37 results about "Gray wolf" patented technology

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 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 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.

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 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.

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.

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.

The present invention relates to the field of image processing technology, and provides an object detection method and system that combines the gray wolf strategy and the whale algorithm, including: obtaining an image to be detected; based on the image to be detected, using an optimal object detection model to obtain the objects in the image to be detected; wherein, the optimal object detection model is obtained by training and iterating the parameters of the object detection model using an algorithm that combines the gray wolf strategy and whale predation; the algorithm that combines the gray wolf strategy and whale predation controls the execution probabilities of the whale spiral predation strategy, the gray wolf predation strategy, and the random walk strategy based on a scaling factor and population concentration, and updates the position of each individual; the scaling factor is related to the ratio of the optimal fitness in two adjacent cycles; the population concentration is related to the sum of the fitnesses of all individuals. The detection ability of the object detection model, especially the detection ability of small objects, is 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 present invention relates to the technical field of machine vision and image processing, and specifically provides an anchor bolt hole positioning method and system based on a neural network and a grey wolf optimization algorithm, including: obtaining a two-dimensional image of the tunnel inner wall; evenly dividing the two-dimensional image of the tunnel inner wall to obtain a number of square regions, and establishing a coordinate system; determining whether the preset anchor bolt holes of the tunnel catenary avoid adverse conditions such as cracks and water seepage points. If so, determining the current anchor bolt hole position; if not, randomly generating artificial wolves in the square region with a coordinate system by using the grey wolf optimization algorithm; establishing a fitness function, sorting the wolf pack, and obtaining the lead wolf, scout wolf, and brute wolf; constructing a feedforward neural network, and combining the intelligent behaviors of the lead wolf, scout wolf, and brute wolf to obtain the position of the lead wolf and the fitness function value of the lead wolf; wherein, the position of the lead wolf is the optimal position of the current anchor bolt hole, and the positioning of the anchor bolt holes of the tunnel catenary is completed. The present invention provides a scientific solution for the positioning of the anchor bolt holes of the tunnel catenary.

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.

The present invention belongs to the technical field of multi-UAV forest fire inspection task allocation, and discloses a multi-UAV forest fire inspection task allocation method, device, and medium. Based on the geographical data and fire information of the forest fire area, the present invention constructs a multi-UAV collaborative inspection task allocation model, with the dual optimization objectives of minimizing the total inspection distance and optimizing the UAV load balance, and additionally introduces a map level cost and a UAV detour cost to improve the rationality of task allocation; selects the iterative strategy of the gray wolf population between the individual internal discrete operation and the group cooperation crossover operation according to the dynamic switching factor; in the iterative optimization, updates the elite archive population based on non-dominated sorting and crowding distance, and enhances the convergence and distribution of the solution through the "removal-insertion" strategy. The present invention solves the defect that the traditional continuous multi-objective gray wolf algorithm cannot be applied to discrete problems through unique discretization operations, and can quickly generate inspection paths that take into account timeliness and energy consumption balance.

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.