22 results about "Cuckoo search" patented technology

The invention relates to the technical field of power system operation and control, and discloses an improved model-free adaptive flexible low-frequency power transmission oscillation suppression method, which performs zero-phase lag filtering on noisy signals through a nonlinear tracking differentiator, effectively solves the problem of control divergence caused by signal noise, and improves the suppression efficiency. A tight-format dynamic linearization and linear expansion state observer technology is adopted, accurate capture of dynamic characteristics of a strong nonlinear system and real-time observation and compensation of internal and external full-dimensional disturbance are achieved without dependence of a physical model, and particularly, an adaptive estimation algorithm of an output variable quantity penalty factor is introduced. The problem that model parameter identification jumps severely under the broadband oscillation working condition is solved, the robustness of a control system is enhanced, and the self-adaptive optimality of a control strategy is guaranteed and interference of long-term online operation on steady-state performance is avoided by combining parameter optimization of a cuckoo search algorithm and an event triggering mechanism of time-frequency double criteria.

The invention relates to a time-frequency difference passive positioning method based on an adaptive step cuckoo search algorithm, and belongs to the technical field of passive positioning, and the method comprises the following steps: S1, constructing a time-frequency difference positioning mathematical model; s2, proposing a time-frequency difference positioning algorithm based on an adaptive step cuckoo search algorithm ASCS, and solving the time-frequency difference positioning mathematical model; according to the time-frequency difference positioning algorithm based on the adaptive step cuckoo search algorithm ASCS, an adaptive step strategy and the discovery probability of nonlinear logarithmic decline are introduced on the basis of the cuckoo search algorithm; according to the adaptive step length strategy, the position is adaptively updated by introducing a global historical optimal solution in the optimization process.

The invention discloses a power grid intelligent power flow control method based on an improved cuckoo search algorithm. The method comprises the following steps: firstly, constructing a mathematical model of an AC-DC hybrid system, analyzing the characteristics and interaction of each device in the system, and determining a composite function taking the minimization of power generation cost, active loss and generator voltage deviation as targets according to an operation target; and then, decomposing the complex objective function into a plurality of sub-problems through hierarchical processing, and realizing respective solving. In hierarchical calculation, an improved cuckoo algorithm is adopted for optimization in a solution space, and an optimal power flow distribution and control strategy is obtained. And finally, intelligent layered power flow control of the alternating-current and direct-current hybrid system is realized according to an optimization result. According to the invention, the system response speed and the regulation efficiency are obviously improved, and the effect in the aspect of optimized operation is outstanding; compared with the traditional Cuckoo search CS algorithm and NSGA algorithm, the cost consumption is respectively saved by 2.21% and 4.49%; the experimental model is low in weekly average cost and high in power transmission efficiency, the power transmission efficiency is effectively improved, and the loss cost is reduced.

The application provides a census clustering method for fairness and balance constraint optimization, comprising the following steps: step 1, obtaining a census sampling data set X; step 2, selecting an initial population clustering center by using a cuckoo search algorithm for optimization; step 3, selecting an optimal K value of population survey clustering by using a contour coefficient comparison method in a K value selection interval; step 4, performing a weighted Euclidean distance optimization iteration; step 5, introducing fairness and balance constraints; and step 6, iterating until a convergence condition is reached. The method eliminates the influence of data bias by optimizing the number of samples in the clustering cluster, and guarantees the fairness and representativeness of each group in the clustering result. The optimization method of the application can significantly improve the accuracy, fairness and stability of the clustering result when dealing with complex census data, especially in scenes involving sensitive attributes and sample imbalance, and effectively improve the reliability and fairness of census data analysis.

The invention discloses a Cuckoo search algorithm-based network node interaction method and system, and the method comprises the steps: setting wireless sensor network parameters, constructing a node energy consumption model containing a free space and a multipath attenuation mode, and adaptively selecting an energy consumption mode according to the relation between a transmission distance and a threshold value; selecting a single-hop or multi-hop transmission mode according to the distance between the cluster head nodes and the base station, determining the optimal number of cluster heads, and optimally selecting the cluster head nodes by using a cuckoo search algorithm; and optimizing a data transmission path, and selecting a relay node through a fitness function to realize interaction with a core network control function node. The system comprises a network initialization and parameter configuration module, an energy consumption modeling module, a cluster head selection and optimization module and a path optimization and core network interaction module. According to the invention, network energy consumption can be effectively reduced, network life is prolonged, data transmission efficiency and network adaptability are improved, and intelligent network management is realized.

The invention discloses a data-driven production line layout intelligent optimization and reconstruction method, and relates to the field of intelligent manufacturing engineering. According to the method, the cuckoo search algorithm, the group collaborative search mechanism and the firefly algorithm are fused, and intelligent optimization and dynamic reconstruction of the production line layout are realized. According to the invention, the stability of layout optimization and the performability of the scheme can be improved.

The present application relates to a kind of based on multi-index fusion evaluation power quality disturbance feature screening method, comprising: step 1: input power quality disturbance signal, extract multiple power quality disturbance features, construct power quality disturbance feature universe;Step 2: based on the fusion evaluation index of intersection degree index, redundancy index, separation index, using Cuckoo search method is searched and each dimension power quality disturbance feature subset selected is obtained;Step 3: based on cost factor, the selected power quality disturbance feature subset is evaluated, and the optimal power quality disturbance feature subset is obtained, and based on the optimal power quality disturbance feature subset, power quality disturbance signal is classified.The method is conducive to improving power quality disturbance classification efficiency and accuracy.

The application provides a kind of random scheduling method for ship maintenance task under milestone constraint, and relates to task scheduling technical field.The application obtains the data of the task to be scheduled in project, encodes the task to be scheduled, and generates a task list;process the task list based on heuristic rules and probability selection method, and generate an initial population;use the initial population as an initial bird nest, minimize the maximum completion time and minimize the delay cost as the goal, optimize the initial bird nest by improved discrete valley bird search algorithm, and obtain the optimal solution;the application takes minimizing the maximum completion time and minimizing the delay cost of task as the goal, studies the task scheduling method of time-varying task duration and resource demand under milestone task, and proposes several heuristic rules based on the characteristics of milestone task and random scheduling.It fills the gap in current research, and obtains a task scheduling scheme of high-end equipment manufacturing that is more in line with the actual production situation.

The invention provides a nickel-based alloy strip stamping springback prediction method, and relates to the technical field of nickel-based alloy stamping manufacturing, and the method comprises the steps: obtaining initial springback values corresponding to a plurality of groups of stamping process parameter combinations in the stamping process of a nickel-based alloy strip, and calculating the stamping springback value of the nickel-based alloy strip based on all the stamping process parameter combinations and the corresponding initial springback values; constructing a training data set and a test data set; the training data set serves as input data, the initial springback value corresponding to the training data set serves as output data, and a springback prediction model driven by physical information is constructed; using an adaptive cuckoo search algorithm to search a predicted springback value as an optimal springback value, wherein the difference value between the predicted springback value and the expected springback value is minimum in the springback prediction model driven by physical information, and using a stamping process parameter combination corresponding to the optimal springback value as an optimal process parameter combination; optimization can be carried out in a complex parameter space, the optimization efficiency and stability are effectively improved, and local optimization is avoided.

The application discloses a slope auxiliary driving control method, device and equipment of a heavy haul train and a storage medium, relates to the technical field of intelligent transportation and vehicle engineering, and comprises the following steps: analyzing train attributes, a running line and running environment information of the heavy haul train to obtain line slope, slope length information, speed limit information and slope distribution position data; according to the data, a multi-objective optimization model containing a fail-safe function, a smoothness function and an energy consumption function is constructed; according to the line slope, the multi-objective optimization model is solved by using a hybrid multi-objective optimization algorithm based on a non-dominated sorting genetic algorithm, a multi-objective cuckoo search mechanism and a Gaussian disturbance strategy to obtain train control parameters; and the heavy haul train is subjected to auxiliary driving control according to the train control parameters and the speed limit information. The application realizes safe, smooth and energy-saving multi-objective optimization intelligent auxiliary driving control for the long and large slope running scene of the heavy haul train.

This invention discloses a muscle strength estimation method based on the GRA-ICS-SVR model and MMG signals. Muscle strength is divided into eight levels, and three-channel MMG signals and corresponding muscle strength signals are collected for each level. Stable muscle strength signal segments and their corresponding MMG signal segments are selected. MMG signal features are extracted from the time domain, frequency domain, time-frequency domain, and nonlinear dynamic characteristics. The GRA method is used to filter effective features from the MMG signal features to construct sample data. The SVR model is used for muscle strength estimation, and an improved Cuckoo Search algorithm (ICS algorithm) is employed to optimize the key parameters of the SVR model, finding the optimal solution for these parameters and establishing a muscle strength estimation model based on ICS-SVR. This invention achieves higher accuracy in muscle strength estimation.

The application discloses a kind of tourism route planning method and system based on artificial intelligence, comprising the following steps: step one: multiple modal input data are collected and multiple modal data set is constructed;Step two: multiple modal data set is input to Perceiver IO model and is encoded;Step three: original city environment atlas is constructed;Step four: based on intent-environment two-way remapping network, generate individualized city environment atlas;Step five: construct tourism path set;Step six: based on improved cuckoo search algorithm, the tourism path set is optimized, and the heterogenous reconfiguration egg exchange mechanism is introduced to realize leap evolution update;Step seven: when meeting preset evolution termination condition, end evolution update, generate optimized tourism path set, and select the highest fitness as the current recommended path.The application fuses multiple modal modeling and improved cuckoo search algorithm, and realizes the efficient planning of individualized tourism path.

The invention relates to the technical field of power system optimization, in particular to an IMOCS-based capacity configuration optimization method for a green storage optical storage system, and the method comprises the steps: obtaining target data of a target storage park, and constructing the green storage optical storage system; according to the green storage optical storage system, an improved multi-target cuckoo search algorithm IMOCS is adopted to optimize the capacity configuration of the green storage optical storage system, and an optimal capacity configuration scheme of the green storage optical storage system is obtained, the improved multi-target cuckoo search algorithm IMOCS is obtained by combining a traditional multi-target cuckoo optimization algorithm with an improved Tent chaotic mapping strategy and Levy flight based on an adaptive scale factor. According to the method, the problems of low convergence speed, easy local optimum, insufficient multi-target balance capability and the like of the existing traditional optimization algorithm in the multi-target optimization of the green storage heat storage system can be solved.

The invention belongs to the technical field of computer vision, and provides a self-learning typical obstacle autonomous recognition method in an extreme scene, which comprises the following steps: step 1, collecting obstacle data in an extreme environment; 2, manually marking the position of an obstacle, and establishing a corresponding training set and a target data set; step 3, selecting cuckoo search by an intelligent algorithm, setting the maximum cycle index and the search interval of the cuckoo search, and setting an error initial value error1; and 4, generating parameters of the image recognition module according to a cuckoo search rule, and adding 1 to the number of iterations. The invention provides a model training method for extreme environments and small samples, and typical obstacles can be identified and positioned under the condition of fewer training samples.

According to the intelligent filter design method and system fusing the physical driving model and the filter, the high-performance filter is rapidly designed through an end-to-end automatic optimization process. Firstly, initialization parameters are randomly set, and dynamic bandwidth adjustment is introduced to effectively reduce the search difficulty; then, a collaborative mechanism of gradient optimization and cuckoo search is adopted, gradient descent based on a microphysical model is used for fine fitting of local parameters, cuckoo search is periodically introduced to perform variation on the structure so as to enhance global exploration, and local optimum is avoided. In the optimization process, adaptive learning rate scheduling and dynamic optimal solution updating are introduced, and convergence stability and global coordination are ensured. And after the global optimization is completed, gradient refinement is executed to further improve the precision. Finally, completely optimized physical parameters and topological structures can be automatically output, and the efficiency and overall performance of filter design are remarkably improved.

The invention provides a cutting engineering process capability balancing method. The method comprises the following steps: obtaining each process capability, obtaining a process sequence, calculating a beat demand, determining a production scheduling rule, and constructing a production scheduling model based on a cuckoo search algorithm; wherein the construction of the production scheduling model based on the cuckoo search algorithm comprises the steps of initializing a population, calculating fitness, updating a solution through Levy flight, applying a production scheduling rule, executing crossover and mutation operations, performing constraint processing and performing iteration termination and output. The cutting engineering process capability balancing method is convenient to operate and intelligent, scientific and reasonable production scheduling is achieved, production is efficiently organized, the operation time of all the processes is equal to or close to the average beat multiple as much as possible, time waste caused by idle stations is reduced, and cutting engineering production scheduling is optimized.

This application relates to the field of satellite navigation signal processing technology, and provides a method and computer equipment for integer ambiguity resolution based on capuchin monkey search algorithm and cuckoo search algorithm. The method includes: selecting a portion of capuchin monkey individuals as dominant individuals in a pre-defined population, who expand the search space by jumping or swaying; another portion of capuchin monkey individuals, acting as followers, move along the direction pointed by the dominant individuals to update their positions, guided by the dominant individuals' positions; then, a cuckoo uses the Levy flight mechanism to locally optimize the initial candidate solution set of the capuchin monkeys in real time; the candidate solutions of the capuchin monkeys are perturbed by the random perturbation characteristics of Levy flight; and finally, each element of the position vector corresponding to the final global optimal solution output after iteration is rounded to obtain an integer solution, which is then used as the final fixed optimal solution for integer ambiguity. This application demonstrates fast and accurate resolution performance and a high success rate in multi-dimensional ambiguity resolution.

A kind of method for predicting transformer hot spot temperature based on numerical calculation and linear regression model, steps are: determining transformer design object;Construct three-dimensional electromagnetic-temperature-fluid multi-coupling field simulation model;Obtain the size of each temperature measuring point temperature and hot spot temperature of transformer under different load coefficients and ambient temperature;Establish the database used when training and verifying prediction model;Based on neural network, construct the prediction model of distribution transformer winding hot spot temperature;Select cuckoo search algorithm to optimize the initial weight and threshold of neural network model.The method for predicting hot spot temperature based on cuckoo search algorithm optimized neural network model is established, and the numerical size of distribution transformer hot spot temperature under different operating environments is obtained.The present application is beneficial to evaluate the operating state of the transformer, and provides reference data for the subsequent use and operation of distribution transformer.It has practical engineering significance for ensuring the safe operation of distribution transformer and improving the level of rural power supply.

The invention relates to the technical field of power load prediction, in particular to a short-term power load prediction method for aggregating quadratic mode decomposition and a combination model, and the method comprises the steps: building a hybrid parameter optimization model of a cuckoo search algorithm and a variational mode decomposition method, determining the optimal parameters of variational mode decomposition, and obtaining the optimal parameters of the variational mode decomposition; performing primary decomposition and reconstruction on the original power load sequence by using the sample entropy to obtain a reconstructed high-frequency strongly unstable sub-sequence; carrying out secondary decomposition and reconstruction to obtain decomposed and reconstructed subsequences and feature vectors; and taking the decomposed and reconstructed subsequences and the feature vectors as model input, predicting high-frequency and strong non-stationary subsequences by using a long-short-term memory network and predicting low-frequency subsequences by using a gating circulation network unit, and superposing the subsequences to obtain a final prediction result. Therefore, the problem that the prediction difficulty is increased due to the fact that an optimal decomposition parameter cannot be adaptively determined by a decomposition technology and a high-frequency non-stationary component is generated by decomposition in the prior art is solved.

The invention relates to a tolerance analysis method and device based on a product assembly dimension chain. The method comprises the steps that an assembly tolerance graph and an assembly directed graph are acquired; generating an assembly dimension chain according to the assembly tolerance diagram; constructing a Jacobian spinor model based on the assembly dimension chain, and solving a tolerance contribution degree; performing semantic reasoning on the assembly dimension chain to obtain a tolerance transfer coefficient; performing optimization solution through a cuckoo search algorithm according to the assembly dimension chain, the tolerance contribution degree and the tolerance transfer coefficient to obtain a tolerance distribution scheme; and performing virtual assembly and tolerance analysis based on digital twinning according to the assembly directed graph and the tolerance distribution scheme to obtain an assembly success rate, and optimizing and solving the tolerance distribution scheme by introducing a cuckoo search algorithm in combination with a tolerance contribution degree and a tolerance transfer coefficient obtained by semantic reasoning. And then virtual assembly and tolerance analysis based on digital twinning are carried out to determine the assembly success rate, so that the accuracy and efficiency of tolerance analysis can be effectively improved, and the assembly efficiency and success rate are further improved.

The invention discloses a ramp aided driving control method, device and equipment for a heavy-load train and a storage medium, and relates to the technical field of intelligent traffic and vehicle engineering.The method comprises the steps that train attributes, running routes and running environment information of the heavy-load train are analyzed; line gradient, slope length information, speed limit information and ramp distribution position data are obtained; according to the data, constructing a multi-objective optimization model comprising a fault-safety function, a stability function and an energy consumption function; according to the line gradient, solving the multi-objective optimization model through a hybrid multi-objective optimization algorithm based on a non-dominated sorting genetic algorithm, a multi-objective cuckoo search mechanism and a Gaussian disturbance strategy to obtain train control parameters; and carrying out auxiliary driving control on the heavy-load train according to the train control parameters and the speed limiting information. According to the invention, safe, stable and energy-saving multi-objective optimization intelligent auxiliary driving control is realized for a long and large ramp operation scene of the heavy-load train.

This invention proposes a layout optimization method for the human-machine interface of intelligent machine tools based on a cuckoo-particle swarm optimization approach, belonging to the field of equipment interface ergonomics design. It includes determining the module importance of the intelligent machine tool human-machine interface, classifying visual perception intensity levels, constructing a layout optimization model, solving the layout optimization model, and evaluating the optimization effect. Based on the human-machine interaction task and the operator's cognitive characteristics, the ergonomic optimization design method for the intelligent machine tool human-machine interface is systematically studied. Furthermore, a solution method based on a cuckoo-particle swarm optimization hybrid intelligent algorithm is proposed, introducing a particle swarm optimization algorithm with inertial weight W into the cuckoo search algorithm to solve the problems of slow convergence and easy getting trapped in local optima in the later stages of the algorithm. Its beneficial effects are: obtaining an optimized layout scheme for the human-machine interface of CNC machine tools, improving the comfort and interaction efficiency of the human-machine interaction process of intelligent CNC machine tools, and providing good support for the optimization design of the human-machine interface of CNC machine tools.