354 results about "Stochastic gradient descent" patented technology

The invention relates to the technical field of computer-aided communication, in particular to a wireless mouse strong interference source avoiding method based on a wireless frequency hopping model, which comprises the following steps: acquiring multi-band signal characteristics by monitoring electromagnetic spectrum data of an environment where a wireless mouse is located in real time; constructing an interference source analysis model by using an improved weighted fuzzy clustering algorithm, and accurately identifying the type and intensity distribution of an interference source; according to an analysis result, dynamically generating a frequency hopping strategy by using a channel state prediction model based on reinforcement learning, and determining priority sequences of a main channel and a standby channel; when the interference on the main channel exceeds a preset threshold value, performing real-time evaluation by means of a composite quality index and switching to an optimal standby channel; meanwhile, through a closed-loop feedback mechanism, according to the difference between actual channel quality and expected channel quality, a stochastic gradient descent method is adopted to optimize frequency hopping strategy parameters, and dynamic self-adaptive adjustment of interference avoidance is achieved. Complex electromagnetic environment interference can be effectively dealt with, and the communication stability of the wireless mouse and the use experience of a user are remarkably improved.

Approximate closed-form bounds of Bayes security against record-level inference attacks, in particular membership and attribute inference attacks, are provided. In various embodiments, these approximate bounds of Bayes security are used in conjunction with training neural-network models by differential-privacy stochastic gradient descent to create trained models that achieve a desired level of privacy.

The invention relates to a coronary artery plaque rupture risk assessment method based on a non-local physical information neural network, and belongs to the field of artificial intelligence, and the method comprises the following steps: S1, introducing a near-field dynamics theory, reconstructing a mechanical constitutive equation of plaque rupture, and deducing a crack propagation criterion; s2, constructing a non-local physical information neural network, taking geometric structure information and material attributes of the coronary artery plaque as input, and taking a displacement field and a stress field of the plaque as output; taking the plaque mechanics basic equation as a physical constraint condition, and adding a loss function; s3, constructing a data set; s4, training the physical information neural network by using the training set, and adjusting network parameters by using a stochastic gradient descent optimization algorithm to minimize a loss function; optimizing and testing the model; and S5, inputting a coronary artery medical image number to be evaluated into the trained non-local physical information neural network, and outputting a rupture risk evaluation result of the coronary artery plaque.

The invention discloses a space target point cloud three-dimensional reconstruction and rendering method, belongs to the field of space target scene reconstruction and rendering, solves the problem of low accuracy of space target scene reconstruction and rendering, and comprises the following steps: generating an initial sparse point cloud and a camera pose of a space target by using an SFM; performing denoising and outlier processing on the initial sparse point cloud by using a bilateral filtering algorithm to obtain an initial point cloud; distributing an initial gray Gaussian volume for each point in the initial point cloud, and projecting the initial gray Gaussian volume to a two-dimensional plane by using a projection formula in combination with a camera pose; rendering the two-dimensional projection image by using a micro-rasterization rendering technology; fusing the luminosity loss function, the structural similarity loss function and the depth loss function to construct a total loss function, and optimizing an initial gray Gaussian body attribute in the rendered image by using a stochastic gradient descent method; a self-adaptive density control mechanism is introduced to adjust the density of the initial point cloud, and a space target point cloud three-dimensional reconstruction structure is generated; according to the invention, the reconstruction and rendering accuracy of the space target scene is improved.

According to the motor anomaly detection model training method and the motor anomaly detection method provided by the invention, a plurality of audio samples and category labels thereof during motor operation are acquired, a plurality of disturbance audios are generated through speed disturbance for each audio sample, the disturbance audios are input into a frequency domain feature extraction sub-model and a time domain feature extraction sub-model, and the frequency domain feature extraction sub-model and the time domain feature extraction sub-model are extracted. Performing linear transformation and weighted summation on the enhanced wavelet time-frequency spectrum and the enhanced time-domain features through a full connection layer to generate fusion enhanced features, performing feature transformation on the fusion enhanced features through a deep neural network, inputting the fusion enhanced features into a classifier to obtain a classification probability, and calculating triple loss according to a category label and the classification probability; and updating parameters of the frequency domain feature extraction sub-model, the time domain feature extraction sub-model, the full connection layer, the deep neural network and the classifier through loss back propagation and a stochastic gradient descent algorithm to obtain a trained motor anomaly detection model. Therefore, the accuracy of motor state recognition of the model in a high-noise environment is improved.

The invention relates to the technical field of GIS (Geographic Information System) equipment denoising, in particular to a GIS local interference source denoising method, which comprises the following steps of: constructing a hybrid architecture of a quantum convolutional neural network and a quantum recurrent neural network, extracting features by using a variable component sub-circuit as a convolution kernel in the quantum convolutional neural network, processing time sequence data by using quantum bit entanglement in the quantum recurrent neural network, and extracting a local interference source in the quantum recurrent neural network; normalization, coding and enhancement are carried out on electrical, electromagnetic and other multi-source data, a quantum stochastic gradient descent algorithm is used for training, a multi-modal fusion network based on an attention mechanism is also constructed, features of all modals are extracted through an independent convolutional layer, then splicing and weighted fusion are carried out, and finally a quantum reinforcement learning environment is constructed. An intelligent agent strategy network and a reward function are designed based on a quantum neural network, parameters are adjusted according to quantum Q learning, quantum calculation, deep learning and a multi-modal fusion technology are integrated, the GIS local interference source denoising effect is effectively improved, and stable operation of equipment is guaranteed.

The invention discloses an adaptive optical system non-common-path aberration correction method based on a neural network. The adaptive optical system non-common-path aberration correction method comprises the following steps: constructing a lightweight neural network CP-Net; simulating and generating a perfect point spread function image; generating a deformable mirror control voltage matrix based on the image; taking the voltage matrix as the input of CP-Net for training, and ending the training when the loss function value is smaller than a preset minimum value; the trained CP-Net is connected to an actual control loop, real-time images acquired by a scientific camera are received, and perfect reference voltage is generated and directly used for controlling and changing the mirror surface type of the deformable mirror. According to the method, the deformable mirror control command can be directly generated, aberration does not need to be measured, real-time automatic adjustment can be achieved, compared with a traditional phase diversity method and a stochastic gradient descent algorithm, the operation difficulty can be greatly lowered, the correction time can be greatly shortened, and an operator does not need to manually adjust related parameters in the correction process.

The invention discloses a medical auxiliary diagnosis method and system based on a convolutional neural network, and relates to the field of medical big data analysis, and the method comprises the steps: collecting various types of medical data, which comprise medical image data, electronic medical record text data and vital sign monitoring data; constructing a convolutional neural network model with a multi-branch structure; preparing a training data set containing labeled samples, wherein labeled information comprises disease diagnosis results and disease severity grades; training the convolutional neural network model by using the training data set, dynamically adjusting the learning rate in the training process by adopting a stochastic gradient descent optimization algorithm and combining a learning rate attenuation strategy, and introducing a regularization method; inputting to-be-diagnosed medical data into the trained convolutional neural network model, and outputting disease diagnosis probability distribution and disease severity prediction results through forward propagation calculation of the model; and generating a medical auxiliary diagnosis report according to the output prediction result.

Aspects of the disclosure are directed to implementing a projection-based stochastic gradient descent technique that maintains label differential privacy when training one or more machine learning models. The technique includes denoising gradients by exploiting projections when training the machine learning models to improve performance of the trained machine learning models while maintaining label differential privacy. For instance, the projection-based stochastic gradient descent technique can improve performance of machine learning models in higher-privacy regimes, such as digital content management.

The invention provides an electromechanical equipment fault prediction method and system based on multi-source information fusion, and the method comprises the steps: collecting operation data, including vibration data, temperature data and current data, during the operation of electromechanical equipment; performing feature extraction on the operation data based on a principal component analysis algorithm to obtain a fusion feature vector; inputting the fusion feature vector into a fault prediction model based on a deep belief network, and outputting a prediction result; wherein the deep belief network adopts a small-batch stochastic gradient descent algorithm combined with an adaptive learning rate adjustment strategy during training; and judging whether the electromechanical equipment has a fault hidden danger or not according to the prediction result. According to the method, the relevance between different types of data is mined, and the defect of low prediction precision is overcome.

The invention discloses a neural network selective forgetting learning method based on nerve cell importance, which comprises nerve cell importance evaluation, parameter mask generation, random label forgetting process and model fine tuning, and specifically comprises the following steps: distributing memory values of a forgetting sample by a model into nerve cells; the importance of the neurons on the forgotten data set is evaluated; setting the neuron mask with the memory value greater than a specific threshold value as 1, and otherwise, setting the mask as 0; randomly setting labels of samples in the forgotten data set, and updating neuron parameters of which the mask is 1 by using a stochastic gradient descent method; and finally, performing global fine adjustment on model parameters by using the reserved data set to obtain a final forgetting model. According to the image classification task-oriented neuron granularity selective forgetting learning method provided by the invention, the forgetting of feature levels is realized in the convolutional neural network, the forgetting effect and the original task performance are ensured, and meanwhile, the difference between approximate forgetting and accurate forgetting is reduced.

The invention relates to the field of mechanical engineering, in particular to an assembly error-based optical system imaging quality prediction method, which comprises the following steps of: firstly, constructing an accurate optical system finite element model, simulating a bolt tightening process, obtaining mirror surface deformation under the action of assembly stress, fitting a mirror surface shape by utilizing a Zernike polynomial, taking the mirror surface shape as an intermediate medium, and predicting the imaging quality of an optical system. The Zemax is adopted to simulate the imaging of the optical system under the conditions of mirror surface deformation and assembly poses, and the influence rule of various assembly errors on the imaging quality of the optical system is explored. Thirdly, an assembly data set is constructed through an integrated simulation method, an MLP-XGBoost combined neural network agent model is established, model training is carried out, and compared with a single MLP model, a single XGBoost model and a traditional regression model, the method has higher superiority and reliability in the prediction precision aspect. And finally, a stochastic gradient descent method is adopted to optimize the pre-tightening force under different primary and secondary mirror assembly pose conditions, the energy concentration ratio is averagely improved by 11.34% compared with the energy concentration ratio before optimization, and the assembly and adjustment efficiency and precision of the double-mirror optical system are improved.

The invention discloses a physiotherapy instrument control system and method based on artificial intelligence, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: collecting an electromyographic signal in real time, carrying out band-pass filtering and TK energy operator processing, and outputting a muscle activation feature vector; based on the muscle activation feature vectors, spatial-temporal features are extracted through a convolutional neural network, and a physiotherapy parameter combination is constructed; a PID controller is adopted to adjust the physical therapy parameter combination in real time, and optimized physical therapy parameters are output; performing electromagnetic thermal coupling simulation on the optimized physiotherapy parameters through finite element analysis to obtain a physiotherapy instrument control instruction; and updating the weight of the convolutional neural network by adopting a stochastic gradient descent algorithm according to the physiotherapy efficiency evaluation report, and outputting an optimization execution instruction. PID parameters are optimized through a quantum annealing algorithm, stimulation parameters are dynamically adjusted in combination with myoelectricity beta wave band phase locking, and treatment parameters and physiological response are synchronized.

The invention discloses a pre-training method and system for a drainage pipeline defect classification and grading model. The method comprises the steps of obtaining a drainage pipeline image without a label; performing updating training on the weight of a feature encoder by utilizing the obtained drainage pipeline image without the label, combining a composite transformation technology, designing an asymmetric branch comprising the feature encoder, reconstruction loss, similarity loss and a stochastic gradient descent algorithm, and completing pre-training of the feature encoder; performing feature extraction and region division on the drainage pipeline image by using a pre-trained feature encoder, generating a target object mask by combining a correlation technology of an image region feature vector, taking the target object mask as a supervision information training feature encoder weight, and selecting a part of the weight as an initialization weight of a backbone network; pre-training of the backbone network is completed; according to the method, the drainage pipeline image without a label can be effectively utilized for pre-training, and the adaptability and precision of the defect detection model are improved.

This invention discloses a SAR target detection and recognition method based on a multi-level enhancement network. This method primarily addresses the problems of existing technologies in complex environments, such as poor robustness, high false alarm and missed detection rates, and low detection and recognition accuracy. The method involves labeling and partitioning SAR measured data to obtain training and test sets; constructing a multi-level enhancement network consisting of a cascade of data-level enhancement modules, feature-level enhancement modules, region proposal modules, and decision-level enhancement modules; training the multi-level enhancement network using the training set based on a stochastic gradient descent algorithm; and inputting the test set images into the trained multi-level enhancement network to obtain SAR target detection and recognition results. This invention significantly improves SAR target detection and recognition performance in complex environments and can be used for battlefield reconnaissance and situational awareness.

The invention discloses a federal learning algorithm based on adaptive aggregation and dynamic training, and belongs to the field of deep learning, and the learning algorithm specifically comprises the following steps: S1, each client receives an initial global model parameter, and carries out model training by using local data; s2, locally updating the weight of the model by adopting stochastic gradient descent; s3, after local training is completed, the client side calculates a model loss value of the current round, and the loss is reserved to serve as a reference for subsequent adaptive aggregation; s4, on the client side, each client predicts the time demand of the next round of training by adopting a dynamic training strategy according to the historical training time of the client; according to the method, the accuracy and generalization ability of the model are improved, resource idling is avoided, it is ensured that computing resources of all clients are fully utilized, the convergence speed of the global model is increased, the computing overhead is reduced, resource utilization is optimized, and the cost is reduced.

The invention discloses a high-elasticity antibacterial yarn production control method and system, and relates to the technical field of textile manufacturing. The method comprises the following steps: acquiring tension, temperature, antibacterial agent distribution and other multi-dimensional state information, and generating a comprehensive performance evaluation index based on a feature projection and gating residual fusion model; the index is compared with target performance, and key influence parameters such as fiber arrangement unevenness and antibacterial agent application distribution density are identified; calling a process response model to generate a parameter adjusting strategy, and driving an adjusting device to adjust process variables such as tension and temperature through a control instruction; and finally, based on a stochastic gradient descent algorithm and in combination with an expert knowledge base, adjusting rules are periodically updated, and performance self-adaptive control is achieved. According to the method, the intelligence and robustness of regulation and control of the elasticity and the antibacterial performance of the yarn are improved, and the method is suitable for industrial production of high-end textiles such as medical yarn and intelligent wearing fabric.

The invention discloses a pavement crack recognition and classification method based on multi-feature scale fusion Faster-R-CNN, and particularly relates to the technical field of road detection, and the method comprises the steps: obtaining pavement image data of a research region through an automobile carrying a high-definition camera, and carrying out the preprocessing of the pavement image data to form a required image data set; a ZFNet is used as a crack feature extraction module to perform multi-feature fusion and enhance feature information, an RPN full convolutional network is combined to perform detection by optimizing an anchor box generation mode, and back propagation and stochastic gradient descent are used to optimize and extract a disease candidate region to realize disease classification and frame regression; according to the Faster-R-CNN target detection algorithm, the detection precision is improved through a Soft-NMS algorithm, the Soft-NMS algorithm is the same as an NMS algorithm in the execution process, but function operation is used for original confidence score, the objective is to reduce the confidence score, reduce the omission ratio and improve the detection precision, and construction of the Faster-R-CNN target detection algorithm is achieved.

The invention discloses a road crack deep learning segmentation method for a complex scene, and belongs to the technical field of image processing. Firstly, multi-illumination and multi-material road surface images are acquired at high altitude through carrying multi-spectral equipment by an unmanned aerial vehicle, and generalization is improved by adopting adaptive threshold graying, CLAHE illumination equalization and GAN data enhancement. Secondly, deformable convolution, a self-adaptive space attention mechanism and an object context representation module are introduced into the HRNet backbone network, and the problems of irregular fracture shapes and background interference are solved; and finally, class imbalance is relieved by adopting a focus loss function, and robustness is improved by combining stochastic gradient descent and a verification set dynamic weight storage strategy. In the test stage, edge equipment deployment is carried out, a pixel-level segmentation result is output, crack parameters are quantified, and decision support is provided for road maintenance. The method can significantly improve the crack segmentation precision in a complex scene, and especially has an engineering practical advantage in a fine crack and noise interference scene.

The invention discloses a machine olfaction system drift compensation method based on dynamic domain adversarial learning. The method comprises the following steps: step 1, collecting sample data through a machine olfaction system; step 2, constructing a DRDAN, and inputting the sample data into the DRDAN to obtain a predicted gas category and a domain classification label of the sample data; the DRDAN comprises a feature extractor, an integrated gas classifier and a domain discriminator, the integrated gas classifier is connected with the feature extractor, the integrated gas classifier outputs samples and finally predicts gas categories, the domain discriminator is connected with the feature extractor through a gradient inversion layer, and the domain discriminator outputs domain classification labels of sample data; and obtaining an optimal model parameter by minimizing a DRDAN learning objective function, and performing gradient updating through stochastic gradient descent. The method has the advantages that cross-domain joint distribution alignment is achieved, and class distinguishability is improved.

The invention relates to the technical field of organic light-emitting device production process data processing, and provides an organic light-emitting device production process optimization method, which comprises the following steps of: monitoring key parameters in an evaporation cavity in real time, analyzing data in a cleaning treatment process by utilizing a preset statistical algorithm, and optimizing the production process of an organic light-emitting device. And extracting feature data influencing the surface cleanliness and flatness of the substrate. And then, establishing a mathematical model in the vacuum evaporation process, and solving by applying an optimization algorithm to obtain a parameter combination which enables the performance of the device to be optimal. Furthermore, electrical and optical performance and life stability data of the device are collected, and optimization parameters are simulated, calculated and optimized through a data processing model. In the production process, equipment operation data are collected in real time, optimization parameters and real-time data are compared, and the parameters are optimized in real time through a stochastic gradient descent method and stored in a knowledge base. The device performance and the production efficiency are effectively improved, and the production cost is reduced.

The invention discloses a lumbar disc herniation postoperative recurrence prediction system based on a multi-modal medical image, and belongs to the field of medical images. The lumbar disc herniation postoperative recurrence prediction model based on the multi-modal image is trained by using the lumbar vertebra image set, the feature true value corresponding to each image and the recurrence prediction label corresponding to each patient, and in the training process, model parameters are updated by using a stochastic gradient descent algorithm; and a trained lumbar disc herniation postoperative recurrence prediction model based on the multi-modal image is obtained. According to the method and the system, due to rich image features provided by the multi-modal image, the prediction network trained based on the multi-modal data shows higher accuracy in postoperative recurrence prediction of the lumbar disc herniation.

The invention discloses an airborne multi-source integrity monitoring method and device for an aviation navigation operation network, and belongs to the field of satellite navigation, and the method comprises the steps: constructing an overall design architecture of an airborne multi-source high-performance navigation receiver; an ionosphere gradient monitoring algorithm based on carrier phase difference is provided, IGM detection statistical error characteristics of the carrier phase are researched, an ionosphere gradient monitoring threshold is established, and a switching mechanism from an airborne DFree mode to an IFree mode is realized; an aviation navigation multipath mitigation suppression algorithm is researched, multivariate feature extraction of observation data is combined, an observation data feature matrix is established, a parallel time convolutional neural network architecture is designed, and the weight is updated through a stochastic gradient descent method. According to the airborne multi-source integrity monitoring method and device for the aviation navigation operation network provided by the invention, accurate monitoring of the ionosphere gradient is realized, a switching mechanism between a DFree mode and an IFree mode is optimized, and the integrity monitoring capability and the system availability of the aviation navigation operation network are improved.

The invention belongs to the technical field of production control, and provides a combined optimization method and system for yarn combined weaving parameters. The method comprises the following steps: acquiring sample data containing first parameters such as yarn twist and elasticity and corresponding interaction characteristics, and sample labels such as pattern definition and cloth cover flatness; and inputting the data into the initial neural network, and calculating a loss value by combining a loss function which contains a mean square error and reflects a physical interaction weighted item through feature transformation of an input layer and a hidden layer. And by using a stochastic gradient descent algorithm, when the loss value is greater than a threshold value, performing back propagation to update the parameter training model until the loss value of the model meets the requirement and is not over-fitted in the verification set. Physical constraints such as influence of yarn twist and elastic interaction on pattern definition and influence of fabric density and twist on flatness are embedded through the mechanism sensing layer, effective interaction characteristics are constructed, and accurate prediction of silk fabric indexes is achieved.

The invention relates to a small model side privacy data enhancement method and system based on large and small model collaboration, and the method comprises the steps: determining a generator at a user side and a loss function of the generator according to a data mode type, training the generator at the user side through employing a differential privacy stochastic gradient descent method, generating synthetic data through employing the trained generator, and carrying out the prediction of the synthetic data. The synthesized data is sent to a server side; the server side calculates embedded vectors of the synthetic data by utilizing a large model, clusters the embedded vectors by adopting a clustering mode, and filters clustering results; sending an enhanced data set containing the user itself and the non-user itself and an embedded vector of each data in the enhanced data set to a user side; and after the user side fuses the enhanced data set and the local original data, the generator is updated.

This specification relates to privacy-preserving model training on tabular data. In some aspects, a method includes receiving, by one or more computing devices, tabular data; serializing the tabular data into a natural language string in a natural language format; combining the natural language string and a prompt as an input to a pretrained large language model (LLM) to generate a predicted result, wherein a set of learned vectors are added into the pretrained LLM for fine-tuning the pre-trained LLM; fine-tuning the pretrained LLM using a differential privacy stochastic gradient descent (SGD) process, wherein fine-tuning the pretrained LLM comprises: determining values of the learned vectors that minimize a difference between the predicted result and the ground truth; receiving a request including test tabular data for a predication task; and generating, in response to the request for the prediction task, a prediction result for the test tabular data using the fine-tuned LLM.

The invention specifically discloses a cigarette package abnormity monitoring method, which comprises the following steps of: preprocessing cigarette image data, determining the position of a cigarette package by utilizing edge detection, cutting and normalizing a picture; training a single-side cigarette anomaly detection model based on a memory network, dividing a data set, constructing a model containing a convolutional auto-encoder and the memory network, and training by adopting a stochastic gradient descent algorithm; multilateral cigarette packaging cloud edge collaborative anomaly detection model training based on deep mutual learning is carried out, and collaborative training is carried out through a cloud server and an edge end; training and applying a cigarette defect category subdivision model under a personalized federated learning framework, subdividing defect categories, and fusing knowledge at a cloud server side; and evaluating and optimizing the model by adopting a classification performance evaluation index and an AUC-ROC curve. The method can improve the detection efficiency and accuracy, and is suitable for the data characteristics of different edge ends.

The invention relates to the field of heat transfer, in particular to a deep learning-numerical simulation-based rapid and accurate prediction method for heat transfer of a data-driven fluid. Comprising the following steps: S1, constructing physical and mathematical descriptions of a semicircular micro-channel, and establishing a control equation of the semicircular micro-channel; s2, acquiring input conditions of specific prediction; s3, based on a three-dimensional finite element analysis result, constructing a corresponding three-dimensional model and carrying out finite element analysis; s4, designing a full-connection feedforward neural network with one input layer, four hidden layers and one output layer; s5, learning the neural network by using a mean square loss function as a criterion; s6, performing network parameter learning by using stochastic gradient descent based on a back propagation algorithm; and S7, carrying out gradient solution by adopting an Adm algorithm so as to improve the generalization ability of the model. The activation function is selected from a ramp function, namely a Received Line Unit function; finally, the deep learning model is driven through data to predict the heat transfer behavior, and the linear goodness of fit R2 serves as an evaluation index.

The invention discloses a physical layer secure transmission method and system based on a graph neural network, and belongs to the technical field of communication signal processing.The method comprises the steps that firstly, a scene where a plurality of eavesdroppers and legal users communicate with a base station is constructed, and the instantaneous receiving rate of a receiver is obtained through analysis; forming a problem model which aims at maximizing the legal user receiving rate of the system and minimizing the eavesdropper receiving rate of the system and takes transmitted beam forming as a variable; then, a framework based on a graph neural network is developed, wherein a beam forming vector is solved through neural network training; neural network parameters are optimized by adopting a stochastic gradient descent method, so that the purposes of effectively inhibiting eavesdropping and improving the security of a physical layer are achieved.

The invention provides an electroslag remelting smelting endpoint element dynamic forecasting and process decision optimization method and system, and the method comprises the steps: obtaining a historical data set and a real-time data flow in a smelting process, and carrying out the preprocessing of data; an online stochastic gradient descent and incremental learning algorithm is introduced, an element dynamic prediction model is constructed, sensor data in the smelting process are received in real time, and an element concentration prediction model is dynamically optimized; on the basis of a depth deterministic strategy gradient algorithm, an optimization decision model is constructed, and a smelting operation strategy is adjusted in real time according to element components, smelting working conditions and other environment parameters predicted online by the element dynamic forecasting model; and a real-time feedback mechanism is set, so that the element dynamic prediction model and the optimization decision model work cooperatively, and element concentration prediction and real-time synchronous adjustment of a control strategy are realized. The smelting parameters can be adjusted in real time, element prediction precision is improved, energy consumption is reduced, manual intervention is reduced, and smelting efficiency and quality stability are remarkably improved.