115results about How to "Improve convergence accuracy" patented technology

The invention provides a coal mining digital twinning method based on multi-level dynamic deviation correction and AI optimization. The method comprises the steps of monitoring data acquisition and dynamic preprocessing, and performing denoising and space consistency detection on data; constructing and pre-processing the coal mine digital twinborn body; generating a nonlinear deviation field by adopting a high nonlinear variable interpolation and model mapping method; performing Kalman filtering error analysis and correction under the continuous analysis step, and marking an optimization model state in combination with a space mutation step; performing staged sliding window long-time-sequence fluctuation analysis, and classifying and marking regions according to an absolute value of an error and fluctuation; performing multi-level weighted error optimization based on a genetic algorithm, and adjusting material parameters and boundary conditions; carrying out material constitutive rectification based on dynamic Bayesian, and gradually enhancing the adaptability and accuracy of the model; and constructing a hybrid network structure AI optimization prediction model based on deep data driving, capturing the dynamic change of the material constitutive and the characteristic difference of the model constitutive, and realizing the precise digital twinborn simulation of the whole coal mining process.

The invention provides a valve leakage diagnosis method and system based on an improved Hereum algorithm optimized SVM (Support Vector Machine), and relates to the technical field of valve leakage diagnosis. Decomposing the collected original signal to obtain a limited number of IMF components; calculating a fuzzy entropy, and constructing a feature vector; taking the feature vectors as data samples, and dividing the data samples into a training set, a verification set and a test set; using the training set to train the SVM model, and according to the classification error rate of the verification set on the trained SVM model, performing iterative optimization on the parameters of the SVM model based on an improved He-Ma algorithm to obtain an optimized SVM model; inputting the test set into the optimized SVM model to obtain a valve leakage diagnosis model; and acquiring a new acoustic emission signal, and inputting the new acoustic emission signal into the valve leakage diagnosis model to obtain a diagnosis result. The method not only can improve the classification accuracy and generalization ability of the diagnosis model, but also can enhance the stability of the model under different working conditions and environmental noise.

The invention discloses a power prediction method considering photovoltaic power clustering, which comprises the following steps of: firstly, acquiring historical data of a photovoltaic power station, decomposing a photovoltaic power time sequence by using an STL time sequence decomposition method, performing DBSCAN clustering on the decomposed photovoltaic power data, identifying a low-power cluster and a high-power cluster, and predicting the power of the photovoltaic power station according to the low-power cluster and the high-power cluster; establishing a photovoltaic power prediction model based on a time convolutional network and Times Net for low power and high power respectively; secondly, a badger algorithm is improved, Chebyshev chaotic mapping is adopted to improve initialization of a badger population, and a Gaussian mutation strategy and a lens imaging reverse learning strategy are introduced to obtain MHBA; and optimizing hyper-parameters of the TCN photovoltaic low-power prediction model and the TimesNet photovoltaic high-power prediction model by using an improved badger algorithm, and predicting photovoltaic power by using the optimized models to obtain a final prediction value. Compared with the prior art, the precision and effectiveness of the photovoltaic power prediction model can be improved.

The invention relates to a chilled water system of a central air conditioner and an energy-saving optimization control method. The energy-saving optimization method comprises the following steps: S1, establishing an energy consumption model of the chilled water system of the central air conditioner; s2, firstly performing correlation analysis on the selected cooling load influence factors by adopting a grey correlation analysis method, screening out strong correlation influence factors, and establishing a central air conditioner cooling load prediction model based on PSO-LSSVM (Particle Swarm Optimization-Least Square Support Vector Machine); s3, optimizing the energy consumption model parameters of the chilled water system of the central air conditioner while predicting the cooling load value of the next point by using an improved white whale algorithm, and enabling the output result of the energy consumption model to reach the minimum value; the system comprises an energy consumption modeling module, a cooling load prediction module and an energy-saving optimization module. According to the method, the energy consumption model and the cooling load prediction model are established, the cooling load value of the next point is predicted, parameter optimization is performed, and an optimization structure is output; the energy consumption model and the cold load prediction model can be updated periodically, and the problem of performance reduction caused by long-time equipment operation is avoided.

The invention discloses a pedestrian and vehicle detection method based on an improved YOLOv10n. The pedestrian and vehicle detection method comprises the following steps: on the basis of an original model YOLOv10n, replacing a first layer and a third layer in Backbone and a seventeenth layer Conv in Neck with a space-to-depth convolution SPDConv; a focused spatial pyramid pooling module ASPPF is constructed, and semantic fusion of cross-level features is optimized; designing a global grouping coordinate attention module GGCA, fusing the GGCA into a Neck layer, generating attention maps by using global information of the feature maps in the spatial dimension, and weighting the input feature maps through the attention maps to enhance the feature expression ability; a WIou loss function is introduced, the bounding box loss of the network is optimized, and the convergence speed and regression precision of the model are improved. And training a model by using a data set which is made by autonomous labeling to obtain a training result, and completing a pedestrian and vehicle detection task by using an optimal training weight. According to the method, a good detection effect is achieved with a small number of model parameters, and the pedestrian and vehicle recognition accuracy is well improved.

The invention belongs to the technical field of federated learning, and more specifically relates to a heterogeneous federated learning-oriented adaptive differential privacy protection method. The method includes introducing adaptive noise at different levels of the model. The contribution of each level of the model to the overall learning effect is different, and in order to reduce damage to key features as much as possible while noise addition is performed, differential noise addition is performed on different parts based on the importance degree of the levels of the model, that is, less noise is applied to the more important level, and more noise is applied to the secondary level. According to the method, the problem that the traditional differential privacy federal learning method usually has negative influence on the performance of the model due to the introduction of noise, especially the problem that the convergence speed and precision of the model are reduced is solved.

The invention discloses a water turbine wear fault diagnosis method based on a deep learning model, and belongs to the technical field of water turbine fault signal processing. Comprising the following steps: acquiring a water turbine wear fault sound signal; a Tent chaotic mapping function is adopted to optimize an initial population of a white shark optimization algorithm WSO; introducing a long and short term memory (LSTM) network model to solve the limitation of a convolutional neural network (CNN) in processing a long sequence problem, and constructing a CNN-LSTM water turbine wear fault diagnosis model; an improved white shark optimization algorithm IWSO is introduced into the CNN-LSTM model, and hyper-parameters of the model are optimized, so that the diagnosis precision is improved, and the calculation amount is reduced; and importing the collected fault signal as an input into the model for training and testing to obtain a final diagnosis result. Diagnosis and identification of the water turbine wear fault signal are completed through the steps, the deep learning model has good robust performance, the diagnosis precision of the water turbine wear fault can be improved, and the deep learning model serves as a beneficial supplement of an existing water turbine state monitoring and fault diagnosis system.

The invention relates to a medical image target detection method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining a to-be-detected medical image and a medical image target detection model, according to the medical image target detection model, an expandable residual module is added in a backbone network, a multi-branch attention mechanism module is added to a head in a neck network, and at least part of conv modules in the backbone network and the neck network are replaced by YOLOv8 models of deep down-sampling modules. The medical image target detection model is obtained through joint regression loss function training; and inputting the to-be-detected medical image into the medical image target detection model to obtain a medical image target detection result. According to the medical image target detection method provided by the invention, the accuracy of medical image target detection is improved.

The invention discloses a PID (Proportion Integration Differentiation) parameter optimization method based on an improved graffiti search algorithm, and relates to the technical field of PID. The invention aims to solve the problems of insufficient local search capability, slow convergence speed and poor system stability in the prior art, thereby realizing a better PID parameter optimization effect. Specifically, the PID parameter optimization method based on the improved graffiti search algorithm comprises the following steps: constructing a PID controller model; a step of replacing a fixed perception probability and a step length in the model with an adaptive perception probability and a Levy flight step length; a step of adding a crow misleading prevention factor into the model; and acquiring preset parameters in the PID controller model based on a particle swarm algorithm. The preset parameters comprise proportional (Kp), integral (Ki) and differential (Kd) parameters. Parameters of the Levy flight step length are dynamically adjusted through a Bayesian optimization algorithm. The method is suitable for being applied to PID parameter optimization work.

The invention provides a post-earthquake emergency material distribution optimization method considering distribution fairness and demand urgency, and relates to the technical field of emergency logistics multi-target distribution optimization under the background of urban earthquake disasters, and the method comprises the following specific steps: S1, determining a basic hypothesis: considering the actual scene of disaster rescue operation, and making the basic hypothesis for the emergency material distribution problem; s2, establishing an emergency material distribution fairness model; s3, establishing a disaster point emergency material demand emergency degree evaluation index system; s4, establishing a multi-target path optimization model; and S5, solving the established multi-target model by using an improved mixed multivariate universe optimization algorithm. The mixed multivariate universe algorithm based on differential evolution is provided, higher convergence precision and better robustness and uniformity are achieved, the earthquake disaster emergency logistics distribution path optimization accuracy is improved, material demand urgency meeting and distribution fairness are guaranteed, the distribution cost is reduced, and the distribution efficiency is improved. And the disaster victim satisfaction degree and the demand emergency adaptation degree are improved.

The invention discloses a CNN-XGBoost power load prediction method based on an attention mechanism, and the method comprises the steps: obtaining related historical load data and feature data, carrying out the data correlation analysis, employing the CNN to construct a feature extraction module based on the advantages of the CNN in feature extraction, and carrying out the calculation of the data correlation. Aiming at the problem that a traditional convolutional neural network cannot distinguish whether the extracted features are effective for prediction, Attention is introduced, different weights are given to the features, the features with different output contribution degrees are distinguished, the extracted features are emphasized consciously, the relation between the extracted features and labels is explored, and an XGBoost algorithm is combined to determine whether the extracted features are effective for prediction or not. According to the method, the advantages of the CNN algorithm in the aspect of feature extraction and the advantages of the XGBoost algorithm of high nonlinear load prediction precision and high prediction convergence speed can be integrated, and the accuracy and stability of short-term load prediction of power consumption of the whole network are improved.

The invention discloses a method and device for achieving accurate lifting of a steel plate through automatic magnetism adjustment, and belongs to the technical field of steel plate lifting control. The method comprises the steps that core parameters of an electromagnet of an electromagnet lifting appliance and observation data in the historical lifting process are collected; the observation data in the historical hoisting process are preprocessed; constructing a reinforcement learning model, and training the constructed reinforcement learning model by using the preprocessed observation data in the historical hoisting process; wherein the reinforcement learning model adopts a self-adaptive learning rate and updating frequency; and the specification and number of the current to-be-hoisted steel plates are obtained, the optimal current of the electromagnet is obtained through the trained reinforcement learning model, and steel plate hoisting is achieved. According to the technical scheme, the electromagnetic attraction can be intelligently and accurately controlled, and the hoisting efficiency and precision are improved.

The invention relates to the technical field of motor control technologies, and particularly provides a permanent magnet synchronous motor back electromotive force reconstruction and position speed extraction method, which comprises the following steps of: establishing a mathematical dynamic model of a permanent magnet synchronous motor under a static coordinate system, and designing an improved super-spiral observer according to the model, the reconstruction precision and the convergence speed of the back electromotive force are enhanced; and standardizing the reconstructed back electromotive force, and extracting the position and speed of the permanent magnet synchronous motor rotor through an integral compensation phase-locked loop. According to the invention, by improving the existing super-spiral observer and the phase-locked loop, the convergence speed and the position estimation precision of the system are obviously improved.

The invention provides a mixed-flow water turbine runner crack and cavitation defect identification method which comprises the following steps: acquiring a normal image, a crack image and a cavitation image of a water turbine runner, and processing by using an image enhancement technology to generate a water turbine runner image data set; constructing a water turbine runner crack and cavitation defect identification network model based on the improved YOLOv8; training a water turbine runner crack and cavitation defect identification network model based on the improved YOLOv8; and detecting the turbine runner defects by using the trained turbine runner crack and cavitation defect identification network model. According to the invention, computer vision and deep learning technologies are adopted, and the crack and cavitation defects of the turbine runner blade can be accurately detected.

The invention relates to the technical field of virtual power plant differentiated contribution distribution and distributed carbon transaction under a multi-agent cooperation framework, and provides a differentiated multi-agent cooperative game-based virtual power plant low-carbon bargaining optimization method, and the method comprises the steps: constructing a multi-virtual power plant-load aggregator MVPP-LA cooperation alliance framework; a wind power system, a photovoltaic system, a combined heat and power generation system and an energy storage system are integrated, supply and demand interaction is enhanced, and a carbon emission quota distribution mechanism, a carbon transaction mechanism framework, a cooperative objective function and an energy system operation constraint condition based on energy interaction are constructed. Cooperative benefits are quantified through a Nash bargaining model, and a distributed adaptive iterative algorithm is adopted for solving, so that fair benefit distribution is realized, and carbon emission and transaction cost are reduced. The method plays a positive role in promoting the utilization of renewable energy sources, improving the utilization efficiency of energy sources and enhancing the flexibility and stability of the system.

The invention discloses a radial permanent magnet motor multi-objective optimization method and system considering end magnetic flux leakage, and the method comprises the steps: constructing a two-dimensional radial permanent magnet motor model based on a three-dimensional radial permanent magnet motor model, introducing a magnetic conductance coefficient in a main magnetic flux leakage region in the two-dimensional radial permanent magnet motor model, and constructing a two-dimensional equivalent model; determining an optimization target, a design parameter and a value range of the design parameter according to the obtained two-dimensional equivalent model; according to the method, the function of performing multi-objective optimization on the radial permanent magnet motor under the condition of considering the end magnetic flux leakage is realized, and the two-dimensional equivalent model is constructed by iteratively calculating the magnetic conductance coefficient through the radius of each air domain and the division number in the main magnetic flux leakage region, so that the calculation precision is guaranteed; and by adopting comprehensive sensitivity analysis to carry out hierarchical optimization on the design parameters, the problems that the precision of the agent model is reduced and an optimization algorithm is difficult to converge due to a large number of design variables can be solved, the calculation time is shortened, and the method is suitable for being widely popularized and used.

The invention provides a virtual power plant optimal scheduling method based on a whale algorithm. The method comprises the steps that an electric heating comprehensive system economic scheduling target with the lowest comprehensive operation cost as the target is determined; constructing constraint conditions of the virtual power plant optimization scheduling model; the whale algorithm is used to solve the economic dispatch target; and analyzing a model result, and evaluating the operation benefit of the virtual power plant. In order to solve the problems that virtual power plant optimization scheduling is high in calculation complexity, low in convergence speed and prone to falling into a local optimal solution, the whale algorithm scheme is adopted, the convergence speed and precision of the algorithm are improved through natural predation behaviors simulated by the whale algorithm and optimization of contraction factors and expansion factors, the calculation time is effectively shortened, and the optimal scheduling efficiency is improved. And the adaptability and the flexibility of the algorithm are enhanced through the shrinkage factor and the expansion factor which can be dynamically adjusted, so that the effects of optimizing the operation cost of the power system, improving the resource configuration efficiency and enhancing the stability of the power grid are achieved, and the development of sustainable energy is promoted.

The invention discloses a virtual power plant decentration AGC method and system based on a precise diffusion algorithm. According to the method, an AGC instruction decomposition model with optimal operation cost and highest frequency modulation performance as double optimization targets is designed according to the adjustment upper and lower limits of each resource and the actual line capacity constraint; a Lagrange multiplier of the model is decoupled, and an accurate diffusion algorithm is adopted to realize rapid and accurate solution of an instruction; and finally, an intelligent agent communication model containing communication noise is obtained. A precise diffusion algorithm is rewritten and a gain function is introduced, so that the precise diffusion algorithm has the capability of translating noise, and the robustness of the algorithm is improved.

The invention discloses a centralized phased array beam control method and system based on an FPGA, and relates to the technical field of phased array beam control, and the method comprises the steps: carrying out the real-time iterative operation through an incremental adaptive correction kernel in a centralized control FPGA, and obtaining an amplitude-phase correction increment and a corresponding target effective frame number; performing increment differential coding on the amplitude-phase correction increment and the corresponding target effective frame number in a centralized control FPGA (Field Programmable Gate Array) to generate an increment differential beam control message; sending the increment difference beam control message to an array element FPGA (Field Programmable Gate Array) at the front end of an array, executing frame-level atom switching according to a target effective frame number, and fusing the amplitude-phase correction increment and a locally stored old beam control weight in the switching process to obtain a final array element beam control weight; generating a real-time beam control directional diagram based on the final array element beam control weight; the stability and convergence precision of beam control are improved, and the bandwidth requirement of a control link is reduced.

The invention relates to an adaptive sparse channel estimation method applied to a reconfigurable intelligent surface (RIS)-assisted millimeter wave large-scale MIMO system. According to the method, two progressive algorithms, namely an NLMS-LOG algorithm and an NLMS-NLMF-LOG algorithm, are designed, and the method aims at making full use of the sparsity of millimeter wave channels so as to improve the accuracy and convergence speed of channel estimation. According to the NLMS-LOG algorithm, a logarithm and a penalty term are innovatively introduced into a cost function, so that the convergence speed is effectively increased; the NLMS-NLMF-LOG algorithm fuses the fast convergence advantage of the NLMS algorithm and the excellent performance of the NLMF algorithm under the condition of low signal-to-noise ratio, and the precision of channel estimation is further improved. Simulation experiment results prove that the scheme provided by the invention is obviously superior to the prior art in the aspects of channel estimation precision and convergence speed.

The invention discloses an analog circuit fault diagnosis method and system for optimizing an SVM (Support Vector Machine) and a storage medium. An SVM algorithm is optimized by adopting a fused particle swarm algorithm and an improved grey wolf algorithm. Comprising the following steps: extracting time domain features and wavelet packet features of various fault sample signals of an analog circuit, and fusing the two features as a final fault feature sample set; dividing the fault feature sample set into a training set and a test set according to a certain proportion; optimizing a kernel function and a penalty factor of the SVM by using a fusion particle swarm algorithm and an improved grey wolf algorithm, and constructing a PSO-IGWO-SVM model; and training and testing the PSO-IGWO-SVM model by using the test set, and outputting a fault diagnosis result of the analog circuit. By combining the advantages of the two algorithms, the classification effect and diagnosis precision of the SVM are improved. Meanwhile, by introducing a nonlinear convergence factor and a proportional weight dynamic adjustment strategy, the search efficiency and convergence speed of the algorithm are further improved.

The invention discloses a test case generation method, a test case generation device and electronic equipment. The method comprises the following steps: acquiring a first fitness value of a target path and an actual path corresponding to each target test case in a target population; dynamically determining a selection probability corresponding to each target test case based on the first fitness value and contribution degree corresponding to each target test case; according to the selection probability corresponding to each target test case, selecting a preset number of target test cases from the target population for genetic manipulation to obtain a next generation population; and updating the target population based on the next generation population, and returning to execute the obtaining of the first fitness value of the target path and the actual path corresponding to each target test case in the target population until a stop condition is satisfied, and outputting the target test case satisfying the target path.

The invention relates to the technical field of image processing, and discloses a medical image denoising method based on a 3D chaotic mapping dung beetle algorithm, and the method comprises the following steps: 1, inputting a to-be-processed original medical image, carrying out the noise adding of the original medical image, and generating a noisy image; step 2, inputting the noisy image into a hybrid filter used for denoising the image; 3, calculating an optimal parameter combination of each filter in the hybrid filter by adopting a dung beetle optimization algorithm, and configuring the hybrid filter by adopting the optimal parameter combination; step 4, performing denoising processing on the noisy image by using the configured hybrid filter to generate a denoised image; wherein in the step 3, the distribution position of the population is initialized by adopting 3D chaotic mapping; the technical problem of poor medical image denoising effect in the prior art is solved.

The invention is applied to the field of satellite constellation design, and particularly discloses a regional remote sensing satellite constellation optimization method based on an improved crayfish algorithm, which comprises the following steps: S1, selecting an orbit observed in a specific region, the orbit needing to meet the average revisit time and the task requirement; the number of satellites meets the task requirement, the orbit height meets the task requirement, and the remote sensing constellation design objective function is S2: mathematical optimization model, and the orbit control parameters of the satellite constellation can be determined according to the six elements of the satellite orbit. According to the regional remote sensing satellite constellation optimization method based on the improved crayfish algorithm, a population initialization improvement strategy and a refraction reverse learning strategy are set, the convergence speed and optimization precision of the algorithm are improved, initial initialization classification is performed on a population through the population initialization improvement strategy, solution individuals are uniformly distributed, and the optimization efficiency is improved. The subsequent initial optimization efficiency of the population is improved, a reverse learning strategy is refracted, the search range of the algorithm is expanded, and the accuracy of the method is improved.

The invention relates to an end-to-end target detection method based on channel information enhancement, and belongs to the field of target detection. Comprising the following steps: preprocessing and marking an image and then constructing a training data set; the method comprises the following steps: constructing an initial target detection model, replacing a C32 module in a neck network with a C3AM module, and adding a One-to-one Detect network structure at a detection end, thereby constructing a target detection model; training the target detection model based on the training data set to obtain a trained target detection model; and inputting a to-be-detected image into the trained target detection model for target identification. According to the method, the attention mechanism is added, the convolution channel to be fused is pruned, and the parameters of the model are reduced; through the One-to-one Detect network structure, the prediction result can be directly output during use, post-processing of the inference graph is not needed, and the problem that the detection speed is low due to the fact that parameters of a target detection model used in an existing target detection method are redundant and the detection result needs to be post-processed is solved.

The invention relates to a water quality anomaly detection feature selection method and system based on an improved pigeon inspired optimization algorithm, and belongs to the field of machine learning. Executing an improved pigeon flock algorithm, and evaluating the pigeon flock; endowing the position and speed of the pigeon with the lowest fitness to the globally optimal pigeon; the speed of the pigeons is updated, the speed of the pigeons is converted through a Sigmoid function, and the positions of the pigeons are updated according to the output value of the Sigmoid function; when an iteration stopping condition is met, returning to a global optimal pigeon; otherwise, mutating the pigeon flock, and updating the position and the speed through the literanism; ranking the varied pigeons according to fitness values, calculating an ideal destination, and updating the positions of the pigeons; according to the method, the self-adaptive iterative map and the compass factor are set, and meanwhile, an Litalian mechanism is introduced, so that information interaction and cooperation can be carried out among pigeon individuals, the common evolution of a group is promoted, and the search capability of the group is further optimized.

The invention discloses an unmanned aerial vehicle airport liftable platform optimization control method, and belongs to the technical field of PID control optimization, and the method specifically comprises the steps: 1, constructing an unmanned aerial vehicle airport liftable platform control system model; step 2, improving the salmons swarm optimization algorithm, wherein the specific improvement strategy is as follows: D1, improving a mathematical model of an exploration stage of the salmons swarm optimization algorithm by using a triangular walk strategy; d2, using a self-adaptive Levy flight migration strategy to improve a mathematical model in a development stage of a samson swarm optimization algorithm; step 3, optimizing PID controller parameters of the unmanned aerial vehicle airport liftable platform control system model by using an improved saltcat swarm optimization algorithm to obtain three optimal parameters Kp, Ki and Kd; and 4, applying the three optimal parameters Kp, Ki and Kd obtained in the step 3 to an unmanned aerial vehicle airport liftable platform control system model, and optimizing the control effect of the unmanned aerial vehicle airport liftable platform.

The invention relates to the technical field of unmanned driving, and provides a path planning method and device, equipment and a storage medium. The method comprises the steps that a plurality of ancestor individuals of a first round of iteration are randomly generated, any one individual represents a driving path of a plurality of stations in a series park, and the starting point and the end point of the driving path are respectively a planning starting point and a planning end point in a path planning task; and cyclically executing the following iterative operations: introducing a population energy index reflecting the current search progress of the algorithm, and adopting different dynamic competition mechanisms for a plurality of candidate individuals of the current iteration to ensure the balance of the algorithm between local development and global search, taking the candidate individuals reserved after screening and new candidate individuals generated based on at least part of the reserved candidate individuals as candidate individuals of next iteration; and taking the driving path obtained by decoding the optimal candidate individual with the highest fitness in the last iteration as the optimal path between the planning starting point and the planning ending point until the maximum iteration number is met.

The present invention discloses a spacecraft formation attitude cooperative control method based on event triggering, comprising: establishing a spacecraft formation system attitude dynamics model considering external disturbance and model uncertainty; setting cooperative error auxiliary variables according to attitude and angular velocity information of formation spacecraft and communication relationship between formation spacecraft; setting measurement error variables according to cooperative error auxiliary variables; determining event-triggered attitude cooperative control law and event triggering conditions according to cooperative error auxiliary variables and measurement error variables; controlling the attitude and angular velocity of formation spacecraft according to the event-triggered attitude cooperative control law, and judging whether the event triggering conditions are met, if so, updating the event-triggered attitude cooperative control law corresponding to the formation spacecraft, and sending the state information of the formation spacecraft to adjacent formation spacecraft. The present invention can significantly save the energy, communication and computing resources of the formation system while ensuring that the formation system has good control performance.

The present invention discloses a flexible job shop scheduling method based on an improved Seagull optimization algorithm, which belongs to the field of shop scheduling. The method includes: 1) solving the problem of small search space by improving the initialization population; 2) solving the problem that the Seagull optimization algorithm is prone to fall into the local optimum by redesigning the local search model; 3) enabling the offline trained model to quickly adapt to the new problem environment by integrating reinforcement learning. Through the present invention, the problem that the population diversity of the Seagull optimization algorithm decreases in the later iteration period and the algorithm is prone to fall into the local optimum can be solved; the convergence speed of the algorithm is accelerated and the accuracy is improved, so that the processing efficiency of the flexible job shop can be effectively improved, and the economic benefits of the factory can be improved. Finally, the method provided by the present invention is verified by examples and compared with other algorithms, which reflects the superiority of the method proposed by the present invention.