37 results about "Gaussian mutation" patented technology

The invention provides an image enhancement optimization method based on multi-strategy improved sparrow search. The method comprises the following steps: performing normalization processing on a gray value of each pixel point of an original image; initializing a sparrow population and parameters according to an elite chaos reverse learning strategy; the method comprises the following steps: preprocessing an input image by adopting a dual gamma function BiGA; calculating the value of the evaluation function; updating the optimal fitness value of the sparrow population; when an iteration stop condition is not met, updating the position of the sparrow population by adopting a probabilistic following strategy, a self-adaptive local search strategy and a Cauchy Gaussian mutation strategy, and updating parameters of a dual gamma function BiGA; otherwise, ending iteration, and calculating an optimal parameter; processing the image, and outputting an enhanced image; according to the method, the visual effect of the image can be improved, edge details and textures can be richer, and the enhanced image can have higher contrast and better definition. The method is best in overall enhancement effect, and the obtained image is better in quality.

The invention relates to a multi-task allocation method and device for unmanned transportation equipment, equipment and a medium, and the method comprises the steps: constructing a multi-task combination model, and setting related constraint conditions; obtaining an initial population through chaotic mapping; performing rapid non-dominated sorting on the initial population, and based on a dynamic congestion degree sorting strategy, selecting and determining a plurality of parent populations through a champion; for each parent population, generating a new individual through adaptive crossover operation, and then performing mutation operation through Gaussian mutation to obtain a filial population; combining the parent population and the child population, performing rapid non-dominated sorting operation, and screening excellent individuals through a probability distribution function to obtain a new parent population; performing iterative computation based on the new parent population, and gradually optimizing a result to obtain an optimal multi-task allocation scheme; and performing multi-task allocation on the unmanned transportation equipment through the optimal multi-task allocation scheme. The method can improve the transportation efficiency, and balance the task scheduling complexity and the utilization rate of unmanned transportation equipment.

The invention discloses a tractor transportation operation working condition construction method based on improved particle swarm optimization and KMeans fusion, and relates to the technical field of agricultural machinery working condition analysis. The method comprises the following steps: acquiring original data of tractor transportation operation through a plurality of data acquisition modes, and carrying out preprocessing and three-stage screening to obtain an effective kinematics fragment; selecting multi-dimensional characteristic parameters to construct a characteristic matrix, and performing data dimension reduction by adopting principal component analysis; optimizing a KMeans clustering initial center by using an improved particle swarm optimization (IPSO) algorithm which introduces a dynamic inertia weight and a Gaussian mutation strategy, and performing clustering analysis on the feature space after dimension reduction; and selecting representative fragments based on feature similarity, and synthesizing a standardized working condition curve by taking the sum of average relative errors of all feature dimensions as a target function. According to the method, the problems that a traditional clustering algorithm is prone to falling into local optimum and the working condition construction accuracy is insufficient are solved, the constructed working condition can truly and comprehensively reflect the actual transportation operation characteristics of the tractor, and a reliable basis is provided for tractor power system optimization, operation efficiency improvement and energy consumption reduction.

The invention discloses a permanent magnet synchronous motor parameter optimization method based on an improved moth flame particle swarm algorithm. In order to solve the problems that a traditional PID controller is difficult in parameter setting, poor in adaptability, insufficient in control precision and the like under complex working conditions, in the strategy, an MFPSO algorithm fuses a particle swarm optimization (PSO) algorithm and a moth flame optimization (MFO) algorithm. An adaptive inertia weight and Gaussian mutation strategy is introduced, so that particles can focus on local fine search, and the speed of convergence to an optimal solution is accelerated. The optimization capacity of the algorithm in different stages is enhanced by adopting a nonlinear acceleration coefficient, and local optimum is effectively avoided. A grouping evolution mechanism is designed to promote intra-group information sharing and inter-group competition, the population diversity is enriched, and the ability of the algorithm to jump out of a local optimal solution is further improved. The fuzzy PID controller based on the MFPSO algorithm optimization parameters can better meet the requirements of high-performance control of permanent magnet synchronous motors in different fields, and has application prospects and economic values.

The invention discloses a wind power prediction method and system based on an improved sparrow algorithm, and relates to the technical field of wind power generation, and the method comprises the steps: collecting and preprocessing the historical operation data of a wind power plant, and constructing a wind power prediction model; the method comprises the following steps: introducing Tent chaotic mapping into a traditional sparrow search algorithm, and introducing adaptive parameters into the Tent chaotic mapping to obtain a preliminary improved sparrow search algorithm; integrating Gaussian mutation operation to obtain a final improved sparrow search algorithm; and using the final improved sparrow search algorithm to optimize the wind power prediction model, and using the optimized wind power prediction model to predict the wind power. According to the invention, through multi-level algorithm improvement and optimization strategies, the problem that a traditional sparrow search algorithm is easy to fall into local optimum is successfully solved; by introducing Tent chaotic mapping and adaptive parameters, the initial population position is optimized, and the global search capability is improved; gaussian mutation operation is integrated, population diversity is maintained, and the capability of jumping out of local optimum is enhanced.

The invention discloses an active power distribution network energy storage optimization configuration method and device, equipment and a medium, and relates to the field of power distribution network energy storage optimization, and the method comprises the steps: obtaining the power flow distribution of an active power distribution network at a current moment; constructing an energy storage optimization configuration model of the active power distribution network; based on the power flow distribution at the current moment, an improved butterfly optimization algorithm is adopted to carry out iterative solution on the energy storage optimal configuration model, and an optimal configuration scheme of the energy storage system in the active power distribution network at the current moment is determined; the improved butterfly optimization algorithm is a butterfly algorithm adopting a dynamic switching probability and a dynamic variance Gaussian mutation strategy; the optimal configuration scheme is a configuration scheme corresponding to the maximum number of iterations; the configuration scheme comprises the access position, the capacity and the charging and discharging power of the energy storage system, optimal configuration of the energy storage system in the active power distribution network can be achieved, and the optimal configuration scheme of the energy storage system in the power distribution network is determined.

The invention provides a ventilation system energy-saving optimization method and system based on a neural network, and the method comprises the steps: collecting the historical operation data of a fan, and carrying out the mean interpolation and wavelet transform filtering preprocessing of the historical operation data; optimizing the human evolutionary optimization algorithm HEOA by adopting a Gaussian mutation strategy to obtain an improved human evolutionary optimization algorithm IHEOA; parameters of the SOFTS model are optimized through an IHEOA algorithm; an IHEOA-SOFTS ventilation system model is constructed, and the relation between the air volume opening degree and the indoor temperature and the relation between the air volume opening degree and the energy consumption are established; establishing a comprehensive objective function considering indoor temperature control precision and total power consumption, taking minimization of the comprehensive objective function as an objective, utilizing an IHEOA algorithm to continuously optimize and adjust the opening degree of each fan in the IHEOA-SOFTS ventilation system model, determining the optimal relation between the opening degree of the fan and the indoor temperature and the total power consumption, and calculating the indoor temperature control precision and the total power consumption of the fan according to the optimal relation between the opening degree of the fan and the indoor temperature and the total power consumption. Therefore, accurate control of fan parameters and energy-saving optimization of the system are realized.

An ultra-high-precision aerodynamic force servo system and an intelligent control parameter optimization method therefor. The aerodynamic servo system is mainly composed of a double-acting air-floatin

The invention provides an internet-of-vehicles computing unloading and resource allocation method based on an improved black-wing optimization algorithm, which comprises the following steps of: acquiring computing time delay and computing energy consumption of tasks under various computing scenes based on the various computing scenes; calculating the total calculation time delay and the total calculation energy consumption of the task based on the calculation time delay and the calculation energy consumption of the task under various calculation scenes: defining a system utility function based on the total calculation time delay and the total calculation energy consumption of the task, and constructing a calculation time delay and energy consumption model under the mobile edge calculation scene by taking the minimum system utility function as a target; and designing a black wing collaborative optimization algorithm based on an elite reverse learning strategy, a Gompertz model and a Gaussian mutation strategy, and solving a calculation time delay and energy consumption model in a mobile edge calculation scene by adopting the designed black wing collaborative optimization algorithm to obtain a final calculation unloading and resource optimization allocation scheme. The convergence speed of the algorithm can be increased, and the algorithm is prevented from being caught in a local optimal solution too early.

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

The invention relates to the technical field of artificial intelligence fault diagnosis, and discloses an observation and aiming system fault prediction method based on INGO-GBDT, and the method comprises the steps: carrying out the preprocessing of collected data through a VIKOR algorithm, carrying out the improvement of a northern eagle optimization algorithm NGO, introducing ICMIC chaotic mapping in an NGO population initialization stage, and introducing a Gaussian mutation strategy in an NGO prey recognition stage. A Levy flight strategy is introduced in the NGO chasing and escaping stage to optimize northern eagle position updating; an improved northern eagle optimization algorithm INGO is adopted to perform parameter optimization on a gradient boosting decision tree GBDT, an INGO-GBDT fault diagnosis model is constructed, and a training data set is trained; and inputting the test data set into the INGO-GBDT prediction model, and carrying out fault diagnosis on the observation and aiming system. According to the method, main parameters of the gradient boosting decision tree (GBDT) are optimized through the improved northern eagle optimization algorithm (INGO), the defect of blindness of parameter selection in the training process is overcome, the prediction precision of the classification prediction model is improved, and higher prediction precision and practicability are achieved.

The present application discloses the construction and application of an esophageal cancer survival period prediction model based on an improved salp swarm algorithm. The present application uses a partitioned iteration strategy to divide the iteration of the algorithm into two periods, namely the early iteration period and the late iteration period; in the early iteration period, a leader fusion mutation strategy is used to update the leader position to increase the exploration ability of the algorithm; in the late iteration period, an optimal individual leading movement is adopted to update the leader position to optimize the exploitation ability of the algorithm; then a dimension-by-dimension Gaussian mutation strategy is used to update all salp swarm individuals to improve the diversity of the population; before the end of the algorithm iteration, the optimal salp position is subjected to an optimal neighborhood perturbation and the fitness values are compared, and a greedy strategy is used to select the optimal fitness value to improve its ability to jump out of the local optimal solution. The IPSSA-BP model of the present application has a good fitting effect, high accuracy, high prediction precision, and good prediction stability.

This invention discloses a multi-threshold image segmentation system, method, and medium for supply chain product inspection. The multi-threshold image segmentation method for supply chain product inspection includes: acquiring supply chain product images; preprocessing the supply chain product images to obtain preprocessed images; generating explosion sparks based on the preprocessed images using an explosion operator; randomly selecting several fireworks and performing Gaussian mutation operations to generate mutated sparks; mapping explosion sparks and mutated sparks exceeding the boundary range back to the feasible range according to an exponential perturbation mapping rule, and generating a new fireworks population according to a random selection strategy; determining whether the new fireworks population meets the termination condition; if so, segmenting the preprocessed images using the optimal threshold to obtain segmented images; and detecting supply chain products based on the segmented images. This multi-threshold image segmentation method for supply chain product inspection solves the problem in existing technologies where the human eye cannot easily identify slightly damaged parts of products in the equipment supply chain.

An ultra-high-precision aerodynamic force servo system and an intelligent control parameter optimization method therefor. The aerodynamic servo system is mainly composed of a double-acting air-floating friction-free cylinder (10) and a pressure control system. The effect of friction on the output force control precision can be removed by using the air-floating friction-free cylinder (10); in the pressure control system, ultra-high-precision control over the pressure is realized by means of a fuzzy PI controller optimized on the basis of a new improved particle swarm algorithm; a Gaussian mutation policy and a fuzzy control theory are fused in the new improved particle swarm algorithm; and optimized fuzzy PI control parameters are obtained by using the new improved particle swarm algorithm, ultra-high-precision pressure control over a cavity of the air-floating friction-free cylinder (10) is executed on the basis of the parameters, and an ultra-high-precision force output of the system can be realized. The aerodynamic force servo system can be applied to occasions with very high requirements for force control precision, and expands the application range of the aerodynamic force servo system.

This invention proposes a method for computing offloading and resource allocation in the Internet of Vehicles (IoV) based on an improved Black Kite optimization algorithm. The method comprises the following steps: obtaining the computational latency and computational energy consumption of tasks in various computing scenarios based on multiple computing scenarios; calculating the total computational latency and total computational energy consumption of the tasks based on the computational latency and computational energy consumption of the tasks in various computing scenarios; defining a system utility function based on the total computational latency and total computational energy consumption of the tasks; constructing a computational latency and energy consumption model for mobile edge computing scenarios with the goal of minimizing the system utility function; designing a Black Kite optimization algorithm based on an elite reverse learning strategy, a Gompertz model, and a Gaussian mutation strategy; and using the designed Black Kite optimization algorithm to solve the computational latency and energy consumption model for mobile edge computing scenarios to obtain a final computation offloading and resource optimization allocation solution. This invention can accelerate the convergence of the algorithm and prevent the algorithm from prematurely falling into a local optimal solution.

The application relates to a WSN cluster head election method and system based on an elite multi-objective gold panning algorithm, and the method comprises the following steps: calculating an optimal cluster head number based on a wireless sensor network; generating an initial population by using a Sobol sequence and performing population iteration; updating individuals of the population by using an optimized gold panning optimization algorithm to obtain a first population; performing Gaussian mutation on the first population to obtain a second population; combining the three populations to form an enhanced elite candidate population; performing target function calculation, quickly non-dominant sorting the individuals by using an NSGA-III algorithm to obtain the level to which the individuals belong; judging whether the current iteration number reaches a target iteration number; if not, selecting the next generation population based on the enhanced elite candidate population and by using the NSGA-III algorithm to perform population iteration; and if yes, outputting a Pareto front to determine a cluster head node in a wireless sensor network (WSN). The application introduces the enhanced elite candidate population to expand population diversity and avoid loss of historical elite solutions.

The invention discloses a ship pipeline path optimization method based on an improved marine predator algorithm, and the method comprises the steps: determining an objective function and the number of iterations, initializing a population position, generating a predator matrix, presetting the number of iterations T, and judging an optimization stage through the current number of iterations t. If in the first 1 / 3 stage, executing global search prey, if in the middle 1 / 3 stage, executing accelerated hunting, and if in the rear 1 / 3 stage, executing accurate preying. If the current number of iterations is in the first half of iteration (tlt; t / 2), applying the FADS effect to influence the position, and performing Gaussian perturbation if the position is in the rear half section. And continuing iteration and judging whether the current iteration number reaches a preset parameter or not, if the loop is ended, outputting the optimal solution, and if the current iteration number does not reach the preset parameter, re-presetting the iteration number T. According to the method, a golden sine guidance mechanism, a refraction reverse learning strategy and a Gaussian mutation strategy are used for performing stage improvement on the algorithm, and a high-quality ship management scheme can be provided for engineers in a relatively short time under various constraints and target limitations.

This invention provides a collaborative path planning method for underwater target search and tracking based on AUVs and multiple USVs. It employs a distributed, cross-domain, heterogeneous collaborative path planning architecture. By setting different fitness functions in the search and tracking phases, the USV cluster and AUVs can dynamically adjust their path planning strategies according to mission requirements, achieving close collaboration between cross-domain platforms within the same time period and overcoming the information transmission lag problem in traditional phased collaborative modes. In the tracking phase, this invention introduces a dynamic periodic underwater target position prediction method to predict the target's future trajectory, enabling the AUV to switch from tracking mode to interception mode, improving the target acquisition success rate. The algorithm effectively balances global exploration capability and local development accuracy by introducing adaptive explosion radius, Levy flight strategy, and Gaussian mutation operation, resulting in stronger global optimization capability and faster convergence speed in the optimization of multi-USV and AUV collaborative path planning.

The invention discloses an intracranial hemorrhage CT image segmentation method and device, a medium and equipment, and relates to the technical field of image segmentation. In a segmentation threshold optimization process, a Bareones mechanism and an orthogonal learning mechanism are integrated into an African vulture optimization algorithm, a search space is expanded in a search population corresponding to a segmentation threshold through Gaussian mutation, and the ability of the threshold optimization algorithm to get rid of local optimum is enhanced; and an orthogonal learning mechanism promotes resource sharing among search population individuals corresponding to the segmentation threshold, and improves the search diversity of the segmentation threshold. And obtaining an optimal individual with the maximum Renyi entropy after the two-dimensional histogram is divided based on the threshold value through multi-round iterative optimization, and taking the optimal individual as an optimal segmentation threshold value of the intracranial hemorrhage CT image. According to the method, the population diversity and convergence speed in segmentation threshold optimization of the intracranial hemorrhage CT image are improved, and the image segmentation effect of the intracranial hemorrhage CT image is improved.

The invention provides an improved competitive particle swarm optimization (CSO) algorithm in order to solve the problems of load unevenness and task completion time optimization in cloud computing task scheduling. Aiming at the problems of slow convergence, easy falling into local optimum, neglect of load balance and the like when the traditional CSO is used for processing the problems, three innovations are carried out: 1, the global exploration capability of the algorithm is effectively enhanced by introducing Gaussian mutation operation to implement position disturbance on winner particles; 2, designing a dual-mode updating strategy, and guiding the population to escape from a local optimal region in an evolution stagnation stage through a differentiated particle updating mechanism; and 3, constructing a multi-objective fitness function, and integrating task completion time (Makespan) and a load balancing index (LB) to realize collaborative optimization of scheduling efficiency and resource utilization. By reconstructing an updating mechanism of the competitive particle swarm, the comprehensive performance of the algorithm in the aspects of scheduling performance and resource utilization rate is remarkably improved, and a more competitive solution thought is provided for the task scheduling problem in the cloud computing environment.

The invention discloses a motor bearing fault diagnosis method and system based on FMD and Transform, belongs to the technical field of motor bearing fault diagnosis, solves the problem of how to improve the fault diagnosis precision of a motor bearing, and obtains MBKA by optimizing BKA through Funch chaotic mapping, a golden sine optimization strategy and a Gaussian mutation strategy, and the algorithm has higher optimization capacity. Parameters of the FMD and the Transform are optimized through the MBKA, so that the uncertainty of manual parameter setting is avoided, and the preprocessing effect of the signal and the fault diagnosis performance of the model are improved. In combination with a motor bearing fault mechanism, the influence of a strong noise environment on a fault diagnosis result is reduced, and feature information in vibration data of different fault types of the motor bearing is fully extracted, so that fault diagnosis is accurately carried out on the motor bearing, and personnel and equipment damage is avoided.

The application discloses a deep optical neural network training method and system based on a hybrid mutation strategy genetic algorithm, and the method comprises the following steps: S1, sequentially stacking a linear operation layer based on MZIs, a nonlinear activation layer based on EOA and a Dropmask based on a mask to build an N-layer deep DONN; S2, preprocessing a data set with different characteristic categories to conform to the data input size of the DONN; S3, uniformly initializing the DONN population, combining the MSE and the Accuracy between the real value and the predicted value as the fitness evaluation function of the individual; S4, taking the exponential ranking selection ERS and the uniform crossover UC as the selection operator and the crossover operator in the training process, adopting a hybrid mutation strategy, and distributing three operators, namely, the single-point mutation SM, the uniform mutation UM and the Gaussian mutation GM, to different individuals for mutation according to a dynamic game probability; and S5, adopting a double-elite reservation strategy, reserving two individuals with the optimal MSE and Accuracy performance to the next generation, and through iterative evolution, until a termination condition is met, and a DONN individual with the globally optimal network parameter is obtained.

The invention relates to the technical field of optimal solution search, in particular to a genetic model-based artificial intelligence optimal solution search method, which comprises the following steps of: generating an initial population by utilizing a Latin hypercube sampling method according to a constraint condition, uniformly distributing individuals in a solution space, calculating individual fitness values, constructing a fitness distribution histogram, and performing optimal solution search. Genotype and phenotype diversity indexes are monitored in real time, when the diversity indexes reach a specific threshold value or meet early warning conditions, gradient descent local search is performed on the optimal individual, a dynamic step length adjustment strategy is adopted to optimize the search process, validity verification is performed on an optimization vector, and when local search is not triggered, the optimal individual is subjected to gradient descent local search. According to the method, the hierarchical selection, single-point crossover and Gaussian mutation operations are performed on the population, and the boundary of a variation individual is repaired, so that the problems of insufficient population diversity control and easy local optimum in a complex solution space in a traditional method are solved, and the search efficiency and the result quality are improved.

The application discloses an intracranial hemorrhage CT image segmentation method and device, medium and equipment, and relates to the technical field of image segmentation. In the threshold segmentation optimization process, the Barebones mechanism and the orthogonal learning mechanism are integrated into the African vulture optimization algorithm. The search space is expanded in the search population corresponding to the segmentation threshold through Gaussian mutation, and the ability of the threshold optimization algorithm to get rid of local optimization is enhanced. The orthogonal learning mechanism promotes the resource sharing between the search population individuals corresponding to the segmentation threshold, and improves the search diversity of the segmentation threshold. Through multiple rounds of iteration optimization, the optimal individual that makes the Rényi entropy of the two-dimensional histogram based on the threshold segmentation maximum is obtained as the optimal segmentation threshold of the intracranial hemorrhage CT image. The application improves the population diversity and convergence speed in the threshold segmentation optimization of the intracranial hemorrhage CT image, and improves the image segmentation effect of the intracranial hemorrhage CT image.

The invention relates to a method and a system for realizing photovoltaic array fault diagnosis based on an integrated learning model CatBoost, which can solve the problem of low fault diagnosis accuracy of a photovoltaic array in a complex terrain and a changeable environment. An existing diagnosis method is limited by environmental factors and insufficient data, key information in a current-voltage characteristic curve (I-V curve) cannot be fully mined, and therefore fault type recognition is limited. In order to solve the problems, an I-V curve correction algorithm is firstly provided and used for correcting the influence of environment variables (such as temperature and irradiance) on fault feature representation, and feature information with higher identification capacity is extracted. Then, a CatBoost model is adopted to realize high-precision real-time fault diagnosis under a photovoltaic array small sample condition, and a sparrow search algorithm (SSA) is utilized to optimize key hyper-parameters of the model so as to improve the diagnosis performance and generalization ability of the model. Furthermore, in order to enhance the optimization capacity of the sparrow search algorithm, an improved sparrow search algorithm (EKSSA) fusing an elite reverse learning strategy and a Cauchy Gaussian mutation strategy is introduced, and the performance of the CatBoost model is optimized, so that the CatBoost model is more excellent in fault detection and classification. Through the method, real-time monitoring, intelligent fault diagnosis and predictive maintenance of the photovoltaic array under complex working conditions can be realized, the reliability and the operation efficiency of the system are remarkably improved, and the method is suitable for practical application scenes of distributed energy systems such as photovoltaic power stations and the like.

The application discloses an electric energy substitution potential prediction method and system based on ISSA-ELM, relates to the technical field of electric energy substitution potential prediction, and comprises the following steps: analyzing influence factors of electric energy substitution potential and quantifying, inputting quantized values of the influence factors of electric energy substitution potential; configuring parameters of a sparrow search algorithm, performing global and local search optimization based on a chicken swarm optimization algorithm and a Cauchy-Gaussian mutation strategy; evaluating a fitness function to obtain an optimal adaptive individual, calculating optimal weights and thresholds of an extreme learning machine, establishing an optimal extreme learning machine prediction model, and outputting an electric energy substitution potential prediction result. According to the method, the parameters of the sparrow search algorithm are configured, the chicken swarm optimization algorithm and the Cauchy-Gaussian mutation strategy are combined, global and local search optimization of prediction model parameters is realized, the search efficiency of the algorithm is improved, the ability of the algorithm to jump out of a local optimal solution is enhanced, and therefore the generalization ability and the prediction precision of the prediction model are improved.

The invention discloses an unmanned aerial vehicle path-finding method and system based on a multi-strategy improved artificial travel mouse algorithm, and the method comprises the steps: collecting the current three-dimensional coordinates of an unmanned aerial vehicle, and setting a starting point and an ending point of the flight of the unmanned aerial vehicle; constructing a target function by taking the minimum height, the corner cost and the shortest flight distance as targets; substituting the current three-dimensional coordinate of the unmanned aerial vehicle into the target function to evaluate the fitness, introducing improved Chebyshev mapping, Gaussian distribution and a Cauchy Gaussian mutation strategy to improve an artificial travel mouse algorithm, and obtaining the optimal next three-dimensional coordinate of the unmanned aerial vehicle by using the improved artificial travel mouse algorithm; and forming an optimal operation path of the unmanned aerial vehicle according to the optimal three-dimensional coordinates, and adjusting the direction of the unmanned aerial vehicle based on the optimal path. According to the method, the flight path of the unmanned aerial vehicle is optimized by improving the artificial travel mouse algorithm, so that the search efficiency of the unmanned aerial vehicle is improved, the navigation cost is saved, and the search requirements of complex environments and limited cost are met.

The embodiment of the invention provides a thermal power station valve inner leakage intelligent identification method based on an improved SVM. The method comprises the steps that a training sample set is constructed by adopting data corresponding to parameters during operation of a drain valve of a thermal power station; optimizing the Kepler optimization algorithm KOA based on the Sobol sequence and the adaptive Gaussian mutation to obtain an improved Kepler optimization algorithm IKOA; the SVM model is trained through a training sample set by adopting multi-step sliding, key parameters of the SVM model are self-optimized by adopting an improved Kepler optimization algorithm IKOA, and the SVM model constructed by adopting optimal values of the key parameters is used as an intelligent identification IKOA-SVM model of the valve inner leakage amount. A Sobol sequence is adopted to initialize a KOA population to achieve optimization of local search and global search, an optimized IKOA-SVM model is obtained, and intelligent identification of the inner leakage of the thermal power station valve is achieved.

This invention discloses a precise motion controller for a magnetically controlled capsule robot based on IDMO-PID, comprising a magnetically controlled capsule robot and an external magnetic source. The magnetically controlled capsule robot is placed in the stomach of a human, and the external magnetic source is located on the outside of the human body. This invention optimizes the motion control system of the capsule robot by rationally analyzing the motion mode and spatial magnetic field of the capsule robot. Based on the linearly decreasing inertia weight factor and the Cauchy-Gaussian mutation strategy, the population position update stage and the optimal solution output stage of the dwarf meerkat optimization algorithm are optimized, which effectively improves and reasonably balances the global search capability and local search capability of the basic dwarf meerkat optimization algorithm at different stages, thereby improving the convergence progress and convergence speed of the algorithm. The improved dwarf meerkat optimization algorithm is used to optimize the motion control system parameters of the capsule robot, realizing the precise motion of the capsule robot under multi-source spatial magnetic fields.

The invention discloses a power distribution network fault recovery method based on a distributed energy grid-connected point connectivity constraint. Firstly, a pre-reconstruction scheme set is generated as a primary particle swarm; calculating system power flow and node voltage of each pre-reconstruction scheme under double time sections at the initial stage and the later stage of recovery; then, taking the maximum weighted load recovery amount and the minimum switching action times as target functions of power distribution network fault recovery control, introducing a penalty factor, constructing a fitness function, and calculating the fitness of each particle; and finally, when the number of iterations reaches a set number of times, performing dominant Gaussian mutation operation on the particle swarm, namely randomly overturning partial switch states in a load transfer loop, and not disconnecting adjacent switches of the distributed new energy grid-connected nodes which are in a connected state any more. And updating the speed and the position of each particle, and taking the pre-reconstruction scheme corresponding to the globally optimal particle as the optimal scheme. The method improves the fault recovery speed and the power supply reliability, and avoids the long-time power failure of the load side.