the method comprises the following steps: S1, initializing a technological parameter set, and generating a preliminary optimization scheme through a morphological optimization control model; s2, mapping high-dimensional parameters to a low-dimensional morphological feature space through self-organizing mapping, and implementing adaptive variation adjustment; s3, integrating the processed morphological characteristics and technological parameters into chromosome codes, and performing multi-generation iterative optimization; s4, combining an evolutionary game model and fuzzy logic control, and dynamically adjusting a process parameter combination; s5, cooperatively regulating and controlling technological parameters through crossover and mutation operations; s6, performing fitness evaluation, and adjusting genetic algorithm parameters; and S7, after a convergence condition is reached, outputting an optimal process parameter, and applying the optimal process parameter to actual production. The method can significantly improve the production efficiency and the product quality, and is suitable for the precision manufacturing field of complex process optimization.

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.

A technique for solving combinatorial problems, such as vehicle routing for multiple vehicles integrates evolutionary algorithms and reinforcement learning. A genetic algorithm maintains a set of solutions for the problem and improves the solutions using mutation (modify a solution) and crossover (combine two solutions). The best solution is selected from the improved set of solutions. A system that integrates evolutionary algorithms, such as a genetic algorithm, and reinforcement learning comprises two components. A first component is a beam search technique for generating solutions using a reinforcement learning model. A second component augments a genetic algorithm using learning-based solutions that are generated by the reinforcement learning model. The learning-based solutions improve the diversity of the set which, in turn, improves the quality of the solutions computed by the genetic algorithm.

The invention discloses a multi-logistics-vehicle dispatching method based on an accessibility matching cross genetic algorithm, and the method comprises the steps: building a multi-logistics-vehicle dispatching model with access constraints based on a scene that a customer has preference demands for logistics vehicle services due to the difference of logistics categories in practical application; a preference relationship of clients to logistics vehicles is described by using an accessibility matrix, crossover operation and mutation operation based on accessibility matching are designed, and a dispatching scheme of multiple logistics vehicles is optimized by embedding a genetic algorithm. Through the above operation, customer preference demands can be comprehensively considered in the iteration process, the total path of the multiple logistics vehicles and the path balance of the logistics workshops are optimized at the same time, and a globally optimal multiple logistics vehicle dispatching scheme is quickly found. According to the method, the solving efficiency of the multi-logistics vehicle dispatching problem with the access constraint is greatly improved, the dispatching efficiency of the logistics vehicles is effectively improved, and the transportation cost is 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 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 relates to the field of friction stir welding process parameter optimization, in particular to an aluminum alloy friction stir welding process parameter optimization method which comprises the steps of obtaining experimental data, training a multi-feature subset collaborative network and finding an optimal process parameter combination. Aiming at the problems that training samples of aluminum alloy friction stir welding are insufficient and a neural network is difficult to accurately predict the quality of a welding joint, a multi-feature subset collaborative network is provided, and the number of the training samples is implicitly increased and the accuracy of a prediction result is improved by constructing multiple feature subsets and a main network and sub network collaborative work method; in order to solve the problems that the change range of technological parameters is large, the precision requirement is high, and the optimal solution is difficult to obtain through a traditional optimization algorithm, the technological parameters are optimized through an improved genetic algorithm, the fitness serves as a selection index of filial generations in interlace operation, a self-adaptive mutation strategy is introduced in mutation operation, and filial generation genes are selectively mutated, so that the optimal solution is obtained. And the practical application effect of process parameter optimization is improved.

The invention provides an LED multi-light-color regulation and control method and system based on a multi-objective evolutionary algorithm, and the method comprises the steps: firstly obtaining the spectrum and electric power data of each single-color LED, and carrying out the preprocessing of the data through combining a cubic spline interpolation method and a fitting function; secondly, optimizing light color indexes such as color coordinates, color rendering index, color temperature, SDCM value and power consumption by designing an evaluation function; the core of the algorithm is to iteratively optimize the population through individual evaluation, survival of the fittest, heredity and mutation operations by using a multi-target genetic algorithm until an optimal PWM combination meeting all light color index requirements is found. Specifically, the method has the advantages of being high in flexibility, good in expansibility and the like, can adapt to different LED channel configurations, provides a light color regulation and control scheme with high pertinence, is particularly suitable for factory regulation of fluorescent powder-free multi-primary-color LED lamps, and provides an efficient and automatic solution for light color optimization.

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.