110results about How to "Convergent stability" patented technology

The invention discloses a garbage truck real-time monitoring and remote scheduling method based on the Internet of Things, and the method comprises the following steps: S1, collecting the real-time data of the operation state of a garbage truck, and carrying out the preprocessing of the real-time data; s2, constructing a task prediction model based on an improved adaptive graph neural network, and generating a preliminary task distribution matrix and a path planning scheme; s3, optimizing the preliminary task allocation matrix and the path planning scheme by adopting an improved PPO algorithm; s4, the optimized task allocation matrix and the path planning scheme are sent to a garbage truck vehicle-mounted terminal, and the task completion rate and the path deviation value are calculated; s5, the operation state of the garbage truck is monitored in real time, and the monitoring result and the operation state data are transmitted to the cloud Internet of Things platform; and S6, dynamically updating the scheduling instruction. According to the method, the Internet of Things, the adaptive graph neural network and the improved PPO algorithm are fused, intelligent scheduling and real-time optimization of the garbage truck are realized, and the method has the advantages of high efficiency, stability and low-cost operation.

The invention belongs to the technical field of new energy prediction, and discloses a power market new energy joint prediction method and system based on SA-GAN, and the method comprises the steps: obtaining the historical data of a power market; respectively adding the attention mechanism into a generator network and a discriminator network in the generative adversarial network, and establishing an attention-generative adversarial prediction model; inputting historical data of the electricity market into the attention-generative adversarial prediction model for training, and continuously optimizing parameters of the attention-generative adversarial prediction model through adversarial learning of a generator and a discriminator; and performing joint prediction by using the trained and optimized attention-generative adversarial prediction model to obtain new energy future power generation data. According to the method, the limitation of a traditional method on complex and nonlinear data processing is effectively solved, joint prediction of multivariable data is realized, interaction among variables is fully considered, and a more comprehensive and accurate prediction result is generated.

The invention relates to the technical field of knowledge graph completion, in particular to a large model knowledge graph completion method and system based on causal guidance. The method comprises the following steps: acquiring a target knowledge graph and an input triple to be complemented, performing structured analysis on an input triple relationship, extracting key topological characteristics, constructing a structured mediation variable, mapping the structured mediation variable into a structure guide prefix, injecting the structure guide prefix into large model input, and constructing a double-path inference model to generate an inference prediction result; and meanwhile, a gradient sensing dynamic loss balance mechanism is introduced, the loss weight is adaptively adjusted according to reasoning feedback, and finally a more accurate and stable knowledge graph completion result is output. According to the method, the controllability, interpretability and training stability of the reasoning process can be enhanced while the knowledge graph completion precision is improved.

The invention discloses a magnetic suspension rotor system model identification method and system based on a frequency response model, and the method comprises the steps: exciting a magnetic suspension rotor system through a harmonic signal, and collecting a time domain signal data sample under each input / output channel generated through excitation in the magnetic suspension rotor system in real time; converting the time domain data sample into frequency domain data to form a frequency response data set for model identification; performing unbiased estimation on the frequency response data set by adopting an optimization algorithm, and establishing an empirical transfer function estimation model; and evaluating the precision of the empirical transfer function estimation model by using a normalized root mean square error, and comparing the predicted output of the model with the time domain signal data sample to verify the accuracy and reliability of the model. Through precise harmonic excitation and real-time data acquisition, in combination with a data conversion technology, time domain signals are effectively converted into frequency domain data, and the efficiency and accuracy of performance analysis and control design of a magnetic suspension rotor system are remarkably improved.

The invention discloses a fine tuning method and system for precise parameters of a pre-training language model. The method comprises the following steps: constructing a robust adapter; a plurality of constructed robust adapters are connected in series to form a robust adapter chain, and the maximum chain node is determined; preparing a weight of a pre-training language model and a data set of a corresponding task label; firstly, each robust adapter in the robust adapter chain is subjected to preliminary training, optimization and approximate fine tuning of parameters of the robust adapters; after the parameters of the robust adapter are approximately fine-tuned, the parameters are combined with the weight of the preorder frozen pre-training language model to complete calculation of a current robust adapter chain node, residual information used for next iteration is generated, and the parameters of the current robust adapter are updated at the same time; and carrying out iterative training, updating residual information until the number of iterations is equal to the maximum node of the robust adapter chain, and obtaining the optimal weight of the pre-training language model and the optimal parameters of the robust adapters, so that more stable convergence and simpler hyper-parameter adjustment can be provided.

The invention discloses an intelligent vehicle finite time integral sliding-mode control method based on a disturbance observer, and the method comprises the steps: inputting composite disturbance into a disturbance observer model, obtaining a disturbance observer estimation value, inputting expected input linear velocity information and expected input angular velocity information, and obtaining an estimated value of the disturbance observer; the linear velocity integral sliding mode controller and the angular velocity integral sliding mode controller act to obtain position information and a vehicle running track, and the traditional sliding mode controller controls to obtain position information and a running track under a traditional sliding mode; and comparing the moving trajectory obtained by the linear velocity integral sliding mode controller and the angular velocity integral sliding mode controller with the tracking condition of the reference trajectory and the moving trajectory under the traditional sliding mode. According to the scheme, buffeting is effectively relieved, the system robustness is enhanced, the control precision is improved, the convergence speed of the system is increased, better intelligence and adaptability are achieved, better control performance and system stability are achieved, and the method is suitable for accurate path tracking and dynamic control of the intelligent vehicle.

The invention discloses a fast convergence reliability algorithm based on an improved AK-MCS model, mainly relates to the technical field of reliability analysis, and comprises a weight clustering algorithm, a multi-trend selection method and a sample number control method adopted for constructing the AK-MCS model, and the three algorithms can be combined for use or can be used independently; according to the method, the number of times of experimental point calculation can be greatly reduced while the model precision is ensured, and the failure rate calculation efficiency is improved.

The invention provides a multi-agent strategy network training method and device, a strategy generation method and device, equipment and a medium. The multi-agent strategy network training method comprises the steps that local observation states and global observation states of all agents and rewards for executing action strategies in the execution process of the action strategies generated based on a currently updated strategy network are collected; determining a target obstacle function value corresponding to each safety evaluation network based on the local observation state and a pre-constructed random discrete diagram control barrier function; determining the security constraint condition of each agent based on the approximate value of the obstacle function output by each security evaluation network; determining an advantage value of each agent action based on a state value prediction value output by the task value evaluation network and the collected rewards; and under the constraint of a security constraint condition, guiding the strategy network to perform parameter updating by taking the advantage value as an updating direction signal. According to the method, the training oscillation or divergence phenomenon caused by safety signal distortion is reduced, and a smoother and more stable convergence process is realized.

The invention provides a sparse system identification method and system based on an improved adaptive filter, and belongs to the technical field of adaptive system identification, and the method comprises the steps: obtaining an input signal at a current moment; updating a correlation estimation matrix and a cross-correlation estimation vector based on a conjugate gradient method; a nonlinear gradient is introduced, and the search direction of the filter is updated; introducing a regularization parameter to obtain a dynamic step length of the filter; updating the tap weight coefficient of the filter; and updating the moment to the next moment, updating the weight vector coefficient of the adaptive filter, and when the weight vector coefficient of the filter is equal to the pulse response of the unknown system, ending the identification of the unknown system. According to the method, the sparse unknown system can be identified more efficiently by modifying nonlinear gradient updating, regularization parameters and momentum enhancement updating.

The invention discloses a printed circuit board element placement method and device based on a traveling salesman problem and a medium, belongs to the technical field of traveling salesman problems and printed circuit boards, and aims to solve the technical problem that diversified constraint conditions and efficiency are difficult to consider when the placement sequence of traditional printed circuit board elements is optimized. According to the technical scheme, the method comprises the following steps: constructing a training data set; performing feature coding on the placement requirement of the printed circuit board element by using an encoder to obtain a coding result; decoding a coding result through a decoder based on a double-pointer network, and generating an element placement sequence meeting the priority constraint; and training the encoder-decoder architecture by adopting a near-end strategy optimization algorithm based on reinforcement learning, and finally obtaining a deep reinforcement learning model for solving the priority constraint traveling salesman problem for placing the printed circuit board element.

The invention discloses an image restoration method, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a first vector, so as to build a target function of an image restoration model; the first vector is formed by stretching a to-be-restored image; according to the plug-and-play precondition Kranoselski-Mann algorithm, an objective function is divided into different sub-problems through a fixed point theory, two precondition matrixes are introduced, and values are assigned to the two precondition matrixes according to an image restoration model and meeting a convergence condition; and the plug and play precondition Kranoselski-Mann algorithm is subjected to iteration for a plurality of steps to obtain a restored image. According to the image restoration method, the plug-and-play precondition Kranoselski-Mann algorithm is adopted, different weighting of each pixel point is achieved, meanwhile, the algorithm is stably converged to a real solution, and therefore the purpose of plug-and-play local restoration is achieved, and the robustness and the restoration effect of the algorithm are improved.

This application discloses a method and apparatus for pre-training large-scale models based on Bayesian domain reweighting. The method includes: dividing the pre-training corpus into multiple data domains and obtaining corresponding empirical loss signals; learning gamma prior hyperparameters based on the empirical loss signals using a prior prediction network to determine the morphological constraints of the variational posterior distribution; optimizing the morphologically constrained variational posterior distribution based on the optimization objective to obtain a converged domain weight distribution, from which the final domain weights are determined, and using this to perform weighted sampling of the pre-training corpus, achieving mixed sampling of pre-training data for large language models. This solves the problem in existing large-scale model pre-training processes where the learning trajectory of domain weights is unstable, making it impossible to stably and accurately learn the optimal domain weights while simultaneously achieving efficient computation, resulting in low training efficiency and weak generalization ability of large models. This method achieves efficient utilization of multi-domain data and improves the comprehensive performance of large models on different tasks.

The application discloses an emotion recognition method based on online cross-modal knowledge distillation, comprising the following steps: acquiring electroencephalogram and electrocardiogram original signals and windowing and cutting; constructing electroencephalogram and electrocardiogram student models, extracting intermediate features from each modal data segment through an encoder, and obtaining non-normalized prediction output through a classifier; constructing a teacher probability distribution through a joint encoder fusion; introducing adaptive contrast loss to align the cross-modal intermediate features, introducing distillation loss to constrain the prediction probability distribution of each modal to align with the teacher probability distribution; synchronously optimizing new student model parameters through online collaborative training; and performing actual inference prediction based on the student model after training. The application combines double modal signals to make up for the defects of single modal information, excavates the complementarity of modes, realizes dynamic generation of teacher supervision signals and real-time collaborative learning of modes through online distillation, does not increase test calculation overhead, effectively improves the recognition accuracy, model robustness and generalization ability, and has good application prospect.

The invention discloses a fruit fly larva tumbling motion modeling and optimizing method based on a material point method. Aiming at a collaborative design challenge of a larva rolling motion model and the problems that a traditional method depends on manual data measurement and is time-consuming in training, a neural mechanical model is designed in combination with biological characteristics of drosophila larvae, and an artificial neural network based on deep learning is constructed as a model controller for training; meanwhile, a hyper-parameter optimization method based on a TPE algorithm is designed. According to the method, the method is embedded into a microprogrammable framework, and high integration and efficiency optimization of the method are achieved. The method can effectively simulate and optimize the rollover motion behavior of the drosophila larva under limited computing resources. The soft body model with the biological characteristics of the drosophila larvae has the advantages of flexible movement and adaptability to complex environments. According to the method, the optimal control strategy in the rollover process of the drosophila larvae is explored, interaction between the drosophila larvae and a complex environment is simulated, and important reference is provided for design of a soft robot similar to the drosophila larvae in reality.

This invention discloses a multi-objective optimization method for flexible distribution systems based on reward decoupling normalization reinforcement learning. This method constructs a multi-objective deep reinforcement learning model based on group reward decoupling normalization strategy optimization to perform multi-objective optimization scheduling for complex constraints in flexible distribution systems, such as renewable energy fluctuations, load changes, and scheduling resource coordination. The method designs the three objectives—minimizing operating costs, minimizing voltage offset, and minimizing photovoltaic curtailment—as independent reward functions, guiding the flexible distribution system's operation strategy to focus on multiple dimensions, including economy, environmental friendliness, and safety. The group reward decoupling normalization strategy optimization algorithm independently normalizes each reward, avoiding the collapse problem of multiple reward signals in advantage estimation and effectively preserving the relative differences between the objectives.

The present application belongs to the technical field of Internet of Vehicles and communication security, and discloses a federated learning training method based on vehicle state perception and applicable to dynamic Internet of Vehicles. The method collects vehicle running state information of vehicle clients, preliminarily screens candidate vehicle clients, calculates comprehensive scores of the screened vehicle clients, adopts a client selection strategy combining selection of the top-ranked clients with random supplementation to determine a set of vehicle clients participating in the current round of federated learning, configures local training parameters for the selected vehicle clients according to vehicle speed and computing capacity, performs local training on local data sets to obtain model updates, performs weighted aggregation on the model updates of the effective participating vehicle clients at the server end, introduces a momentum mechanism to smooth the model update process, and obtains new global model parameters. This method effectively improves the stability, robustness and training efficiency of federated learning in a high-dynamic Internet of Vehicles environment.

The application belongs to the technical field of cloud computing and artificial intelligence, and is used to solve the problem that the prior art cannot perceive the anchor point domination relationship within a batch and dynamically constrain the batch boundary accordingly. Specifically, the application is a cloud server data security management system, which comprises a task acquisition module, a conflict analysis module, a conflict relationship construction module, a grouping management module and an execution control module. The application constructs the potential domination strength based on a sliding window and the output sensitivity based on the model sensitivity and the permission level for each task, calculates the conflict coefficient for batch constraint, so that when the input feature value of a task exceeds three times of the quartile range of its own historical distribution, the conflict coefficient between the task and the high sensitivity task automatically exceeds the dynamic threshold, thereby the task is split into different batch groups. The task pair with the conflict coefficient exceeding the threshold is marked as a conflict and prohibited from being in the same batch, and the low permission task cannot enter the same batch as the high sensitivity task.

The application provides an industrial part point cloud denoising method based on multi-expert fusion and segmented iteration, which comprises the following steps: obtaining a point cloud data set; constructing a denoising model; a geometric perception multi-expert module takes the preprocessed noisy industrial part point cloud as input, and respectively extracts local learning features and local geometric features representing the local structure of the point cloud; a routing gate and a geometric perception multi-expert fusion mechanism are used to process the local learning features and the local geometric features to obtain fusion features; a segmented iterative updating module takes the fusion features as input, and obtains denoised point cloud data through a noise perception segmented iterative updating mechanism; the denoising model is trained by using the point cloud data set, a detail discriminator and a smooth discriminator are introduced to construct a double-discriminator constraint loss during the training process, and a reconstruction loss, a geometric consistency constraint loss, an optimal transport distribution constraint loss and a trajectory consistency constraint loss are introduced; and the trained denoising model is used for point cloud denoising. The application solves the problems of poor noise adaptability and loss of geometric features.

This invention relates to the field of microfluidic control technology and discloses a method and system for detecting and controlling microfluidic droplet formation. The method first constructs an ideal concentric ring geometric model by acquiring the target parameters of the microcapsule and establishes a force-shape coupling response matrix. Based on this, a preset velocity triplet is solved to generate the initial droplet. Then, droplet image sequences are acquired and fitted with real-time concentric ring groups. These are geometrically superimposed with the ideal concentric ring geometric model to generate a deviation ring domain, which is then decomposed hierarchically to obtain geometric deformation characteristic components. Finally, a coordinated velocity adjustment vector is calculated using the force-shape coupling response matrix, a composite fluid correction field is constructed, and the deviation ring ablation cycle is iteratively executed until geometric convergence is achieved. This invention effectively solves the oscillation problem caused by strong parameter coupling in multiphase flow fields by constructing a quantitative mapping and inverse decoupling mechanism between fluid and geometric shape, achieving coordinated and precise control of the microcapsule's external dimensions and internal structure.

This invention provides a method and system for testing the electromagnetic radiation immunity of components, relating to the field of electromagnetic immunity testing technology. The invention sets a range for the electromagnetic frequency and amplitude of the interference signal, generating multiple particles characterizing the interference signal within this range. Through iterative particle swarm analysis, it rapidly collects highly sensitive sample points that cause functional abnormalities in components during electromagnetic interference testing. By clustering these highly sensitive samples, it locates the electromagnetic interference sensitive range of the target component. Compared to full-band scanning and fixed-point scanning methods for electromagnetic interference testing, this invention enables automated and quantitative extraction of the electromagnetic sensitivity range, reducing subjective bias from manual location and improving analytical consistency and repeatability. Furthermore, by focusing on highly sensitive frequency bands and field strength ranges, it optimizes the test path, reduces redundant scanning points, and significantly improves the efficiency of electromagnetic interference testing.

This invention discloses a method and system for geological hazard identification based on multimodal semi-supervised learning. The method includes the following steps: acquiring labeled and unlabeled orthophotos and elevation models of geological hazard areas; constructing teacher and student networks containing dual-branch encoders, extracting features from the orthophotos and elevation models respectively, and linearly fusing them, generating ensemble predictions using learnable dynamic weights; in semi-supervised training, calculating the uncertainty of the student network's prediction results in real time, and triggering an exponential moving average update of the teacher network using the student network parameters only when the decrease in uncertainty compared to the historical mean exceeds a preset evolution threshold; optimizing the student network parameters by combining supervised loss, confidence-weighted consistency loss, and student historical consistency loss; inputting the test data into the trained student network, and outputting fine-grained geological hazard segmentation results. This invention can achieve high-precision automatic identification of geological hazards under small sample conditions.

The invention discloses an emotion recognition method based on online cross-modal knowledge distillation. The emotion recognition method comprises the steps that electroencephalogram and electrocardio original signals are obtained and subjected to window segmentation; constructing an electroencephalogram and electrocardio student model, extracting intermediate features from each modal data segment through an encoder, and obtaining non-normalized prediction output through a classifier; teacher probability distribution is constructed through joint encoder fusion; self-adaptive comparison loss alignment cross-modal intermediate features are introduced, and distillation loss is introduced to constrain prediction probability distribution of each modal to align to teacher probability distribution; new student model parameters are optimized synchronously through online cooperative training; and performing actual reasoning prediction based on a student model after training. According to the method, bimodal signals are combined to make up single-modal information defects, modal complementarity is mined, teacher supervision signal dynamic generation and modal real-time collaborative learning are realized through online distillation, test calculation overhead is not increased, identification precision, model robustness and generalization ability are effectively improved, and the application prospect is good.

The invention provides a fine-grained image classification model training method and system based on heterogeneous resolution, and relates to the technical field of model training. Obtaining a multi-scale image based on the high-resolution fine-grained image sample; performing mosaic processing on the multi-scale image to obtain a heterogeneous image sample simulating degradation; the images are input into a fine-grained image classification model, high-resolution fine-grained image samples are input into a standard flow network to obtain standard features, and heterogeneous image samples are input into an adaptive flow network to obtain adaptive features; generating an information density map based on the energy difference between the standard features and the adaptive features, taking the information density map as a weight to construct feature repair loss, and guiding the adaptive features to be aligned to the standard features; respectively mapping the adaptive features and the standard features to a semantic manifold space, and constraining the consistency of the adaptive features and the standard features in the semantic direction; and the model parameters are optimized in combination with the loss function to obtain a trained fine-grained image classification model, so that the robustness of the model and the accuracy of application to image classification are improved.

The invention relates to the technical field of medium-high frequency vibration control, and discloses a structure medium-high frequency vibration variable weight adaptive robust compensation type active control method, which comprises the following steps of: respectively inputting error signals into a minimum mean square controller, a robust controller and a weight controller for operation; a weight controller compares the squared error signal with a reconstructed reference signal mean value, and adaptively adjusts the weights of a minimum mean square controller and a robust controller according to the signal mean value; the first output control quantity and the second output control quantity are superposed and then act on a structural vibration control point through an electromagnetic actuator to obtain an output signal, the output signal is superposed with an original reference signal, and a new error signal is formed to serve as an input signal of a next round of control operation. According to the method, a reference signal reconstruction technology is adopted, a robust compensation mechanism is additionally arranged, an adaptive variable weight strategy and a convergence time and error robustness statistical method are configured, and the medium-high frequency vibration control effect of structures such as a new energy automobile electric drive assembly speed reducer shell is effectively improved.

The invention discloses a rock slag feature recognition method based on multi-sensor fusion. The method comprises the steps that 1, an industrial camera and a laser radar synchronously collect rock slag images and point cloud data; 2, an improved YOLO11n-seg network is constructed; 3, performing feature extraction on the rock slag image through an improved YOLO11n-seg network; 4, constructing total loss; 5, training the improved YOLO11n-seg network on the basis of the total loss; 6, segmenting a subsequent rock slag image to be processed to obtain a rock slag area segmentation map; 7, projecting the original point cloud data to obtain the point cloud of each rock slag area; and obtaining the rock slag volume based on the point cloud of each rock slag area. The method is simple in step, instance segmentation of the rock slag target area is achieved through the improved YOLO11n-seg network, the contour shape and the volume of the rock slag are obtained, and therefore a basis is provided for parameter optimization of follow-up TBM tunneling.

The present invention belongs to the technical field of multi-agent collaborative control, and specifically relates to an IQL collaborative control method of an AI intelligent control system, the steps of which include constructing a virtual controller based on dynamic asynchrony for a multi-agent system MAS composed of N agents; introducing a cancellation model with multiple interferences in the virtual controller; for the IQL multi-agent system, using a virtual controller to improve the action selection process of the IQL multi-agent system, making each agent equivalent to an interrelated intelligent system rather than an independent individual, thereby improving the control output effect of the IQL multi-agent system under environmental instability; based on the improved IQL multi-agent system, realizing collaborative control of N agents. The present invention improves the adaptability and robustness of the multi-agent system in an uncertain environment, and can maintain a stable collaborative control effect under complex interference conditions.

This invention discloses a reinforcement learning simulation step size control method based on closed-loop adaptive noise injection and entropy increase optimization strategy, belonging to integrated circuit computer-aided design technology. Specifically, the method involves: first, inputting the circuit netlist and interacting with the simulator using file read / write; next, establishing two new network output layers to convert the deterministic actions of the policy network's output into a probability distribution; then, obtaining the entropy regularization term based on the probability density function and weight coefficients, and adding it to the policy loss function; next, generating Gaussian-distributed exploration noise and adjusting the standard deviation of the noise at each step using a PID controller; finally, adjusting the policy network parameters using a gradient update method and injecting the exploration noise into the output actions to obtain the final time step. Using this invention helps enhance the simulation stability of pseudo-transient analysis, improves simulation efficiency, and provides a new method for DC analysis.

The present application relates to a kind of nuclear power unit one loop boron concentration control system and method.The lower outlet of the nuclear power unit one loop in the system is connected with the first end of electric regulating valve and the first end of hydrogenation station respectively through pipeline, the second end of electric regulating valve is connected with the input end of volume control box through pipeline, the output end of volume control box is connected with the first end of filling pump through pipeline, the second end of hydrogenation station is connected with the first end of filling pump through pipeline.The second end of filling pump is connected with the recovery port of one loop.Electric regulating valve is connected with control terminal, and control terminal is used to adjust the opening of electric regulating valve.The present application can realize the rapid stable convergence of one loop boron concentration by the flexible adjustment of the proportion of RCV system lower flow through volume control box according to various working conditions.

The application discloses an image coding, decoding and compressing method and system based on vector quantization entropy modeling, which comprises the following steps: obtaining a first feature map of an image to be coded; obtaining a super-prior binary code stream and super-prior information of the first feature map; quantizing the first feature map by using a lattice vector quantizer; projecting the quantized first feature map to an integer coefficient domain of the lattice vector quantizer to obtain a second feature map; modeling the distribution of the second feature map as a Gaussian distribution independent of each dimension, and predicting the mean and variance of the Gaussian distribution by combining the super-prior information and a spatial context model; dividing the integer coefficient domain of the lattice vector quantizer by using a relaxed boundary, and performing probability estimation and arithmetic coding according to the predicted mean and variance of the Gaussian distribution to obtain a feature binary code stream; and combining the feature binary code stream with the super-prior feature binary code stream to obtain a compressed image binary code stream. The application improves the rate-distortion performance of image compression by using a more efficient lattice vector quantization on three-dimensional features.

The present application relates to the technical field of data processing, and particularly relates to a parameter optimization method for yucca saponin extraction process based on data processing, which comprises the following steps: obtaining multi-dimensional parameter data in the yucca saponin extraction process to construct all process parameter combinations, and initializing a particle swarm containing multiple particles, and each particle represents a set of process parameter combinations; outputting the predicted yield of the process parameter combination in the target iteration by using a machine learning model, and taking the particle corresponding to the process parameter combination with the highest predicted yield as the optimal particle in the target iteration; updating the particle swarm by using an improved particle swarm algorithm to output a global optimal particle, and taking the process parameter combination corresponding to the global optimal particle as the best extraction process parameter. The present application solves the problem of lack of flexible adjustment ability of the existing algorithm in different iteration stages.