48 results about "Gray wolf" patented technology

The invention discloses a transformer substation inspection unmanned aerial vehicle path planning method based on a fusion A *-grey wolf algorithm, and aims to solve the problem of unmanned aerial vehicle path planning in a transformer substation inspection scene, and the method fully considers coupling of task point traversal and obstacle avoidance, thereby ensuring that an inspection path allocated for unmanned aerial vehicle inspection accords with a real transformer substation scene. A transformer substation virtual environment model is established through a grid map of an A * algorithm, and path implementation of the algorithm is simplified; a population is initialized by improving Tent chaotic mapping, a grey wolf algorithm is improved by introducing an adaptive position updating strategy and an elite grey wolf optimization strategy, and an initial traversal path is generated; and refining through an A * algorithm dynamically weighted by an evaluation function to obtain a final path. Simulation results prove that the feasibility and effectiveness of the improved fusion algorithm in obstacle avoidance and path planning in a substation simulation scene, and the improved algorithm is more suitable for path planning in a complex environment with multiple task targets and multiple obstacles.

The invention discloses a grey wolf optimizer modeling method based on multi-strategy combination. The grey wolf optimizer modeling method comprises the following steps: 1) building a target parameter prediction model; 2) optimizing the target parameter prediction model by using a grey wolf optimizer based on multi-strategy combination; and 3) realizing prediction of target parameters by using the optimized target parameter prediction model. The invention provides a multi-strategy optimized grey wolf optimizer MGWO, and the algorithm adopts multi-strategy optimization of chaotic mapping, double nonlinear convergence factors, adaptive weight coefficients, elitist retention and introduction of AO and WOA search prey mechanisms at the same time, and achieves effective balance of the algorithm in global exploration and local mining and utilization.

The invention provides a grey wolf optimization feature selection method, device and equipment based on an adaptive mechanism, and relates to the technical field of data processing. The method comprises the following steps: acquiring a data set to be subjected to feature selection, and initializing a grey wolf optimization algorithm; the fitness value of each grey wolf is calculated with the purpose of obtaining the current optimal solution, and three current optimal wolves are obtained; then taking the current optimal three wolves as search guidance, combining a nonlinear parameter control strategy, adopting an improved grey wolf step length updating calculation mode, adaptively adjusting variation intensity in the search process, updating and calculating related parameters, positions and fitness values of each grey wolf, and continuously converging the distance between the prey and the grey wolf in the search process; and calculating the score of each grey wolf, and selecting the solution candidate with the highest score as the optimal solution in the current population until the maximum number of iterations is reached, thereby obtaining a global optimal solution. The method is used for processing feature selection in high-dimensional and complex classification, and is high in convergence speed and high in accuracy.

The air freight loading efficiency and the flight safety are crucial to the efficient operation of a transportation system. Therefore, aiming at the problem of multi-cargo loading optimization under the complicated constraint condition of the cargo hold, a multi-objective optimization model containing business load limitation, gravity center shift, volume utilization rate and rotational inertia is constructed, and a solution method based on a grey wolf genetic hybrid algorithm is provided. A high-quality initial population is generated by adopting a two-stage initialization strategy, and evaluation is performed through a fitness function. And dynamically adjusting a step length coefficient and a mutation probability to realize self-adaptive adjustment of the search capability. In the selection stage, excellent individuals are reserved by using a tournament strategy, and meanwhile, the population quality is improved through an alpha wolf direct inheritance mechanism. In the breeding stage, the feasibility and diversity of solutions are enhanced through single-point crossing and mutation operation based on the gravity center distance. And after iterative updating, outputting the scheme with the highest evaluation value as the optimal loading scheme. According to the algorithm, the loading balance and the calculation efficiency are improved, and the high loading rate of the space in the cabin is ensured.

The invention discloses a radar interference resource allocation method based on a grey wolf algorithm. The method mainly solves the problems that in the prior art, interference resource allocation efficiency is low, combination explosion occurs, and engineering implementation is not facilitated. The scheme comprises the following steps: 1) constructing an interference task scene, and constraining a solution space through a coding strategy optimization method; 2) constructing an interference resource allocation objective function, and initializing an allocation scheme according to a good point set theory; 3) on the basis of the grey wolf algorithm, a nonlinear convergence factor, a dynamic weight and a reverse learning method are introduced to complete updating of a grey wolf position, namely updating of a distribution scheme is completed; and 4) repeating the iterative updating process in the step 3) until the algorithm converges, and taking the wolf pack position corresponding to the currently obtained interference benefit as a final interference resource allocation result. The method can effectively improve the optimization speed in the resource allocation process, avoids falling into a local optimal solution, and can be used for improving the resource allocation task efficiency in a radar cooperative interference scene.

The invention discloses a buried pipeline external corrosion rate prediction method based on an IGWO-BP neural network, and relates to the buried pipeline corrosion rate prediction field, and the method comprises the steps: obtaining experiment data, and carrying out the normalization processing to obtain sample data; constructing a BP neural network model, and determining the number of nodes of a network hidden layer according to the topological structure of the BP neural network; initializing parameters a, A and C by adopting an improved grey wolf algorithm in combination with a BP neural network topological structure, and randomly generating wolf pack heavy grey wolf position information; calculating fitness values based on the fitness function, sorting the fitness values in a descending order, dividing grey wolves into alpha wolves, beta wolves, delta wolves and omega wolves, and updating the positions and parameters of the grey wolves; iterating the improved grey wolf algorithm to obtain the position of a head wolf alpha wolf and a corresponding minimum error of a target result; and bringing the initial weight and the threshold value corresponding to the position of the result head wolf alpha wolf back to the neural network for judgment to obtain an output prediction result. The prediction precision of the model is improved.

The invention provides an energy collaborative operation method and system based on an adaptive variation grey wolf pack algorithm, and the method comprises the steps: building a collaborative operation model based on a time-of-use electricity price and a gas price, solving the cooperative operation model by using an adaptive variation grey wolf pack algorithm every other preset time period to obtain an operation strategy with the optimal economical efficiency in the time period; the adaptive variation grey wolf pack algorithm comprises the following steps: initializing an algorithm; individual evaluation and grade layering; gray wolf individual positions are updated; performing adaptive variation; and judging an algorithm termination condition. According to the technical scheme of the invention, the method achieves the cooperative operation of cold, heat and electricity multi-energy flow under the scene of time-of-use electricity price, introduces a self-adaptive variation mechanism into the gray wolf pack algorithm, obtains an optimal cooperative operation strategy, and improves the economical efficiency of the energy consumption of a user.

The invention provides a one-roadway four-purpose coal and gas co-mining method for a bottom drainage roadway, and belongs to the technical field of coal mines, the method comprises the following steps: firstly, tunneling the bottom drainage roadway at a position 10-12 meters away from a coal seam floor, determining an optimal drilling position by adopting a grey wolf hunting optimization algorithm, and arranging high-position directional drilling groups every 10-15 meters; an intelligent gas extraction system is installed, and a gas pre-extraction function is started 60-90 days before the working face is propelled; during stoping, a double-air-return system is constructed by utilizing the bottom pumping roadway; water drainage and water supply pipelines are synchronously laid to realize water recycling; transportation equipment, a disaster avoidance chamber and a communication system are arranged; a whole-process monitoring system is established, and various sensors are installed to realize real-time monitoring; and a special emergency plan is formulated, safe and efficient operation of the system is ensured, and the technical problems of gas concentration increase and accumulation risk in the multifunctional utilization process of the bottom pumping roadway are solved.

The invention discloses a rescue path planning method for a search and rescue robot, and the method comprises the steps: firstly, carrying out the nonlinear self-adaptive adjustment of a path optimization strategy through introducing a dual-perception system based on population information entropy and optimization progress; secondly, carrying out orthogonal cooperation on social level guidance of a grey wolf algorithm and a speed disturbance mechanism of a simplified meteorological optimization model, and realizing adaptive fusion of the two strategies through evolvable individual weights; and then, performing iterative optimization based on combination of a grey wolf optimization mechanism and a simplified meteorological optimization model on the candidate path scheme to generate an optimal rescue path. According to the method, the path search strategy can be dynamically adjusted through the optimization state sensed in real time, intelligent balance is achieved between global exploration and local development, the convergence speed, the path quality and the robustness of path planning of the search and rescue robot in a complex environment are remarkably improved, and the reliability and the safety of path planning are improved.

The invention relates to the technical field of data processing, in particular to an unmanned aerial vehicle inspection optimization method for a natural reserve, and solves the technical problem of unreasonable inspection route planning when an unmanned aerial vehicle inspects the natural reserve in the prior art. The method comprises the following steps: dividing a natural reserve into a plurality of grids, calculating static obstacle density of barrier-free grids in the plurality of grids based on point cloud data of static obstacles of the natural reserve, and calculating dynamic obstacle density of the barrier-free grids based on image data of dynamic obstacles of the natural reserve; determining a comprehensive obstacle factor of each barrier-free grid based on the static obstacle density and the dynamic obstacle density; and selecting an initial point of a grey wolf algorithm based on the static obstacle density, and performing iterative calculation of the grey wolf algorithm by taking the length of the inspection path and the minimum weighted value of the comprehensive obstacle factor of the inspection path as an optimization target to determine the inspection path.

The invention relates to the technical field of unmanned aerial vehicle logistics and path planning, in particular to an improved grey wolf optimization method for unmanned aerial vehicle logistics distribution planning. The method comprises the following steps: firstly, constructing a three-dimensional simulation environment containing an obstacle and setting a path constraint condition; a comprehensive objective function is established, and four sub-objectives of path length, collision penalty, smooth penalty and height violation penalty are integrated through weighted summation; in the algorithm level, Kent chaotic mapping is adopted to generate an initial population to improve diversity, control parameters are changed into a nonlinear adaptive updating strategy to balance global exploration and local development, and a particle swarm optimization algorithm idea is fused to introduce an adaptive inertia coefficient and a dynamic weight coefficient to improve a grey wolf position updating rule; and finally, performing iterative optimization through an improved hybrid algorithm, and outputting an optimal path. The method can effectively improve the quality and efficiency of path planning, and enables the generated path to be economical, safe and smooth.

The application discloses a kind of fusion multi-objective grey wolf optimization aquatic product freshness multi-task detection method, including data foundation construction, enhancement, CNN-LSTM-SE feature extraction, multi-task model formation, super parameter optimization, model training and evaluation deployment etc. steps.This method expands sample scale by data enhancement, extracts features using CNN and LSTM in parallel combined with SE attention mechanism, constructs multi-task learning framework to realize multi-index synchronous prediction, and optimizes super parameters in stages using MOGWO algorithm.The beneficial effects are to improve small sample robustness, feature extraction accuracy, multi-task detection efficiency and model generalization ability, and modular design facilitates popularization and application.

The invention provides a welding robot welding path planning method, device and equipment. According to the welding robot welding path planning method, an improved grey wolf algorithm is adopted, and grey wolf individual change probability parameters are set, and an average fitness value of a grey wolf population and the number of grey wolves lower than the average fitness value are introduced; the higher exchange probability of the gray wolf individual exchange sequence is beneficial for the algorithm to jump out of a local optimal region and widely search a global solution space; in the last stage of iteration, certain global search capability is still kept, local optimum can be further verified and optimized, the algorithm is prevented from being converged to a local optimum solution too early, it is ensured that the finally obtained welding path is a globally optimal solution or a solution close to the globally optimal solution, and the reliability and stability of welding path planning are improved.

An attitude maneuvering path planning method based on an improved gray wolf algorithm is proposed. The improved algorithm calculates the fitness value of each gray wolf. As the wolf's position is updated, individuals within the wolf pack continuously mutate, leading to constant population renewal and effectively solving the problem of entering local optima. Simultaneously, the high efficiency of the gray wolf algorithm allows for controlled implementation time. By comparing the fitness values ​​of individual gray wolves and applying a penalty function to wolves with poor fitness values, the global search capability is improved. The optimal parameters output by the improved gray wolf algorithm are used to simulate satellite paths and determine the changes in satellite control attitude data.

The invention provides a black box confrontation attack method based on a grey wolf optimization algorithm, belongs to the technical field of artificial intelligence security, and aims at solving the problems that an existing black box confrontation attack method is insufficient in mobility and low in search efficiency. The black box confrontation attack method comprises the steps that wolf pack initialization is carried out according to an input image, and an initial wolf pack is generated; a multi-level grey wolf optimization architecture is adopted, a wolf pack responsible for global guidance, local optimization and auxiliary exploration is constructed in combination with an initial wolf pack, all the wolf packs cooperatively run a grey wolf optimization algorithm, the positions of the wolf packs are updated through a dynamic hunting strategy, information exchange between the wolf packs is periodically executed, and group updating of the wolf packs is completed; after group updating of the wolf pack is completed, self-adaptive disturbance optimization of the wolf pack is carried out; and selecting a global optimal solution from the wolf pack responsible for global guidance after adaptive disturbance optimization, generating a final adversarial sample, and evaluating the attack success rate and average pixel disturbance of the adversarial sample on the target model according to the final adversarial sample.

The invention discloses a mountain torrent disaster risk assessment model construction method and system, and an early warning method, device and system, and the model construction method comprises the following steps: building a corresponding fuzzy judgment matrix based on an assessment index, and building a corresponding objective function based on the fuzzy judgment matrix; taking the objective function as a fitness function of a grey wolf optimization algorithm, using Poisson disk sampling to initialize a wolf pack position to obtain the wolf pack position, performing iterative updating on the wolf pack position based on the fitness function, and determining each evaluation weight based on a final iterative updating result; and based on a fuzzy comprehensive evaluation method, constructing a corresponding risk evaluation model by using the obtained evaluation weight, and determining a risk level corresponding to the mountain torrent disaster by the risk evaluation model based on the corresponding risk monitoring data. According to the method, the risk assessment model for mountain torrent disasters is constructed based on the fuzzy comprehensive analysis algorithm, various factors generating mountain torrents can be comprehensively analyzed, the early warning result is matched with the specific condition of a mountain area, and the false alarm rate is reduced.

The invention provides a three-soft coal seam mining area arrangement optimization method, medium and system, and belongs to the technical field of three-soft coal seam mining area arrangement, and the method comprises the steps: firstly collecting detailed geological, hydrological and mining condition data, and obtaining a plurality of feasible development-mining system schemes; and then based on a numerical simulation means, mining processes, mining area stability and water disaster risks under different schemes are evaluated. And solving the optimization model by adopting a multi-objective optimization algorithm to obtain a Pareto optimal solution set. And finally, discretizing the Pareto solution to form a multi-dimensional state space, optimizing the state space by using an improved grey wolf hunting algorithm, and finally obtaining an optimal mining area arrangement scheme. According to the method, multi-source data are fully utilized, analog calculation and optimization algorithms are fused, and effective technical support is provided for safe and efficient mining of the three-soft coal seam. The technical problem that the optimization effect is limited due to the fact that the coupling relation between a mining system and an excavation system is ignored in the prior art is solved.

The invention discloses a deterministic network time-triggered flow scheduling optimization method and device, equipment and a medium. The method comprises the following steps: generating a network directed graph according to a topological structure of a deterministic network; searching the shortest path of the time-triggered flow in the network directed graph by using a breadth-first search algorithm; initializing a grey wolf population; iteratively executing the following steps until a preset termination condition is met, and obtaining an updated final grey wolf population: calculating the fitness of grey wolf individuals, and determining the first three grey wolf individuals sorted according to the fitness values from large to small as the first wolf; for each grey wolf individual except the head wolf, updating the position of the grey wolf individual according to the distance between the grey wolf individual and the head wolf position so as to obtain a corresponding updated time-triggered flow scheduling mode; and determining the time-triggered flow scheduling mode corresponding to the grey wolf individual with the maximum fitness value in the updated final grey wolf population as the optimal scheduling mode. The scheduling of the time-triggered stream is effectively realized, and the requirement of real-time application is met.

The present application relates to the technical field of data processing, and particularly relates to a natural reserve unmanned aerial vehicle inspection optimization method, which solves the technical problem of unreasonable inspection route planning of an unmanned aerial vehicle in inspecting a natural reserve in the prior art. The method comprises the following steps: dividing the natural reserve into a plurality of grids, calculating the static obstacle density of the non-obstacle grids in the plurality of grids based on the point cloud data of the static obstacles of the natural reserve, and calculating the dynamic obstacle density of the non-obstacle grids based on the image data of the dynamic obstacles of the natural reserve; determining the comprehensive obstacle factor of each non-obstacle grid based on the static obstacle density and the dynamic obstacle density; selecting the initial point of the grey wolf algorithm based on the static obstacle density, and performing iterative calculation of the grey wolf algorithm with the minimum weighted value of the inspection path length and the comprehensive obstacle factor of the inspection path as the optimization target to determine the inspection path.

The invention belongs to the technical field of mechanical part design, and discloses a bridge crane main beam lightweight design method, which comprises the following steps of: acquiring cross section data of a crane main beam as a design variable, and taking weight reduction of the crane main beam as an optimization target; generating constraint conditions based on the vertical static stiffness, the normal stress, the shear stress, the fatigue strength and the overall stability to construct a crane girder section model; carrying out design variable coding on the crane main beam section model based on an improved grey wolf optimization algorithm, carrying out initialization setting, selecting a first wolf by constructing a fitness function, introducing adaptive update control parameters, updating the position of a grey wolf in a population, and when a termination condition is met, carrying out self-adaptive optimization on the grey wolf; and outputting the potential solution corresponding to the gray wolf position as an optimal solution. Based on the crane main beam section model and the improved grey wolf optimization algorithm, the intelligent lightweight design of the crane main beam is realized, the scheme design efficiency is improved, and the crane manufacturing cost is effectively reduced.

The invention provides a hydrological model optimization method suitable for an arid inland river basin, and the method comprises the steps: collecting hydrological meteorological data, determining the noise number K in an algorithm through employing a K-Means clustering algorithm, enabling the processed hydrological meteorological data to exchange K clusters, enabling each cluster to represent a specific hydrological feature, calculating the center point of each cluster, and carrying out the calculation of the center point of each cluster, randomly initializing a group of grey wolf optimization algorithms in each cluster; instantiating an LSTM model network structure in real time according to the position vector of the current population, setting a fitness function, and evaluating the fitness of grey wolf individuals; and training the hydrological model by using an optimal parameter combination obtained through optimization of the grey wolf algorithm, and evaluating the prediction capability of the model by using a cross validation method. According to the method, the historical hydrological data are clustered to identify different hydrological modes, then the grey wolf algorithm is used to carry out specific parameter optimization on each mode, and through adaptive parameter adjustment and data grouping analysis, accurate prediction of future hydrological conditions can be realized.

The application provides a structure adjustment method, medium and system of a monolithic integrated inverse-conducting GaN-HEMT power device, and belongs to the technical field of GaN-HEMT power devices, and comprises the following steps: a heat flow model and a heat dissipation path topology of the internal structure of a GaN-HEMT chip are established, and a key path analysis algorithm is used to calculate and identify the main bottleneck area of heat dissipation; an upper model with the optimal heat dissipation as the target and a lower model with the lowest drain current of the GaN-HEMT as the target are established, and the constraint conditions of the upper model and the lower model are determined; the micron-level metal heat sink parameters, the micro-channel cooling structure parameters and the vertical groove parameters formed by the source and drain regions of the GaN-HEMT are used as Alpha wolves, Beta wolves and Delta wolves of a grey wolf hunting algorithm, the grey wolf hunting algorithm is used to solve the game model, and the optimal solution obtained is used as an adjustment parameter.

The present application discloses a method, apparatus, device and medium for optimizing the scheduling of time-triggered flows in a deterministic network. The method includes: generating a network directed graph according to the topological structure of the deterministic network; using the breadth-first search algorithm to find the shortest path of the time-triggered flow in the network directed graph; initializing the gray wolf population; iteratively executing the following steps until a preset termination condition is satisfied to obtain the updated final gray wolf population: calculating the fitness of the gray wolf individuals, and determining the top three gray wolf individuals sorted in descending order of fitness values as the lead wolves; for each gray wolf individual other than the lead wolves, updating its own position according to the distance between itself and the position of the lead wolves to obtain the corresponding updated time-triggered flow scheduling method; determining the time-triggered flow scheduling method corresponding to the gray wolf individual with the largest fitness value in the updated final gray wolf population as the optimal scheduling method. It effectively realizes the scheduling of time-triggered flows and meets the requirements of real-time applications.

An unmanned ship cluster task allocation method based on an improved discrete grey wolf algorithm comprises the following steps that 1, information such as map information and unmanned ship cluster information is input in advance, modeling is conducted on all kinds of information, and a cost function for measuring the advantages and disadvantages of tasks is designed; 2, calling an improved discrete grey wolf algorithm to carry out population initialization coding on all task points; step 3, performing updating iteration on the initialized population, and firstly performing population aggregation on scattered task points by adopting an individual and population interaction strategy; step 4, carrying out first wolf guidance strategy updating on the clustered task points; and step 5, performing individual wolf wandering strategy updating on the population. Step 6, if the current iteration number t is greater than the maximum iteration number T, outputting a first wolf population sequence, otherwise, returning to the step 3; and step 7, decoding the first wolf population sequence. The algorithm has higher convergence speed and higher convergence quality.

The invention discloses a flood control dispatching method based on a fusion multi-strategy improved gray wolf algorithm, comprising the following steps: obtaining sample data of floods and reservoirs; establishing a flood control dispatching model; improving the gray wolf algorithm, using an improved Tent chaotic map to initialize the population, mixing Lévy walk and tangent walk and adding an elite retention strategy to determine the position of the α wolf, parabolic spiral approach to determine the position of the β wolf, determining the position of the γ wolf according to the positions of the α wolf and the β wolf, combining Cauchy mutation and dynamic weight coefficient to determine the position of the ω wolf, adding a cosine convergence factor to obtain a fusion multi-strategy improved gray wolf algorithm: using the fusion multi-strategy improved gray wolf algorithm to simulate flood control dispatching. The invention realizes the combination of local search and global search, avoids the algorithm from falling into premature, improves the algorithm optimization accuracy, and meets the requirements of effectively reducing the flood peak in flood control dispatching.

The invention discloses a canal system dynamic water distribution scheduling method considering short-term rainfall forecast, and the method comprises the steps: employing a grey wolf optimization GWO algorithm to optimize an SVR model according to meteorological and hydrological data; performing short-term forecasting on rainfall through a GWO optimized SVR model and a long short-term memory LSTM prediction model; the predicted short-term rainfall is used for constructing a canal system water distribution multi-target dynamic optimization model comprehensively considering irrigation time and irrigation water efficiency; a dynamic multi-target non-dominated sorting genetic algorithm is used for solving a multi-target canal system water distribution dynamic optimization model, and meanwhile, maximum utilization of rainfall resources and an optimal control strategy of canal system water distribution are realized through a prediction-decision-prediction rolling optimization mechanism. On the premise of ensuring agricultural safe and stable production, the potential of saving water for agricultural irrigation is released as much as possible.

1. Name of the designed product: toy (big gray wolf). 2. Use of the designed product: for toys. 3. Design points of the designed product: in shape. 4. Picture or photo that best shows the design points: perspective view.

The present invention discloses a method for planning unmanned aerial vehicle (UAV) swarm missions for regional coverage reconnaissance, and relates to the technical field of UAV swarm collaborative reconnaissance. The method comprises: determining a target area; performing gridding processing on the target area to obtain multiple grid areas; constructing a global fitness function, using an improved gray wolf optimization algorithm to simulate the hunting strategy of a wolf pack, and evaluating the quality of each gray wolf through the fitness function; constructing a global fitness function as a weighted sum of regional coverage and target point coverage, wherein the regional coverage is determined based on the number of grid areas covered by the sensor, and the target point coverage is calculated by counting the number of times each target point is covered by the sensor and normalizing the result. The method of the present invention can improve the regional coverage rate of UAV swarm regional coverage reconnaissance missions, and has reliability and scalability, thereby promoting the further development and application of UAV swarm regional reconnaissance coverage technology.

The present application relates to a method, device and equipment for aircraft in-and-out transfer scheduling based on the gray wolf algorithm. The method includes: obtaining the current in-and-out transfer task parameters; calling a pre-built scheduling model; initializing the gray wolf population according to the in-and-out transfer task parameters, selecting α wolf, β wolf or δ wolf in the gray wolf population by roulette to perform neighborhood optimization, obtaining neighborhood gray wolf individuals, and updating the positions of α wolf, β wolf and δ wolf according to the fitness function values ​​corresponding to the neighborhood gray wolf individuals, α wolf, β wolf and δ wolf; iteratively updating the positions of α wolf, β wolf and δ wolf, and when the pre-selected iteration stop condition is met, stopping the iteration and outputting the current in-and-out transfer scheduling plan; the in-and-out transfer scheduling plan is used for aircraft in-and-out transfer scheduling. The use of this method can improve the update efficiency of the optimal solution, enhance the algorithm's optimization ability, and thus efficiently execute fleet in-and-out transfer scheduling.

The invention relates to the technical field of sorting equipment, in particular to a right-angle robot sorting method and system based on a grey wolf search strategy, and the sorting method comprises the following steps: numbering materials on a conveyor belt, and coding a population according to a double-strand chromosome method; performing population initialization according to a tent chaotic mapping method; a target function is initialized, wherein the target function is the total stroke of all the materials sorted by the mechanical arm; a grey wolf position updating formula in the grey wolf algorithm is used for updating the grey wolf position, variation and recombination operation in the genetic algorithm is used for changing the position of a grey wolf individual, and local optimum is avoided; and carrying out local search operation on the updated head wolf, retaining the optimal individual of the target function, and updating the population. According to the invention, the fine management and optimization of the sorting task are realized, a set of efficient and intelligent path optimization method is provided, the sorting efficiency and accuracy are improved, and the time cost and energy consumption in the sorting process are reduced.