38 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 discloses a tourist route planning method and system based on artificial intelligence. The method comprises the following steps: step 1, collecting multi-modal input data and constructing a multi-modal data set; step 2, inputting the multi-modal data set into a Perciver IO model for encoding; 3, constructing an original urban environment map; 4, generating a personalized urban environment map based on the intention-environment bidirectional remapping network; 5, constructing a tourism path set; step 6, optimizing the travel path set based on an improved Rhododendron search algorithm, and introducing a heterogeneous reconstruction egg exchange mechanism to realize jumping type evolution updating; and step 7, ending evolution updating when a preset evolution termination condition is met, generating an optimized travel path set, and selecting the travel path with the highest fitness as a current recommended path. According to the method, multi-modal modeling and the improved Rhododendron search algorithm are fused, and efficient planning of the personalized travel route is realized.

The invention discloses a sea surface wind speed inversion method and device based on marine environment noise, and a storage medium. The method comprises the following steps: S1, obtaining marine noise data; s2, acquiring wind speed data according to time and latitude and longitude information in the ocean noise data; s3, audio data in the ocean noise data are processed, and center frequency point noise spectrum level data are obtained; s4, summarizing the noise spectrum level data of the center frequency point and the corresponding wind speed data into a neural network data set through time and latitude and longitude information; step S5, constructing an initial Elman network; s6, performing iterative optimization on the network weight and bias of the Elman network by adopting a cuckoo search algorithm to generate optimized network parameters; step S7, initializing an Elman network by using the optimized network parameters, and generating a CS-Elman model; training a CS-Elman model by using the neural network data set; and step S8, carrying out quantitative evaluation on the CS-Elman model. The method can achieve the high-precision prediction of the sea surface wind speed.

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 invention discloses a Tibetan teaching evaluation method and system based on artificial intelligence. The Tibetan teaching evaluation method comprises the following steps: S1, collecting multi-modal original data formed in a Tibetan teaching process; s2, preprocessing is carried out, a corresponding Tibetan dialect classification result is obtained, and parameter configuration required by the semantic coding model is loaded; s3, generating a student semantic embedding representation and a standard answer semantic embedding representation based on the semantic coding model; s4, constructing a semantic consistency scoring model and a training sample set; s5, optimizing parameters of the semantic consistency scoring model by adopting a fractional order cuckoo search algorithm; and S6, executing an automatic scoring task of the answer text of the student. According to the method, semantic coding and the fractional order cuckoo algorithm are fused, automatic scoring of Tibetan answering is achieved, and the method has the advantages of being high in adaptability, accurate in scoring and high in fairness.

The invention discloses an active power distribution network flexible resource collaborative planning method based on an improved CS algorithm, and the method comprises the steps: constructing a planning and operation integrated active power distribution network double-layer optimization model, enabling an upper planning layer to take the minimum annual comprehensive economic cost as a target, optimizing the locating and sizing of a distributed power supply and energy storage, and covering the investment cost, the operation and maintenance cost and the power purchase cost; the lower operation layer generates an energy storage charging and discharging strategy and a power flow scheduling scheme based on a typical day scene by taking minimum network loss and maximization of new energy consumption as targets, guarantees node voltage and line current safety margin through probability constraint, and constructs a multi-resource coordinated planning-operation closed-loop model; and adopting an improved cuckoo search algorithm to carry out iterative solution on the bilayer programming model. And improving a cuckoo search algorithm, combining a particle swarm optimization algorithm with the improved cuckoo search algorithm, converting double-layer multiple targets into a fitness function by adopting a linear weighting method, and solving an optimal planning scheme.

This invention provides a method and apparatus for predicting the risk of credit bond investment transactions, relating to the field of artificial intelligence technology. The method includes: collecting transaction data during the credit bond investment transaction process, the transaction data including training set data and prediction set data; performing supervised training based on the training set data and a support vector machine algorithm to obtain a credit bond risk prediction model; performing risk prediction on the prediction set data based on the credit bond risk prediction model and determining the prediction accuracy; using the prediction accuracy as the fitness value of a cuckoo search algorithm, iteratively optimizing the penalty factor and kernel parameters in the credit bond risk prediction model using the cuckoo search algorithm to obtain the risk prediction model; and performing risk prediction on target transaction data during the target credit bond investment transaction process based on the risk prediction model. This invention improves the prediction accuracy of the risk prediction model and reduces learning costs, thereby enhancing the accuracy of credit bond investment transaction risk prediction.

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 application discloses a full-slope erosion and sediment transport simulation method combining soil properties and land use, collects corresponding erosion and sediment transport basic data of full slopes of beam and valley slopes in a basin, including soil data, terrain data, land use data, vegetation data and water and sediment data; based on the erosion and sediment transport basic data, terrain conditions and land use type boundary conditions, the erosion environment type of the full slope is automatically distinguished, and the initial to-be-optimized parameters affecting the erosion are obtained based on the erosion environment type; the initial to-be-optimized parameters are updated based on the cuckoo search algorithm until the optimal parameters are obtained; the slope combination and slope length in the terrain conditions and the land use type and vegetation coverage combination in the land use type boundary conditions are changed, a plurality of different scenes are obtained, the full-slope sediment production and transport evolution process is simulated based on the different scenes and the optimal parameters, and the slope sediment transport rate under different conditions is obtained.

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.

A gravity value extraction method based on orthogonal distance minimization relates to the field of design of gravity value extraction methods using gravimeter interference fringe fitting. It addresses the numerous difficulties faced in extracting the phase of interference fringes during interference fringe fitting in the prior art, which in turn limits the measurement efficiency and accuracy of atomic gravimeters. The method of the present invention is implemented by initializing interference fringe-related parameters; setting the minimum sum of the orthogonal distances from data points to the curve as the optimization objective function; fitting the interference fringes using an adaptive cuckoo search algorithm; and calculating the gravity value based on the phase obtained after fitting. When optimizing the objective function, the method uses an adaptive cuckoo search algorithm that introduces multiple optimization strategies, improving the algorithm's global search capability and enabling better application in gravity value extraction scenarios. The method of the present invention can improve the accuracy of gravity value extraction to a certain extent.

The present application discloses a task scheduling method based on an improved particle swarm optimization algorithm, which includes: obtaining task data to be scheduled, encoding particles according to the task data; iterating the particles by a particle swarm optimization algorithm; in response to that the particle swarm optimization algorithm does not fall into a local optimal solution, outputting a scheduling scheme; and in response to that the particle swarm optimization algorithm falls into the local optimal solution, outputting the scheduling scheme by fusing the particle swarm optimization algorithm with a cuckoo search algorithm. The present application introduces a cuckoo search algorithm when the particle swarm optimization algorithm falls into a local optimal solution, solving the dilemma of the particle swarm optimization algorithm falling into a local optimal solution, while improving the global search capability of the algorithm.

The application discloses a target attribute recognition method and system based on three-dimensional force information of a mechanical finger tip, and five kinds of silica gel models with different hardness and three kinds of shapes are prepared; a mechanical hand with an optical three-dimensional force sensor is used to collect force data of the gripping experiment of the models, original data are preprocessed by feature dimension reduction, and the data set is divided into a training set and a test set; the data set is classified based on a support vector machine algorithm optimized by an improved cuckoo search; the training set is used to train the support vector machine classifier model, the cuckoo search method is used to optimize the support vector machine model hyperparameters, the test set is input into the classifier model to realize attribute classification, and the accuracy of the real class and the predicted class and the confusion matrix during attribute classification are obtained; and the newly collected data is classified and a three-dimensional force time sequence diagram is drawn, so that the hardness and shape attributes of the target gripped by the mechanical hand can be effectively classified, and the application has higher accuracy compared with other algorithms.

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.

The present invention provides a highway tunnel decision-making and maintenance system and a maintenance method thereof, which obtains tunnel static and dynamic data and geological environment data around the tunnel; based on the geological environment data, adopts a geostatistical simulation algorithm and a time series decomposition algorithm to perform geological risk analysis to obtain a safety risk assessment result of the tunnel structure affected by geological factors and a groundwater dynamic change trend prediction result; based on the geological risk analysis result, adopts a heterogeneous integration model of a dynamic Bayesian network and an extreme gradient boosting tree to fuse the tunnel technical status data, predict the tunnel technical status, and generate maintenance decision parameters; adopts a cuckoo search algorithm to optimize the maintenance decision parameters to minimize the maintenance cost under the influence of geological risk; and outputs a decision result including the total maintenance cost allocation based on geological risk, the expected improvement of technical status, the predicted value of the rock formation stability safety factor, and the groundwater seepage risk warning level.

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.

The invention discloses a full-slope erosion sediment transport simulation method integrating soil properties and land utilization, which comprises the following steps: respectively collecting erosion sediment transport basic data corresponding to full slopes of a ridge and loess hills slope and a gully slope in a drainage basin, including soil data, topographic data, land utilization data, vegetation data and water and sediment data; automatically judging the erosion environment type of the whole slope based on erosion sediment transport basic data, topographic conditions and land utilization type boundary conditions, and obtaining initial to-be-optimized parameters influencing erosion based on the erosion environment type; updating the initial to-be-optimized parameters based on a cuckoo search algorithm until optimal parameters are obtained; the slope combination and the slope length in the topographic condition and the land utilization type and vegetation coverage rate combination in the land utilization type boundary condition are changed, multiple different scenes are obtained, the whole slope sediment production and transportation evolution process is simulated based on the different scenes and the optimal parameters, and the slope sediment transportation rates under different conditions are obtained.

The invention relates to a parameter identification method, device and equipment for grid connection of water and electricity through an MMC-HVDC output system, and the method comprises the steps: obtaining an electromagnetic transient simulation model of the MMC-HVDC output system, and obtaining a control parameter set according to the electromagnetic transient simulation model; screening control parameters of the control parameter set by adopting a Sobol global sensitivity analysis method to obtain a dominant parameter set of the electromagnetic transient simulation model; an adaptive cuckoo search algorithm with a chaotic mapping strategy and a dynamic adaptive mechanism strategy is adopted to perform optimal solution iterative calculation on each dominant parameter in the dominant parameter set, so that an optimal dominant parameter corresponding to each dominant parameter is obtained, and the parameter identification precision is improved; and controlling the operation of the electromagnetic transient simulation model based on the optimal dominant parameter, thereby improving the precision and stability of the electromagnetic transient simulation model, and also improving the precision of the simulation result output by the electromagnetic transient simulation model.

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.

The invention relates to the field of communication, in particular to edge computing algorithm optimization for reducing service-side operation cost and improving system anti-interference capability, which is based on a Harris eagle algorithm proposed in recent years and aims at the characteristics of easy local convergence and slow convergence speed of the Harris eagle algorithm in the face of complex problems. A single eagle exploration strategy based on chaotic mapping and a self-adaptive adjustment strategy are integrated to improve the Harris eagle algorithm. And the improved algorithm is used for pattern forming of the array antenna, including peak side lobe level suppression, single-point null control, multi-point null control and null region control. Experimental results show that compared with a particle swarm optimization algorithm, a cuckoo search algorithm, an ant colony optimization algorithm and a standard Harris eagle algorithm, the provided improved Harris eagle algorithm has more superiority in the aspects of suppressing the sidelobe level of an array antenna pattern and controlling null. According to the method, the reduction of the operation cost and the improvement of the anti-interference capability are fully considered, the Harlisia eagle algorithm is optimized, then the Harlisia eagle algorithm is used for forming the directional diagram of the array antenna, and the performance of the Harlisia eagle algorithm is compared with that of other algorithms, so that the optimized algorithm has higher performance.