26 results about "Gravitational search algorithm" patented technology

The invention discloses an intelligent network equipment scheduling optimization method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing multi-source operation state data, and generating a scheduling input feature tensor; s2, constructing a Transform prediction model based on a multi-head self-attention mechanism, and outputting a task density and a resource pressure prediction value; s3, forming a search individual state vector by the predicted values, and initializing an individual population of the gravitational search algorithm; s4, constructing a fitness function and executing a gravitational search algorithm to generate an optimal task scheduling scheme; s5, issuing the optimal scheduling scheme to each device, executing task distribution, migration and scheduling, and collecting execution data; and S6, comparing an execution result with a predicted value, constructing a feedback data set, and jointly updating the model and the optimization mechanism. The invention aims to realize accurate prediction and global optimization of intelligent network task scheduling, improve the resource utilization rate and the system scheduling efficiency, and construct a closed-loop control mechanism with a self-learning capability.

The invention discloses a wireless network high-speed switching automatic test optimization method based on edge computing, and relates to the field of wireless network switching. An improved gravitational search algorithm is combined with a GIS deployment node, multi-dimensional data is collected through an intelligent heterogeneous sensor and reinforcement learning, preprocessing is carried out through a denoising auto-encoder based on a GAN, modeling is carried out through space-time diagram convolution and an LSTM mixed model, a test case is generated through reinforcement learning Monte Carlo tree search, and testing is carried out on edge nodes in a distributed mode. And an optimization strategy is formulated through multi-agent deep reinforcement learning, and strategy implementation and feedback are realized through SDN and a block chain. According to the method, multi-dimensional acquisition, intelligent modeling, automatic testing and deep reinforcement learning optimization are realized, the high-speed switching performance is improved, the time delay and failure rate are reduced, the testing efficiency is improved, resource allocation is optimized, the network security is enhanced, and stable and efficient operation of the wireless network is guaranteed in an omnibearing manner.

The invention provides a method and device for generating a heliostat field arrangement scheme of mountain topography, and relates to the technical field of solar thermal power generation engineering, and the method comprises the steps: constructing an irregular triangular network model of an initial heliostat field arrangement range through employing a Delou triangulation method; establishing a three-dimensional terrain curved surface model according to the irregular triangular net model; regularized grid arrangement is implemented on the three-dimensional terrain curved surface model; performing interpolation analysis on each grid in the regularized grid arrangement by adopting a cubic spline interpolation function, and determining an initial coordinate and a height of the heliostat field; and carrying out multi-objective optimization on the initial coordinates and the height by utilizing a projection area constraint condition and a self-adaptive gravitational search algorithm to obtain a final heliostat field arrangement scheme. According to the method, the technical defects of insufficient terrain adaptability, imperfect shielding effect control, insufficient system energy efficiency optimization and the like in the implementation process of the existing technical scheme can be effectively solved.

The invention provides an air source heat pump heating group regulation and control method and device oriented to power grid interaction, and relates to the technical field of air source heat pump heating group regulation and control, and the method comprises the steps: outputting a power grid regulation and control performance index of each air source heat pump heating group based on a trained gated circulation unit neural network; clustering each air source heat pump heating group on the basis, and determining a target clustering result corresponding to the optimal effectiveness evaluation index; according to an actual regulation and control capability index calculated according to the power grid regulation performance evaluation index, the actual regulation and control capability of each polymer is sequenced; determining a polymer participating in regulation and control and a regulation and control task based on the target power expected to be regulated and controlled by the power grid, the polymer power up and down regulation capability and the actual regulation and control capability, and allocating the regulation and control task according to a gravitational search algorithm so as to enable the difference value between the target power and the output power of the polymer participating in regulation and control to be minimum; the technical problems that in the prior art, regulation and control means are rough, model adaptability is poor, and response capacity cannot be managed in a layered mode are solved.

The invention discloses a single-target and multi-target flood interval forecasting method considering an interval fitting coefficient, and the method comprises the following steps: 1, obtaining historical runoff and rainfall data of a target research region, and carrying out the data quality inspection; step 2, screening LUBE interval forecast factors; 3, constructing an interval forecast evaluation index system; step 4, constructing an LUBE interval forecasting model based on a three-layer dual-output ANN neural network; step 5, establishing a single-target interval forecasting model based on a gravitational search algorithm GSA; step 6, establishing a multi-target interval forecasting model based on a non-dominated sorting genetic algorithm NSGA-III; step 7, determining and selecting a Pareto solution according to the preference of a decision maker, and determining model parameters to obtain a forecast result; according to the method, an interval forecast evaluation index system is enriched, and meanwhile, the method can be expanded to the single-target and multi-target research field.

A variable speed wind turbine maximum power capture method, system and application thereof, the method is based on active disturbance rejection control, RBF neural network and variable speed wind turbine maximum power capture method, including the following steps: establishing the equivalent model of variable speed wind turbine; using the second order linear active disturbance rejection control (LADRC) algorithm to construct the generator torque controller; using the gravity search algorithm to optimize the generator torque controller parameters and obtain the optimal parameter data set; based on the optimal parameter data set, the radial basis function neural network is trained, and the trained radial basis function neural output is suitable for the generator torque controller parameters under the current wind speed condition. Using the gravity search algorithm to optimize the generator torque controller parameters and obtain the optimal parameter data set; based on the optimal parameter data set, the radial basis function neural network is trained, and the trained radial basis function neural output is suitable for the generator torque controller parameters under the current wind speed condition.

The invention relates to the technical field of power system modeling and simulation, in particular to an intelligent equivalent modeling method for a wind power plant, electronic equipment and a storage medium, and the method comprises the steps: obtaining the dynamic response data of a plurality of doubly-fed fans in the wind power plant in a typical fault scene of a power grid; performing first-layer grouping on the doubly-fed fans according to whether a crowbar circuit is put into the doubly-fed fans or not; respectively extracting low-voltage ride-through fault response characteristics of the first type of input units and the second type of non-input units to serve as second-layer grouping indexes; obtaining an initial mass center of an MI-GSA-K-means algorithm by using a gravitational search algorithm; iteratively calculating DBI indexes corresponding to different clustering numbers, and selecting the clustering number corresponding to the minimum value of the DBI indexes as the optimal clustering number; and aggregating the grouped doubly-fed fans in the same group into an equivalent unit, and determining a wind power plant dynamic equivalent model based on LVRT-GSA dual-stage clustering. According to the method, the consistency of the equivalent model and the system fault process is improved, and the model precision can be effectively improved.

The invention discloses an electric power task allocation and optimization method and system, and belongs to the technical field of electric power Internet of Things, and the method comprises the steps: obtaining a calculation task generated by a business terminal; extracting a feature vector T of the calculation task through an edge access point or an edge node controller; periodically broadcasting the resource state vector S of each edge node, and quickly matching the feature vector T of the task with the resource state vector S of the peripheral available edge node based on the resource state of the edge node of fuzzy logic; modeling a feature vector T of the task and a resource state vector S of a peripheral available edge node into a dynamic system, and performing dynamic cooperative task allocation based on an improved gravitational search algorithm; and unloading the task to the target edge node for execution according to the distribution result. The method has the advantages that network and load changes are responded in real time, the multi-dimensional quality requirement of power business is accurately met, and the task allocation and optimization effect with the optimal global efficiency of the edge cluster is achieved.

The invention provides a transformer fault detection method and system based on machine learning, and relates to the technical field of transformer fault detection. Comprising the following steps: acquiring dissolved gas data in transformer oil, and constructing an original feature data set; performing data preprocessing and feature expansion on the original feature data set to generate an expanded feature data set; dividing the extended feature data set into a training set and a test set; constructing a fault diagnosis model based on a support vector machine according to the training set and the test set, and optimizing parameters of the support vector machine by adopting a gravitational search algorithm to obtain an optimized support vector machine model; and using the optimized support vector machine model to diagnose the fault type of the transformer to obtain a diagnosis prediction result. The method solves the problem that the traditional method in the prior art excessively depends on expert experience and a single machine learning algorithm, so that the accuracy of a transformer fault diagnosis prediction result is low.

The present invention provides a combine harvester operating speed control system and method based on multiple operating parameter reward systems. The control system includes the following steps: inputting actual operating speed, number of harvested crop supervoxels, grass-to-grain ratio, plant height, stubble height, and swath width information into a Gaussian process regression model of a gravitational search algorithm to obtain a predicted feed rate; establishing a DQN neural network, inputting the predicted feed rate into the DQN neural network, and inputting the trash content, breakage rate, loss rate, and rotation speed as reward functions into the DQN neural network, which outputs a theoretical operating speed; inputting the difference between the actual operating speed and the theoretical operating speed, and the change in this difference, into a fuzzy neural network PID controller, and controlling the forward speed of the combine harvester based on the output of the fuzzy neural network PID controller. This invention improves harvesting quality and efficiency while reducing failure rates and alleviating operator skill requirements and workload.

This invention discloses an automated testing and optimization method for wireless network high-speed handover based on edge computing, which relates to the field of wireless network handover. The method uses an improved gravitational search algorithm combined with GIS to deploy nodes, collects multi-dimensional data using intelligent heterogeneous sensors and reinforcement learning, pre-processes data using a GAN-based denoising autoencoder, models the data using a hybrid model of spatiotemporal graph convolution and LSTM, generates test cases using reinforcement learning Monte Carlo tree search, executes the tests in a distributed manner at edge nodes, formulates optimization strategies using multi-agent deep reinforcement learning, and implements strategy implementation and feedback using SDN and blockchain. The method utilizes multi-dimensional data collection, intelligent modeling, automated testing, and deep reinforcement learning optimization to improve high-speed handover performance, reduce latency and failure rates, increase testing efficiency, optimize resource allocation, enhance network security, and comprehensively ensure stable and efficient operation of wireless networks.

The invention discloses a satellite task planning method based on a gravitational genetic search algorithm, and relates to the technical field of satellite task planning. Aiming at the defects of low convergence speed, unsmooth optimization path and the like of a genetic algorithm and the defects of over-high convergence speed, poor global search capability and the like of a gravitational search algorithm, the invention provides a gravitational genetic search algorithm for improving a universal gravitational search algorithm by combining respective advantages of the genetic algorithm and the gravitational search algorithm. Individuals are divided into an elite solution and a general solution, and the iteration process of the whole algorithm is divided into three stages of standard gravitational search, elite solution cross heredity and partial individual variation. The gravitation genetic search algorithm provided by the invention not only complies with the law of motion of universal gravitation, but also adds population memory and population information, can avoid falling into local optimum, effectively improves the convergence rate of the algorithm, and can be popularized and applied in the research of satellite task planning problems in different fields.

The invention discloses an optical joint virtual energy storage model double-layer optimization scheduling method considering uncertainty, and the method considers the uncertainty of photovoltaic power generation, and builds a photovoltaic power generation GAN mathematical model through collecting historical data. Virtual energy storage is introduced, that is, electrochemical energy storage and virtual energy storage form an energy storage system, and double-layer optimization scheduling of a light storage combined system is realized by establishing a light storage combined double-layer optimization scheduling model. And finally, adding an improved strategy to the gravitational search algorithm, and solving the optimal configuration parameters of the optical storage operation model by using the improved gravitational search algorithm. The improved strategy is added on the basis of the gravitational search algorithm, the global search capability of the optimization algorithm is improved, the optimal configuration parameters of the optical storage model are obtained, and reference is conveniently provided for system operation.

This application discloses a transformer condition monitoring method, device, and electronic equipment, relating to the field of power equipment. It involves sequentially performing data normalization, outlier detection, and missing value processing on the acquired raw transformer data. The correlation between various features in the preprocessed data is analyzed, and the preprocessed data is divided according to the analysis results to obtain feature data characterizing transformer condition changes. A prediction model is invoked to extract and predict features from the feature data, obtaining feature prediction data for a preset future time period. Key feature data is extracted from the feature prediction data using a feature selection algorithm, and a fault identification model is invoked to perform fault identification processing on the key feature data. The fault identification model is obtained by optimizing an artificial neural network model or support vector machine based on a gravity search algorithm. By performing multiple data processing and filtering processes from data preprocessing to model prediction, the reliability and accuracy of subsequent model predictions are ensured.

The invention discloses a dam break omen method based on gravitational search and a double-branch converter, and relates to the technical field of dam break, and the method comprises the steps: carrying out the preprocessing of a pre-obtained multi-source remote sensing image data set; constructing an initial feature set, and performing feature subspace optimization on the initial feature set by adopting a gravitational search algorithm; constructing a double-branch converter model, inputting the optimal feature subset into the double-branch converter model, and embedding the optimal feature subset into a gravitation coefficient matrix; mapping the fragment-level abnormal heat map into dam body pixel-level, section-level and reservoir-level space grids; cutting the double-branch converter model, distributing an edge lightweight subnet model to an edge node, receiving a hierarchical confidence map, and uploading the hierarchical confidence map to a cloud node; reasoning is carried out at the cloud node, and dam break early warning is carried out. According to the method, cross-modal correlation modeling under time dynamic is realized, texture details of a local area and the displacement trend of the dam body can be distinguished more finely, and the detection capability of progressive micro-deformation of the dam body is improved.

The invention discloses a power grid power load management prediction system based on cloud computing. The system comprises a data acquisition and storage module; relates to the technical field of data processing, and is characterized in that a data set is decomposed into a plurality of subsequences through variational mode decomposition, setting of a target function is integrated, a mode correlation penalty term and a reconstruction error term are integrated, and in order to minimize inter-mode correlation and minimize a reconstruction error, namely, to ensure decomposition independence and decomposition accuracy, a method is provided for solving the problem of the prior art. According to the method, an adaptive chaotic particle swarm optimization algorithm, a dynamic neighborhood gravitational search algorithm and a dynamic neighborhood gravitational search algorithm are adopted, decomposition parameters which minimize a fitness function are finally determined, the quality of input data of the load prediction model is improved, and the prediction accuracy of the load prediction model is improved.

The present invention provides a method and device for generating a heliostat field layout scheme for mountainous terrain, relating to the technical field of solar thermal power generation engineering, including: using the Delaunay triangulation method to construct an irregular triangular mesh model of the initial heliostat field layout range; establishing a three-dimensional terrain surface model according to the irregular triangular mesh model; implementing a regularized grid layout on the three-dimensional terrain surface model; using a cubic spline interpolation function to perform interpolation analysis on each grid in the regularized grid layout to determine the initial coordinates and height of the heliostat field; using the projected area constraint condition and the adaptive gravitational search algorithm to perform multi-objective optimization on the initial coordinates and height to obtain the final heliostat field layout scheme. The present invention can effectively solve the technical defects such as insufficient terrain adaptability, imperfect control of the shading effect, and insufficient optimization of the system energy efficiency existing in the prior art solutions during the implementation process.

The invention discloses a water turbine PID parameter optimization method, system and device based on a gravitational search algorithm and a storage medium. The method comprises the steps that a mathematical model of a water turbine speed regulation system is established, and transfer functions of all parts in the mathematical model are determined; a low-pass filter is introduced on the basis of a traditional PID controller, and a mathematical model of a robust PID controller is obtained; taking the three parameters of the robust PID controller as optimization variables, establishing an objective function based on preset performance requirements of the water turbine speed regulation system, and constructing an optimization model based on a gravitational search algorithm; optimizing robust PID controller parameters by using a gravitational search algorithm; and finally, the optimized robust PID controller is applied to the water turbine speed regulation system. The method solves the problems of low convergence speed and easy falling into local optimum in the prior art, and has the characteristics of improving the global search capability, improving the optimization efficiency and improving the convergence speed by optimizing the parameters of the robust PID controller through the gravitational search algorithm.

The present invention belongs to the field of elemental quantitative analysis of X-ray fluorescence spectrometers (XRF), and discloses a method for quantitative analysis of elemental content combining Support Vector Regression (SVR) with Gravitational Search Algorithm (GSA), including: determining the element to be measured, and obtaining the XRF spectral data of the sample to be measured by using a spectrometer; determining the peak information of the element to be measured based on the spectral data; constructing a GSA-SVR model and training the constructed GSA-SVR model by using a data set, and predicting the content of the element to be measured based on the peak information of the element to be measured by using the trained GSA-SVR model. After normalizing the data set, the data is divided into a training set and a test set. An SVR prediction model is constructed by using the training set data, and then the performance of the prediction model is tested by the test set. A GSA-SVR model constructed based on the training sample data optimized by GSA is used to realize the quantitative analysis of elements through this model. The quantitative analysis of elemental content based on GSA-SVR can be widely applied to the field of XRF quantitative analysis of elements.

The application discloses a kind of complex product function structure configuration equilibrium solving method considering parameter misalignment, comprising: the direct influence interval matrix between the construction product function structure attribute;Design consensus reaching process measures the judgment of expert group and establishes updating mechanism;Affine arithmetic is used instead of interval analysis to avoid interval dependency problem;The reliable weight of product function structure attribute is calculated by improving DEMATEL method;The product function structure configuration equilibrium multi-objective optimization model considering parameter misalignment disturbance is constructed;Three kinds of intelligent optimization algorithms, improved gravity search algorithm based on adaptive mechanism, improved hybrid gravity search and differential evolution algorithm and projection gradient descent algorithm, are designed to solve the model and obtain the optimal configuration solution of product function structure.The application can effectively handle the parameter misalignment problem caused by cognitive uncertainty, and improve the reliability and balance of product function structure configuration.

The invention relates to a lithium battery life prediction method based on modal decomposition and sparse attention. The method comprises the following steps: firstly, carrying out 3sigma-linear regression detection and correction on a battery capacity attenuation curve, and filtering an outlier to enhance data stability; then, different modal components in the capacity sequence are extracted by using an ICEEMDAN decomposition method, and residual error and trend characteristics are modeled respectively; a sparse attention mechanism is introduced into the residual component to improve the capturing capability of key features; and finally, screening an optimal model in the candidate base learner through a gravitational search algorithm, constructing a Stacking integrated structure to complete capacity prediction, and predicting the residual life according to a capacity attenuation curve. The method effectively improves the prediction precision, stability and generalization ability of the model, and is suitable for efficient estimation of the service life of the lithium battery under complex working conditions.

The invention relates to the technical field of microgrid operation, in particular to a universal gravitation algorithm-based microgrid optimization operation method, device, equipment and medium, and the method comprises the steps: randomly generating a plurality of object initial positions in a D-dimensional search space based on a universal gravitation search algorithm, and obtaining an original solution group; optimizing the original solution group by using a reverse learning mechanism to generate a reverse solution group; sorting and screening the reverse solution group and the original solution group according to fitness values based on an elitist strategy to obtain an optimization group; calculating the fitness value of each object in the optimization population, and setting the current position of each object as the optimal position; gravitation parameters are updated, and population object positions are updated; and optimizing the population through a reverse learning mechanism and an elitist strategy, and repeating the steps until a preset iteration number threshold value is reached. Through a reverse learning mechanism and an elitist strategy, the optimization performance and convergence speed of the algorithm are improved, the problem of oscillation near an optimal solution is effectively avoided, and the robustness of the algorithm is improved.

The invention discloses a wind turbine generator resonance suppression system and method based on a multi-target H infinity / generalized H additional damping controller, and the system and method achieve the optimization of control parameters through the combination of pitch-torque control and a shrinkage coefficient particle swarm optimization gravitational search algorithm, and reduce the output power fluctuation and structural load of a wind turbine generator. And the resonance effect in a specific frequency range is weakened. The system is suitable for dynamic stability optimization of the large-scale wind turbine generator, improves the operation reliability and prolongs the service life.

This invention proposes a cooperative search method for underwater unmanned underwater vehicles (UUVs) based on an improved gravity search algorithm, relating to the field of UUV search. The method includes: deploying multiple virtual particles within the search area to form a gravitational field; dynamically updating the mass and orientation preference states of each particle during the search process; calculating the basic gravitational force acting on each UUV and performing orientation-sensitive corrections to obtain the total attractive force; calculating the repulsive force between the UUVs and modulating the repulsive force to obtain the total repulsive force; synthesizing the total attractive and repulsive forces to obtain the total resultant force, resulting in a new course and velocity; updating the position of the UUVs based on the new course and velocity, detecting and avoiding search area boundaries and obstacles, and updating the state of the detected particles. This invention achieves efficient adaptive cooperative search of an underwater UUV swarm by constructing a gravitational field using virtual particles.

The application discloses a power task allocation and optimization method and system, and belongs to the technical field of power Internet of Things. The method comprises the following steps: obtaining a calculation task generated by a business terminal; extracting a feature vector T of the calculation task through an edge access point or an edge node controller; periodically broadcasting a resource state vector S of each edge node, and quickly matching the feature vector T of the task with the resource state vector S of a peripheral available edge node based on a fuzzy logic edge node resource state; modeling the feature vector T of the task and the resource state vector S of the peripheral available edge node as a dynamic system, and performing dynamic collaborative task allocation based on an improved gravity search algorithm; and unloading the task to a target edge node for execution according to the allocation result. The application has the effects of real-time response to network and load changes, accurate satisfaction of multi-dimensional quality requirements of power business, and realization of task allocation and optimization with the globally optimal efficiency of edge clusters.

The present application relates to the technical field of electric data processing, more particularly, the present application relates to a conference content context-aware question and answer retrieval method and system for continuous conferences, comprising: acquiring multi-session conference text data of continuous conferences and user input retrieval requirements; performing vectorization processing on the conference text data and the retrieval requirements to obtain conference content vectors and retrieval vectors; and calculating semantic similarity of each session conference and the retrieval requirements.The present application constructs theme consistency drift index and time-effect correlation saturation, accurately quantifies evolution and turning point of conference theme and actual value of historical information, and on this basis, uses context correction coefficient to improve the gravitational search algorithm, so that the search process can dynamically adjust the gravity according to the context correlation strength, thereby effectively excluding obsolete information and achieving accurate and time-effective knowledge retrieval in long-span and complex logic continuous projects.