23 results about "Gray wolf" patented technology

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 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.

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 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.

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.

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 invention provides an intelligent identification method for the current situation of a deep and far sea based on a multi-temporal remote sensing image, and belongs to the technical field of identification of the current situation of the deep and far sea. According to the method, sub-pixel-level image registration is realized by adopting a layered registration strategy based on coastline stable ground features, geometric, texture and spectral features of deep and far sea structures are extracted to establish a feature vector library, and the cosine similarity of adjacent inter-phase feature vectors is calculated to carry out change detection; a coevolution network structure is combined with a sea area utilization state identification model of a multi-head grey wolf hunting model to carry out sea use type classification, illegal behaviors are judged, and an intelligent identification report including spatial distribution, change trend and compliance evaluation is generated. The technical problem that the sea area structure change detection precision is insufficient due to the fact that the multi-temporal remote sensing image is affected by cloud shielding in the deep sea environment is solved.

The invention provides a mixed grey wolf optimization path planning method for multiple plant protection unmanned aerial vehicles and a control terminal, and relates to the technical field of plant protection unmanned aerial vehicle control. The method comprises the following steps: synthesizing path cost, height cost, turning angle cost and collision avoidance cost, and constructing a target function for plant protection unmanned aerial vehicle path planning; constructing a grey wolf algorithm model, setting the maximum number of iterations, performing population initialization by using SPM chaotic mapping, calculating fitness values of a grey wolf pack, and performing incremental sorting on the fitness values to obtain the first three wolves, the first wolf and the second wolf; updating the nonlinear convergence factor according to the current iteration times, updating the position of the grey wolf according to the positions of the wolves, the nonlinear convergence factor and the reduced area function, and calculating the moderate value of the grey wolf after the position is updated; and according to the updated moderate value of the grey wolf, re-determining the first three wolves, the first three wolves and the second three wolves which are ranked, and outputting an optimal individual when iteration is finished. The pesticide spraying efficiency can be improved.

This application discloses a method for predicting harmful algal blooms based on Tent-GWO-GRU, comprising: acquiring and preprocessing historical water quality data of the target watershed; constructing a gated recurrent unit neural network (GRU) model based on the historical water quality data; optimizing the Grey Wolf Algorithm (GWO) based on the Tent chaotic mapping algorithm; and optimizing the GRU model parameters and making predictions. This application applies a time delay to the index data, fully considering the lag in algal bloom growth and evolution, thus improving the feasibility of the method. The introduction of the Tent chaotic mapping algorithm improves the method for generating the initial wolf pack in the GWO algorithm, resulting in a more uniform distribution of the initial wolf pack, significantly improving the algorithm's fitness value, and making it easier to find the global optimum. This application uses the Tent-GWO optimization algorithm to optimize the hyperparameters and network structure of the GRU model, which not only improves the model's stability but also achieves higher prediction accuracy.

The application discloses a cloud platform task scheduling method based on a dimension learning strategy and grey wolf optimization, which comprises the following steps: 1, initializing parameters of a DLH-GWO algorithm; 2, selecting an execution cost, a maximum completion time, a waiting time and a resource utilization to construct a multi-objective optimization model, and calculating the fitness of a grey wolf individual to obtain the positions of the top three wolves; 3, combining a search strategy of the dimension learning DLH and the positions of the top three wolves, and calculating two candidate update positions of the remaining wolves in the grey wolf population N; 4, comparing the fitness values of the two candidate update positions and the current position of the grey wolf individual, and selecting a better position to update the position of the grey wolf individual; 5, performing the same strategy position adjustment, selection and update operation on all the grey wolf individuals; and 6, judging whether the maximum iteration number is reached in the tth iteration, if not, continuing the next search, otherwise outputting an optimal solution, balancing the global and local search capabilities of the algorithm and improving the availability of the cloud platform.

This invention relates to the field of power plant planning technology and discloses a method for optimizing the distributed power generation capacity of a virtual power plant based on an improved gray wolf algorithm. This method, based on the operating strategy of the virtual power plant, simulates the configuration scheme represented by each individual gray wolf in the initialized gray wolf population to obtain operating data. Then, based on the operating data and the distributed power generation capacity optimization configuration model, the initial fitness value of each gray wolf is obtained. Based on the distance between gray wolf individuals and the radius of their niche, a corresponding niche is constructed with each gray wolf as the center. Through a sharing mechanism, based on the number and distribution of gray wolves within each niche, the initial fitness value of each gray wolf is adjusted to obtain a target fitness value. Based on the target fitness value, the gray wolf individuals in the population are iteratively updated until the convergence condition is met, resulting in the optimal configuration strategy.

The application discloses a parameter optimization method, a signal processing method and related devices of a mass flowmeter. The parameter optimization method comprises: initializing a group of grey wolf individuals, the position of each grey wolf individual comprising a proportional parameter and an integral parameter randomly selected within a preset range; constructing a target fitness function, the target fitness function being used to describe the deviation between the amplitude of a vibration sampling signal corresponding to the grey wolf individual and an expected amplitude; based on a grey wolf algorithm and the target fitness function, iteratively updating the position of the grey wolf individual in a direction in which the fitness value decreases; after the iteration is completed, selecting the position of the grey wolf individual with the minimum fitness value as an optimal parameter and outputting the optimal parameter. The parameter optimization method of the application optimizes the parameters of PI control based on the grey wolf algorithm, effectively optimizes the amplitude control of the sensor signal, reduces the deviation of the signal, improves the accuracy of the signal, and further improves the measurement accuracy of the mass flowmeter.

This invention belongs to the field of data recognition technology and provides a method and device for fish recognition based on an optimized convolutional neural network. It utilizes acquired historical fish image data to train an improved YOLOvx target recognition model. The model construction process includes first performing a global optimization using the Tornado algorithm to obtain the initial tornado position, then employing a strategy combining the Tornado algorithm and the Grey Wolf algorithm for local optimization. The global optimization process in the Tornado algorithm is improved by incorporating the fourth defense mechanism of the crested porcupine, resulting in a Tornado combined intelligent algorithm. This algorithm is then used to improve the upsampling process of the YOLOvx target recognition model, resulting in an improved YOLOvx target recognition model. The trained model is then used to recognize the target image to obtain the recognition result of mixed fish. This invention solves the problem of low recognition accuracy in existing technologies and can improve the accuracy of fish recognition.

The invention discloses a natural gas consumption prediction method based on an adaptive gray seasonal model, and the method comprises the steps: collecting natural gas consumption data to form an original seasonal sequence, and calculating a cumulative sequence; building a natural gas consumption prediction model according to the cumulative sequence; then, a grey wolf optimizer is used for searching adaptive parameters of the natural gas consumption prediction model; then, linear parameters of the natural gas consumption prediction model are obtained through least square estimation; generating a time response equation of the natural gas consumption prediction model according to the linear parameters; then, calculating a predicted value of the cumulative sequence; subsequently, a predicted value is obtained by a restoration operation. According to the invention, a natural gas consumption prediction task is executed based on a novel adaptive gray season model; according to the model, the nonlinear trend of data can be effectively captured through adjustment of structural parameters; by introducing seasonal correction, the seasonal change of the data can be accurately identified.

The invention belongs to the technical field of data recognition, and provides a convolutional neural network fish recognition method and device based on an optimized structure, and the method comprises the steps: training an improved YOLOvx target recognition model through employing obtained historical fish image data; the construction process of the model comprises the following steps: firstly, carrying out primary global optimization by utilizing a tornado algorithm to obtain an initial tornado position, and then carrying out local optimization by adopting a strategy of combining the tornado algorithm and a grey wolf algorithm; a global optimization process in a tornado algorithm is improved by combining a fourth defense mechanism of a crown porcupine to obtain a tornado combined intelligent algorithm, then the tornado combined intelligent algorithm is utilized to improve an up-sampling process of a YOLOvx target recognition model to obtain an improved YOLOvx target recognition model, and a target image to be processed is recognized by utilizing the trained model to obtain a target recognition result. And obtaining a recognition result of the mixed fishes. According to the invention, the problem of low identification accuracy in the prior art is solved, and the accuracy of fish identification can be improved.

This invention provides a method, medium, and system for optimizing the layout of mining areas in soft coal seams, belonging to the technical field of mining area layout for soft coal seams. The method includes: firstly, collecting detailed geological, hydrological, and mining condition data, and obtaining multiple feasible development-mining system schemes. Then, based on numerical simulation, evaluating the mining technology, mining area stability, and water hazard risk under different schemes. Next, using a multi-objective optimization algorithm to solve the optimization model, obtaining the Pareto optimal solution set. Finally, discretizing the Pareto solution to form a multi-dimensional state space, and using an improved gray wolf hunting algorithm to optimize the state space, ultimately obtaining the optimal mining area layout scheme. This method fully utilizes multi-source data, integrates simulation calculations and optimization algorithms, and provides effective technical support for the safe and efficient mining of soft coal seams. It solves the technical problem that existing methods neglect the coupling relationship between the mining and excavation systems, leading to limited optimization effects.