1350 results about "Fitness function" patented technology

The present invention relates to the technical field of circuit control, and relates to a direct-current high-voltage generator regulation and control method and system based on intelligent feedback. A fuzzy control logic operation is performed on an output current flux value and an input voltage fluctuation factor of a load experimental object, so as to obtain the disturbance amplitude of a voltage fluctuation state for a current flux change trend; if a step-up transformer can execute a PID control technology by adjusting a transformation ratio, PID real-time adjustment parameters are calculated, and a crossover mutation operation of individuals is performed on the basis of a fitness function, so as to generate a first boost regulation and control scheme; and if the step-up transformer cannot execute the PID control technology by adjusting the transformation ratio, a calibrated state feedback controller is constructed on the basis of a state variable of the current step-up transformer and an adjustment signal output amplitude, so as to generate a second boost regulation and control scheme. The present invention achieves intelligent feedback regulation and control by independently analyzing components of direct-current high-voltage generators, improving the stability and accuracy of outputting high-voltage direct current.

The invention discloses a method for generating an autonomous inspection orthoimage of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle surveying and mapping and autonomous navigation. Global optimization of an air route is realized through a path optimization strategy fused by a genetic algorithm in combination with grid characteristics of an inspection area and image parameter constraints, and candidate waypoints are used as nodes; redundant waypoints are screened through leg smoothness factors, route complexity is reduced, a genetic algorithm takes flight height and waypoint spacing as constraints, a fitness function containing flight distance, turning times and overlapping rate is constructed, a global optimal path is found through population iteration, multi-target requirements are optimized and balanced, and it is ensured that the route meets the image acquisition precision requirement and also meets the requirement of image acquisition. The flight distance can be shortened, the turning frequency is reduced, the cruising ability of the unmanned aerial vehicle is adapted, efficient propelling of the inspection task is guaranteed, meanwhile, waypoint coordinates output through simulation directly adapt to a flight control system, and it is guaranteed that actual flight parameters are consistent with planning parameters.

The invention discloses an unmanned aerial vehicle three-dimensional path planning method based on an improved artificial travel mouse optimization algorithm, belongs to the technical field of unmanned aerial vehicle path planning methods, and aims at enabling an unmanned aerial vehicle to autonomously navigate, avoid obstacles, find an optimal path and improve task execution efficiency and safety in a complex environment. A constraint condition and a target function of unmanned aerial vehicle three-dimensional path planning are mapped into a search space and a fitness function of an optimization algorithm, an optimal flight path is searched by adopting an artificial travel mouse optimization algorithm, and optimization calculation is performed on the path in combination with unmanned aerial vehicle performance constraints, topographic features and meteorological conditions; aiming at a three-dimensional path planning problem of an unmanned aerial vehicle under a complex condition, an artificial travel mouse algorithm is adaptively improved, and algorithm parameters and structures are respectively adjusted according to mathematical characteristics of a problem model in the aspects of initialization, exploration and development; the flight path is adjusted based on environment change and self state information sensed by a sensor in real time, and the safety and task requirements are met.

The invention discloses a non-direct connection-oriented data routing method, which comprises the following steps of: S1, acquiring topological structures, link bandwidths, time delays, loads and energy consumption states of nodes in a network in real time, and constructing a time sequence dynamic matrix of the nodes and links; s2, on the basis of the dynamic matrix, adopting a neural network adaptive enhanced ant colony optimization method to generate alternative paths; s3, generating a grey wolf optimization algorithm initial population by using the alternative paths, and constructing a multi-dimensional composite fitness function; s4, according to the fitness function, driving the grey wolf optimization algorithm to perform multi-scale iteration to update the path; s5, topology and node state prediction is carried out based on the dominant path, and the prediction path is optimized in advance; and S6, issuing the optimal path and the alternative path at the same time, carrying out data parallel forwarding, and driving a neural enhanced ant colony optimization algorithm to update online. The method improves the network path selection efficiency and the resource utilization rate, and is suitable for data routing in a complex network environment.

The invention provides an energy optimal path planning method and device for an unmanned surface vessel in a complex marine environment, and belongs to the technical field of path planning. The method provided by the invention comprises the following steps: constructing a comprehensive energy consumption model; constructing a hybrid enhanced particle swarm optimization algorithm; constructing a safety space set and an adjacent graph through a grid method, generating an initial optimal path by adopting an A * algorithm, and carrying out bounded random disturbance and safety correction on target particle waypoints; smoothing the path generated by iteration by adopting a B-spline technology, resampling the smoothed optimal solution, and then reinjecting the optimal solution into the population; adjusting an inertia weight and a learning factor based on the number of iterations, and introducing double guide factors to carry out secondary adjustment on a cognitive item of particle speed updating; and embedding the comprehensive energy consumption model as a fitness function into a hybrid enhanced particle swarm optimization algorithm, performing real-time energy consumption evaluation, individual and global optimal path updating and path smooth optimization on each candidate path in an algorithm iteration process, and outputting an optimal navigation path of the unmanned surface vessel.

The invention discloses a non-standard weldment-oriented robot efficient collision-free path planning method and system, and belongs to the technical field of robot welding, and the method comprises the following steps: S1, obtaining three-dimensional model data of a non-standard weldment and an operation environment, and extracting welding seam point cloud data; s2, carrying out feature extraction and downsampling on the welding seam point cloud data to generate a group of key path points; s3, global optimization is conducted on the welding path through an improved genetic algorithm, candidate paths are generated, and the welding sequence of the multiple sections of welding seams and the welding gun postures at the key path points are optimized at the same time through the improved genetic algorithm; s4, evaluating the candidate paths generated by the improved genetic algorithm through a multi-target fitness function including path length, attitude stability and collision risk; and S5, outputting an optimal collision-free welding path according to an evaluation result, and converting the optimal collision-free welding path into a smooth track which can be executed by the robot. According to the invention, the high-efficiency, high-quality and collision-free welding gun motion trail can be automatically generated.

The invention provides an aircraft aerodynamic configuration design method and device based on simulation, and relates to the technical field of aircraft aerodynamic configuration design, and the method comprises the steps: obtaining aerodynamic characteristic parameters of aircrafts of different airfoils to construct a multi-source aerodynamic database; a neural network model is established based on the database, airfoil geometric parameters serve as input, the lift coefficient, the resistance coefficient and the lift-drag ratio serve as labels for training, and an aerodynamic performance prediction model is obtained; candidate combinations are generated in an airfoil geometric parameter design space, aerodynamic characteristics of the candidate combinations are predicted through a model, a fitness function is optimized by utilizing dynamic weights, and the combination with the highest fitness is selected as an optimized airfoil parameter by adopting a genetic algorithm; and performing simulation verification on the optimization parameters, extracting a simulation result, comparing the simulation result with the output of the prediction model, judging that the optimization combination is effective when an error meets a preset requirement, and completing aerodynamic configuration design accordingly. The method improves the prediction accuracy and design efficiency of aerodynamic performance.

The invention discloses a millimeter wave radar vital sign detection method based on an HHO-CEEMDAM algorithm, and the method comprises the steps: building a millimeter wave radar experiment system, and collecting an intermediate frequency signal of a human body echo; performing data reading and recombination, extracting phase features, and enhancing a target signal through non-coherent accumulation to determine the distance between the chest of the human body and the radar; recovering the phase of the vital sign signal from the incoherent accumulation FFT result by using the linear characteristic of arc tangent demodulation, and performing phase unwrapping and phase difference to obtain optimized phase information; setting a CEEMDAN parameter initialization range and designing a fitness function; a CEEMDAN parameter is optimized by using an HHO algorithm; performing CEEMDAN decomposition by using the optimized parameters, and screening breathing and heartbeat IMF components; and according to the IMF component, obtaining estimated values of the respiratory rate and the heart rate. The method improves the estimation precision of the respiratory rate and the heart rate of the millimeter wave radar in fatigue driving detection.

The invention belongs to the technical field of radar signal processing, and discloses a sea surface small target detection method based on optimized characteristic mode decomposition, which comprises the following steps: S1, acquiring to-be-detected signal data; s2, decomposing an original signal into a plurality of modal components by using FMD, and selecting an envelope spectrum entropy as a fitness function; s3, performing global optimization on the fitness function in the FMD by using an SOS algorithm; s4, introducing a PSO algorithm to carry out local optimization on key parameters of the FMD; s5, components with low envelope spectrum entropy values and correlation coefficients larger than a threshold value are reserved; s6, extracting an envelope spectrum entropy and frequency band energy ratio feature from the screened modal components, introducing a Gini coefficient as a weighting factor, and constructing a GSEBE joint feature; and S7, inputting the entropy value of the envelope spectrum into a DELM classifier with a controllable false alarm, and realizing target detection based on comparison between a predicted value and a judgment threshold. According to the invention, the capability of distinguishing sea clutters and target echoes is enhanced, and more accurate classification detection is realized.

The invention discloses a middle and primary school test question intelligent generation method based on an education big model. The method comprises the steps of generating a structured education data set; constructing an education large model specially used for intelligent generation of middle and primary school test questions; inputting target knowledge points, question types, difficulty and grade parameters, and generating a candidate test question set; setting a test question fitness function, comprehensively evaluating the knowledge coverage rate, difficulty distribution and language expression indexes of the candidate test questions, screening and optimizing the candidate test question set, and generating an optimized candidate test question set; detecting semantic accuracy, logic preciseness and teaching conformity of the test questions by using an automatic quality evaluation mechanism, and outputting test question evaluation results; and continuous optimization and self-adaptive updating of test question generation are realized. According to the method, the optimal multi-question type test question set can be automatically output on the premise of ensuring comprehensive coverage of knowledge points, reasonable difficulty distribution and excellent language quality, and the quality, balance and diversity of automatically generated test questions are greatly improved.

The invention discloses a small sample self-learning accurate identification method based on distillation knowledge migration. The method comprises the following steps: S1, extracting deep semantic features of a source domain and shallow features of a small number of samples of a target domain, and calculating a mapping matrix; s2, calculating an entropy difference distillation excitation function based on the initial alignment features; s3, executing domain knowledge distillation and generating staged distillation representation; s4, constructing a composite fitness function and initializing a parameter population; s5, performing iterative optimization by adopting a variable step size dynamic feedback compression strategy; s6, loading the optimal parameters and performing coupling alignment with the historical distillation representation; and S7, performing combined fine adjustment on the distillation weight and the model parameters through self-learning feedback. According to the method, through adaptive knowledge distillation and dynamic optimization feedback closed loop, high-precision and adaptive identification under extremely few labeled samples is realized, the generalization ability of the model is remarkably improved, and overfitting is effectively inhibited.

The invention discloses a die forming process parameter diagnosis method based on a genetic algorithm, and particularly relates to the field of data analysis. Comprising the steps of multi-source heterogeneous data fusion and enhancement processing, multi-target dynamic fitness function construction, constraint adaptive genetic algorithm optimization, process parameter-defect association mapping, online transfer learning optimization, dynamic environment online correction and man-machine collaborative decision verification. According to the method, collaborative optimization of the size, the energy consumption and the cycle time is realized through the multi-target dynamic fitness function, and the practicability and the flexibility of a parameter scheme are remarkably improved; a closed-loop learning system is constructed by utilizing transfer learning and a historical case library, so that the generalization ability and the result reliability are enhanced; temperature and humidity fluctuation is compensated in real time through an environment online correction function, the robustness and stability of the production process are greatly improved, and finally a comprehensive solution with intelligent optimization and manual intervention capabilities is formed.

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

The invention discloses a pavement maintenance decision-making method, system and equipment, a medium and a product, and relates to the field of highway engineering management. The method comprises the following steps: firstly, collecting performance data of a target road section, and identifying a to-be-optimized pavement maintenance unit through a threshold judgment method or a K-means clustering algorithm; encoding each maintenance measure type and the corresponding maintenance opportunity into a real number vector, and taking the real number vector as a maintenance scheme code; constructing a multi-target fitness function covering pavement performance, maintenance cost and carbon emission; carrying out iterative optimization on the maintenance scheme through a particle swarm optimization algorithm on the basis, and outputting a particle swarm optimization solution set; performing rapid non-dominated sorting and congestion degree distance calculation on the particle swarm optimization solution set, and extracting a Pareto optimal solution set; and fusing the Pareto optimal solution and the full-life-cycle comparison data of the target road section, and outputting a maintenance decision scheme for each pavement maintenance unit, so that the decision efficiency and the scientificity, accuracy, sustainability and refinement degree of the maintenance decision scheme are improved.

The invention relates to the technical field of medical information, and discloses a knowledge graph driven intelligent analysis system based on the medical field, and the system comprises a knowledge graph construction module which is used for constructing a medical knowledge graph containing a subject, an event, an object entity and an information reachable relation; the behavior pattern learning module is used for learning a parameterized behavior decision model for the subject entity; the dynamic deduction module is used for deducing the market change after the event is injected according to the behavior decision model and the information reachable relation; and the strategy generation module is used for reversely generating a market strategy by taking the dynamic deduction process as a fitness function. The method comprises the following steps: constructing the medicine field knowledge graph; learning a behavior decision model of the subject entity based on a graph; dynamically deducing the market change in a virtual environment; and reversely solving and generating the market strategy. According to the method, prospective dynamic deduction can be carried out on the market, the optimization strategy is actively generated, and the scientificity and timeliness of decision making are improved.

The invention provides an adaptive stochastic resonance weak fault detection method based on kurtosis optimization, and belongs to the technical field of fault detection, and the method comprises the steps: collecting and preprocessing a transient signal on a cable line; constructing a bistable stochastic resonance model; kurtosis of the output signal is used as a fitness function, and potential well parameters of the bistable stochastic resonance model are optimized based on a particle swarm optimization algorithm; and substituting the optimized potential well parameter into the bistable stochastic resonance model, and processing the preprocessed transient signal to obtain an enhanced output signal for fault detection. The method has the beneficial effects that the stochastic resonance system is constructed on the basis of the classical bistable model, the kurtosis of the output signal is used as the fitness function of the particle swarm optimization algorithm, the potential well parameters are adaptively optimized, the potential well parameters are driven to automatically converge to the optimal state, and the robustness of the system is improved. The high-impedance grounding fault traveling wave signal can be extracted and enhanced from a strong noise background, and the detection sensitivity and reliability are improved.

The invention discloses a spatio-temporal crime prediction method and system fusing spatio-temporal heterogeneous information. The method comprises the following steps: constructing a spatio-temporal data set; constructing a crime time sequence signal data set; periodically decomposing the time sequence signal into a plurality of intrinsic mode functions (IMF); the sample entropy is used as a fitness function to evaluate the advantages and disadvantages of decomposition results under different parameter combinations so as to determine the optimal input time window length; performing clustering processing on the crime data to identify urban crime hotspot areas, and generating crime spatial distribution data; performing feature analysis in time and space, and further completing contribution evaluation of key features to the crime situation; constructing a space-time prediction model, and segmenting a three-dimensional space-time data set into samples in a sliding window mode according to a time window determined in a self-adaptive mode to serve as input and output of the model; and completing training and tuning of the model. And a more accurate and efficient solution is provided for urban crime situation analysis.

The invention relates to a digital intelligent regulation and control preparation method and system for a high-solid-waste low-carbon high-performance grouting material, and solves the problems that a traditional preparation technology lacks an autonomous and controllable digital intelligent regulation and control system and is weak in adaptability to fluctuation of raw materials, and the method comprises the following steps: obtaining XRF chemical components and XRD mineral composition data of industrial solid waste raw materials; constructing a material gene database; based on the database, screening a proportioning scheme by using a performance prediction model, and predicting workability, strength development and shrinkage performance; inputting a prediction result as a fitness function into a multi-objective optimization algorithm, and outputting an optimal material gene combination; a batching scheme is generated based on the optimal combination, and a stirring process is started after technological parameters are preset; collecting data through a real-time monitoring system, and comparing the data with the digital twin model; and based on a comparison result, automatically adjusting material proportioning parameters. The high-solid-waste, low-carbon and high-performance grouting material has the advantages that accurate design and regulation of the high-solid-waste, low-carbon and high-performance grouting material are achieved, material performance is improved, and carbon emission and cost are reduced.

The invention discloses a satellite beam scanning scheduling method and device for preventing same-frequency interference and a storage medium. The method belongs to the field of satellite communication, and comprises the following steps: determining sub-bands respectively adapted to a plurality of beams of a satellite; initializing a particle swarm, wherein each element of a particle vector of the particle swarm indicates a beam position scanned by each beam of the satellite in each beam hopping time slot of a beam hopping period through different numerical intervals; a fitness function is constructed, and the function value of the fitness function is related to the severity of the same-frequency interference in the satellite coverage area; performing iterative optimization on the particle swarm according to a particle swarm optimization algorithm, and determining an optimal particle vector; and according to the optimal particle vector, determining the beam position scanned by each beam in each beam hopping time slot of the beam hopping period. According to the invention, by optimizing the beam position scanning scheduling scheme of the beam in the beam hopping period, the coverage overlapping and signal cross interference of adjacent same-frequency sub-beams are effectively avoided.

The invention discloses a multi-degree-of-freedom mechanical arm control method based on quantum particle swarm optimization of a migration strategy. The method comprises the steps that system modeling is conducted; mPC to-be-adjusted parameter definition and constraint processing are carried out; adopting, adopting, and introducing penalty terms to construct a composite fitness function; updating particle positions by adopting a basic or enhanced quantum updating mode; triggering conditions are judged in a group diversity measurement mode, and when the conditions are met, a migration strategy and dynamic subgroup division are executed; the global optimal solution and the fitness value are loaded into a real-time MPC controller, and online adjustment is carried out; a prediction equation is constructed, tracking errors and energy consumption optimization are converted into a standard quadratic programming problem, and the solving precision is dynamically adjusted in combination with Cholesky pre-decomposition, a structured sparse solver and a warm-start and early stop strategy; and a closed-loop adaptive control system is constructed. According to the method, the precision, robustness and real-time performance of trajectory tracking control of the mechanical arm can be effectively improved.

The embodiment of the invention provides a multi-dimensional constrained interface layout fitness function method and device, and the method comprises the steps: generating a plurality of initial layout populations based on a layout instruction and a layout constraint condition, receiving an interface layout fitness calculation rule and a constraint factor corresponding to the interface layout fitness calculation rule, obtaining an element coordinate corresponding to each interface element in each initial layout population, determining an element weight of each constraint factor corresponding to each interface element in each initial layout population based on the layout instruction, and determining interface layout sub-fitness corresponding to each constraint factor in each initial layout population; and based on the interface layout fitness calculation rule and the interface layout sub-fitness, determining the interface layout fitness corresponding to each initial layout population and determining a target layout population, and based on the target layout population, optimizing the layout population generated based on the layout instruction. According to the method, the defect that complex constraints are difficult to process is effectively overcome, and the generalization ability of interface layout is remarkably improved.

The invention discloses a micro-nano calibration satellite constellation cooperative control method based on GA and DRL coupling optimization, and relates to the field of satellite networking and constellation optimization design. The method comprises the following steps: constructing a micro-nano satellite constellation task model based on constellation information data; performing global optimization on the constellation parameter vector according to the fitness function in a full constellation parameter space by adopting a genetic algorithm based on the micro-nano satellite constellation task model to obtain an initial constellation configuration set; determining a strategy parameter set according to the initial constellation configuration set by adopting a deep learning method; and carrying out in-orbit deployment according to the strategy parameter set, carrying out state estimation on each satellite, carrying out dynamic updating on the satellites based on the satellite execution control quantity, and completing task allocation and maneuvering negotiation through inter-satellite links by adopting a satellite distributed cooperative strategy so as to realize constellation-level execution control. According to the invention, constellation-level optimal configuration and autonomous cooperative control can be realized, and the in-orbit calibration capability of the micro-nano satellite constellation is improved.

The invention discloses a fuzzy neural network PID photovoltaic series welding temperature control method based on improved PSO optimization. The method comprises the steps that a target photovoltaic series welding equipment heating transfer function model is acquired; setting an initial PID parameter; adjusting a PID increment parameter of the PID control module in real time according to the temperature error, the temperature error change rate and a preset fuzzy rule base; the neural network is combined with fuzzy control, and parameters and rules of fuzzy control are automatically modified through training data; according to the photovoltaic series welding temperature condition, the fitness function of the PSO algorithm is improved, the adjustment mode of the inertia weight is improved, and the improved PSO algorithm is used for optimizing the initial PID parameters of fuzzy neural network PID photovoltaic series welding temperature control. Self-adaptive precise control and robust control of the welding temperature control system are achieved, the temperature fluctuation phenomenon in the photovoltaic series welding process is effectively improved, and the robustness and control reliability of the system are enhanced.

The invention discloses a multi-unmanned aerial vehicle route planning method in a multi-wind area environment. The method mainly comprises the following steps: establishing a three-dimensional map of typical task areas of unmanned aerial vehicles, dividing the areas, setting different wind directions and wind speeds, setting a plurality of unmanned aerial vehicles, and setting a fixed starting point and a fixed target point; randomly distributing a target point for each unmanned aerial vehicle by using an evolutionary algorithm, calling an improved RRT * algorithm for each unmanned aerial vehicle, and searching a feasible leg from the starting point to the target point in the three-dimensional map; iterating the distribution schemes of different target points by using an evolutionary algorithm and a fitness function to obtain an optimal target point distribution scheme, and further generating a flight path of the unmanned aerial vehicle; and performing path stretching and cubic B-spline interpolation on the initial route of each unmanned aerial vehicle to obtain route planning of the multiple unmanned aerial vehicles.

The invention discloses a small modular prismatic high-temperature gas cooled reactor core optimization design method and system based on data driving. The method comprises the following steps: constructing a training data set; a data-driven agent model is trained to replace and execute high-cost simulation calculation; and exploring a design space and generating a group of Pareto optimal candidate solutions by using a multi-objective evolution algorithm and taking the proxy model as a fitness function evaluator. The key feature of the method is a closed loop of iterative verification and model updating: verifying the candidate solution through high-fidelity simulation, if the prediction precision does not meet the convergence criterion, expanding the verified new data point to a training data set, and retraining the agent model. According to the method, the calculation cost can be remarkably reduced, a complex design space can be efficiently explored, and a physically reliable reactor design scheme with better performance can be obtained.

The invention discloses a fruit and vegetable mature period prediction method based on deep learning. The method comprises the following steps: S1, obtaining a preprocessed fruit and vegetable growth environment and state data set; s2, constructing multi-scale time window statistical features based on the preprocessed fruit and vegetable growth environment and state data set, and performing feature fusion on the multi-scale time window statistical features and the preprocessed fruit and vegetable growth environment and state data set to generate a fusion feature vector; s3, constructing a prediction model structure; s4, initializing a grey wolf optimization algorithm search space, taking the preprocessed fruit and vegetable growth environment and state data set as a training sample, taking a prediction error index as a fitness function, and iteratively searching the search space to obtain an optimal hyper-parameter set, so as to obtain a trained prediction model; and S5, inputting fruit and vegetable growth environment and state data collected in real time into the trained prediction model, outputting a fruit and vegetable mature period prediction result, and generating a mature period prediction time sequence. According to the invention, it is ensured that the prediction result is accurate and stable, so that high-reliability support is provided for agricultural picking management.

The invention provides a hub variable pitch bearing bolt joint optimization method, system and device and a medium, and belongs to the technical field of bearings. A mechanical model and a dynamic coupling relation of a wind wheel system are constructed; establishing a multi-objective optimization function, and setting constraint conditions; based on an adaptive variation strategy optimization algorithm, setting a multi-stage search strategy, and defining a multi-layer constraint fitness function to embed a multi-objective optimization function and constraint conditions into an algorithm solving framework in combination with global search and local optimization; the improved spider bee optimization algorithm is applied to an operation characteristic model of the double-drive variable pitch system, a hub parameter set is generated, and a local stress concentration area occurring in the optimization process is recognized and avoided; performing dynamic simulation verification on an optimization result; and outputting the design scheme after the simulation verification is passed. According to the invention, a combined optimization mode based on an intelligent algorithm of a wind wheel key component and a multi-objective optimization mechanism is provided, and the balance of the design performance of the overall structure of the hub and the engineering applicability are improved.

The invention discloses a coding imaging reconstruction method and system based on coupling of a neural network and a genetic algorithm, and relates to the technical field of coding image reconstruction. The method comprises the following steps: inputting a coded image of a scene to be reconstructed into a pre-trained preliminary reconstruction convolutional neural network to obtain an initial source distribution image of the scene to be reconstructed, and disturbing the initial source distribution image to obtain multiple candidate source distribution images; the multiple candidate source distribution images serve as an initial population, the maximum fitness function value serves as a target, optimization is conducted on the source distribution image of the scene to be reconstructed through an improved genetic algorithm, and a target source distribution image of the scene to be reconstructed is obtained; the improved genetic algorithm comprises the step of cooperatively guiding population breeding by adopting residual convolutional neural network guided search and traditional genetic operation; and the residual convolutional neural network is used for learning a residual change rule of the source distribution image in an iterative optimization process. According to the method, the imaging speed can be remarkably improved while the reconstruction precision of the coded image is ensured.

The invention relates to an unmanned aerial vehicle group cooperative surveying and mapping path planning method and system adopting reinforcement learning and application, and relates to the technical field of unmanned aerial vehicle surveying and mapping, and the method comprises the steps: randomly deploying a plurality of unmanned aerial vehicle group cooperative surveying and mapping paths covering a target area based on the constraints of unmanned aerial vehicle navigation boundaries and the like; constructing a fitness function by taking the routing inspection path length and the like as fitness factors, and evaluating a fitness value; when convergence is not satisfied, clustering the paths based on the fitness value, and constructing a multi-cluster unmanned aerial vehicle group cooperative surveying and mapping path; sorting the paths according to the fitness from good to bad, generating first-level to N-level paths, and constructing a reward tree; and updating the paths from the second level to the Nth level based on the reward tree, and circulating until a target surveying and mapping path meeting the convergence fitness is generated and output. According to the method, the problems of high trial and error cost and difficulty in convergence caused by a large number of clusters and difficulty in searching reward points in traditional unmanned aerial vehicle group cooperative surveying and mapping path planning are solved, and an effective solution is provided for intelligent path planning of unmanned aerial vehicle group cooperative surveying and mapping.

The invention provides a proportioning component high-throughput optimization design method and system for concrete super-multi-target requirements, and relates to the technical field of civil engineering material and artificial intelligence combination. Comprising the following steps: (1) constructing a fitness function of key performance required by a concrete material project; (2) establishing a constraint system of knowledge and numerical values; (3) constructing a material continuous and discrete constraint nested concrete super-multi-target proportioning component optimization model; (4) solving a population of the concrete component proportion optimal solution set oriented to the multiple targets required by the project; and (5) performing high-throughput optimization of approximate Pareto frontier, a good and inferior solution distance method (TOPSIS), carbon emission model calculation and cost polynomial calculation on the component proportion optimization population. According to the method, effective design of concrete raw material types and mix proportion parameters which comprehensively consider a plurality of key performance requirements of concrete in engineering and carbon emission environmental and cost economy requirements can be realized.