86 results about "AdaBoost" patented technology

The invention relates to the technical field of fault prediction, and provides a fault prediction method for a multi-modal cross-attention enhancement graph neural network, and the method comprises the steps: collecting the data of equipment; performing adaptive enhancement and normalization processing on the image data, performing sliding window segmentation, standardization and noise suppression on a time sequence numerical signal, and performing semantic vectorization coding on a maintenance log text; extracting low-dimensional spatial features of image data by using the pruned lightweight convolutional neural network, connecting time sequence features of modeling time sequence numerical signals in series, extracting context semantic expressions of maintenance log texts, integrating the features into multi-modal data, alternately taking each modal feature as Query and the other modal features as Key and Value, and obtaining multi-modal data; calculating attention weight and performing weighted fusion; constructing a modal node weighted graph, and performing inter-node feature propagation through a multi-layer graph attention network; and a residual service life regression prediction module and a degradation level classification module are deployed in parallel, and fault early warning is completed through multi-task joint optimization.

The invention relates to a water quality prediction method and system based on gating residual enhancement and feature fusion, and belongs to the technical field of water environment intelligent analysis and deep learning. Taking each water quality index as a node of the graph, and constructing two complementary variable relation graph structures by utilizing a Pearson's correlation coefficient and mutual information; respectively inputting the two graph structures into a graph convolutional network, extracting deep dependency features among indexes, and splicing and fusing the deep dependency features. A multi-head attention mechanism is used as a trunk to extract global time dependence, a GRU network is introduced to extract local time sequence features, GRU output is used as an adjustable residual term to be injected into the attention trunk through a residual gating mechanism, self-adaptive enhancement of local dynamic features is achieved, and finally a self-adaptive fusion mechanism is introduced to generate comprehensive representation. According to the method, the complex dependency relationship between the water quality indexes and the time dynamic evolution process can be modeled in a collaborative manner, the response capability to key local change and sudden change events is remarkably enhanced, and the accuracy and robustness of water quality prediction are improved.

The invention provides a multi-modal sentiment analysis method and system based on main modal two-stage guidance, and relates to the technical field of sentiment analysis. Inputting the multi-modal data into a multi-modal sentiment analysis model, and extracting language, visual and acoustic features from the multi-modal data through a feature extraction module; semantically decoupling the multi-modal features into modal invariant features and modal unique features through a feature space distribution alignment module, and realizing feature distribution alignment dominated by language modals through alignment reconstruction constraints; performing self-attention modeling on the modal invariant feature through an attention enhancement module to obtain a first enhanced feature, and adaptively enhancing the visual and acoustic unique features through a cross-modal attention mechanism by taking the language unique feature as a dominant feature to obtain a second enhanced feature; the first enhanced feature and the second enhanced feature are fused through the emotion prediction module, an emotion intensity prediction result is obtained through regression prediction, and the accuracy and robustness of emotion analysis in a complex scene are improved.

The invention provides a diffusion-driven channel adaptive point cloud semantic communication method, which belongs to the technical field of wireless communication, and comprises the following steps: obtaining a training point cloud data set, constructing a point cloud feature extraction network based on a hypergraph convolutional neural network as a semantic encoder, and constructing a channel adaptive enhancement module as a channel decoder, a channel adaptive recovery module is constructed at a receiving end as a channel decoder, a diffusion reconstruction network is constructed as a semantic decoder, end-to-end joint training is performed on the point cloud feature extraction network, the channel adaptive enhancement module, the channel adaptive recovery module and the diffusion reconstruction network, the diffusion reconstruction network predicts original point cloud distribution, and the point cloud feature extraction network and the channel adaptive enhancement module are subjected to end-to-end joint training. And a loss function is constructed based on the chamfering distance between the predicted point cloud distribution and the original point cloud. In the communication process, the reconstructed semantic features are input into the diffusion reconstruction network to reconstruct an original point cloud structure. The high-order semantic features of the point cloud can be effectively extracted so as to improve the adaptive capacity of the method to the incomplete point cloud.

The invention discloses an oil-based drilling fluid performance parameter prediction method, and relates to the technical field of oil-gas field development. According to the method, firstly, multiple training sets are obtained, each training set comprises a drilling fluid formula and temperature which serve as input and drilling fluid performance parameters which serve as output, then the training sets are used for training an Adaboost-BP prediction model, and the trained model can be used for prediction; the Adaboost-BP prediction model takes a three-layer BP neural network as a sub-model, and meanwhile, a feature enhancement-fusion module is arranged on a hidden layer of the sub-model. According to the method, multiple key indexes of the oil-based drilling fluid can be predicted at a time, and compared with a traditional method, the prediction efficiency is improved, and the application range is widened.

The invention discloses a fine-grained feature matching vehicle re-identification method for a license plate shielding scene, and the method comprises a vehicle detection stage based on illumination perception and adaptive prediction, and a fine-grained Transform feature matching stage based on implicit semantic prototype perception. Comprising an illumination sensing module and an adaptive prediction module. The illumination sensing module is used for realizing image adaptive enhancement under different illumination conditions; the adaptive prediction module suppresses a redundant prediction frame through a self-attention weight mechanism; in the fine-grained Transform feature matching stage based on implicit semantic prototype perception, an implicit semantic prototype learning module and a fine-grained Transform matching module are included; the implicit semantic prototype learning module unsupervised decouples vehicle structural components through a group of learnable semantic prototypes to generate a soft semantic distribution diagram; and the fine-grained Transform matching module is used for introducing semantic affinity bias in cross attention. According to the method, the accuracy and stability of vehicle detection and identity matching in a complex environment can be improved.

The application discloses a train axle temperature anomaly recognition method based on a generative adversarial network and ensemble learning, which comprises the following steps: collecting operation data in actual operation of an urban rail train, obtaining a two-class data set with unbalanced categories after preprocessing, and dividing the data set into a training set and a test set; training a constructor and a discriminator of the generative adversarial network by using abnormal data samples in the training set, and realizing automatic network parameter adjustment by using a Bayesian optimization algorithm; synthesizing abnormal samples by using the trained generative adversarial network model, and jointly constructing a training set with balanced categories with the original training set; filtering and screening noise samples by using a cross-committee filtering technology; constructing an axle temperature anomaly recognition classifier by using an AdaBoost method, training the ensemble learning model by using the training set, and inputting the test set to obtain a test result. The application solves the problems of missing of the axle temperature abnormal samples of the urban rail train and data imbalance, and improves the accuracy and correctness of the axle temperature anomaly recognition.

The invention discloses a breeding performance prediction system based on an intelligent sheep breeding platform, and belongs to the technical field of animal husbandry informatization. The intelligent breeding platform is designed, standardized collection, storage and intelligent management of sheep full-life-cycle data are achieved, and a solid data foundation is laid for follow-up research; secondly, innovatively combining ensemble learning (AdaBoost, GBRT and the like), machine learning (SVR, KNN and the like) and a sorting learning algorithm, and constructing a multi-level breeding sheep breeding performance prediction model system; particularly, a subjective and objective combination weighting method based on AHP-PCA is provided, and the prediction precision of the model is remarkably improved through feature reconstruction; in addition, an Achimedes optimization algorithm (AOA) is introduced into the field of breeding prediction for the first time, and adaptive search of hyper-parameters is realized by using a physical simulation mechanism of the AOA, so that the accuracy, efficiency and robustness of the model are remarkably improved.

This invention provides a landslide susceptibility assessment method based on categorical feature enhancement, belonging to the field of landslide prediction. The method includes: preprocessing multi-class data of the landslide disaster research area, deriving the original values ​​of evaluation factors corresponding to landslide points to obtain an original dataset; training a categorical feature enhancement algorithm and performing grid search to obtain a categorical feature enhancement algorithm with optimal parameters; using an adaptive enhancement model as a meta-learner, and using the categorical feature enhancement algorithm with optimal parameters as a base learner for adaptive enhancement ensemble modeling to obtain an ensemble model; substituting the training set into the ensemble model for training simulation, and importing grid points into the trained ensemble model for landslide susceptibility assessment, outputting a landslide susceptibility prediction map. This invention solves the problems of low adaptability of existing machine learning models to complex data and low accuracy in susceptibility assessment.

The application provides an open vocabulary segmentation method and system for multi-modal model representation optimization, and belongs to the technical field of computer vision. Image data to be segmented is acquired; a pre-trained multi-modal model is used to process the acquired image to obtain a segmentation result. The application better optimizes visual-text representation in a multi-modal task, effectively aligns the same visual-text representation space, proposes a mask-sensitive loss to constrain the classification score and mask quality to be consistent in the parameter fine-tuning process, thereby giving the visual encoder local perception ability and improving the effect of the model in the fine-grained downstream task, introduces the original pre-training feature as a representation compensation to ensure the zero-shot ability of the pre-training visual-language model in the optimization process, and interacts the text representation and the visual representation, so that the text representation can be adaptively enhanced for different input images, and the alignment property of the visual-text in the open vocabulary segmentation can be effectively improved.

The invention relates to the technical field of large language models, in particular to a gearbox degradation trend prediction method of multi-head memory LLM under unsteady excitation, which comprises the following steps: performing feature extraction based on a vibration signal to obtain a time-frequency domain feature set; screening out a plurality of degradation sensitive features from the time-frequency domain feature set; performing feature fusion on each degradation sensitive feature to obtain a fused degradation feature; inputting the fused degradation features into a trained multi-head memory large language model, and outputting a degradation trend predicted value; the multi-head memory large language model adopts a multi-head memory attention mechanism to enhance the model feature extraction capability; a dynamic enhancement factor is generated based on the decoding features through an adaptive enhancement normalization layer, and the normalization strength is adjusted in real time to obtain enhanced normalization features; and inputting the enhanced normalized features into a large model detection head to obtain a degradation trend prediction value. According to the method, the gear box degradation trend prediction precision and efficiency can be improved.

This invention discloses an intelligent management method based on real-time speech recognition and transcription, relating to the field of artificial intelligence technology. The method includes the following steps: Step S1, multimodal audio perception and environmental adaptive enhancement; Step S2, acoustic feature extraction and real-time transcription mapping; Step S3, semantic error correction compensation based on dynamic context weighting; Step S4, structured element extraction and logical association reconstruction; Step S5, intelligent management closed-loop decision-making and task distribution; Step S6, multi-source information backtracking and index construction; Step S7, intelligent management efficiency evaluation. This application can solve the problems of limited recognition accuracy and missing semantic logic under complex sound fields. By improving transcription accuracy through multimodal perception and dynamic semantic compensation, it achieves automated connection from speech recording to structured management decisions, significantly improving office management efficiency.

The present invention discloses a method for predicting the internal leakage level of a valve based on an acoustic emission signal, which solves the problems in the prior art of being unable to automatically extract valve internal leakage features, as well as the problems of low modeling efficiency and low prediction accuracy. The method comprises: first, collecting the acoustic emission signals of the valve under different internal leakage rates, and performing bandpass filtering preprocessing on the original signals. Then, wavelet scattering transform is used to extract features of the valve internal leakage acoustic emission signal, the second-order scattering coefficient is converted into a two-dimensional feature matrix by averaging in the time dimension, and the ReliefF algorithm is used to extract the optimal scattering coefficient feature. The optimal feature subset and pressure are used as inputs of the classification model, and the AdaBoost.M1 method is used for classification modeling. Finally, the trained AdaBoost.M1 model is used to predict the internal leakage level of the unknown valve internal leakage acoustic emission signal. The present invention realizes the automatic extraction of valve internal leakage features and improves the efficiency and prediction accuracy of valve internal leakage acoustic emission signal modeling.

The present application relates to a kind of multi-loop cable group electromagnetic loss intelligent calculation method and device, belong to power cable operation technical field, method includes: with finite element method to establish the cable group electromagnetic field model including multi-loop, current sample is randomly generated and corresponding cable core and metal sleeve loss are calculated, form current-loss sample set;GWO-RBF-Adaboost Intelligent Prediction Model is constructed, the center point of radial basis function neural network RBF, width and output weight are globally optimized by grey wolf optimization algorithm GWO, and the RBF neural network is used as the base learner of Adaboost, and current-loss sample set is used to train to obtain current-loss mapping relationship model;The current data of multi-loop cable group to be evaluated is input into the GWO-RBF-Adaboost model trained, and the cable core loss and metal sleeve loss of each loop are predicted.The present application realizes the convenient calculation of multi-loop cable group electromagnetic loss by current, and significantly improves the calculation precision of electromagnetic loss.

The invention discloses an adaptive enhanced dynamic graph contrast learning multivariate time sequence classification method, which is oriented to a multivariate time sequence classification task under the condition of few labels, and comprises the following steps: 1) an adaptive enhanced strategy; 2) dynamic graph comparison learning; and 3) carrying out two-stage combined training. According to the method, in a preprocessing stage, trend-periodic decomposition and statistical significance test based on random permutation are combined to judge the strength of time sequence dependence, and an enhanced recommendation strategy matched with data characteristics is generated; and dynamic graph comparison characterization learning and a two-stage training mechanism are introduced, so that the classification performance and the model stability are improved while unlabeled data are fully utilized.

The application discloses a knowledge reasoning path selection and evaluation method, system, device and medium based on reinforcement learning, belongs to the technical field of path selection and evaluation, and comprises the following steps: representing a knowledge graph as a graph structure, adopting a deep reinforcement learning framework; generating node embedding vectors by using graph contrast learning and an adaptive enhancement mechanism; constructing a generation network and a discrimination network to obtain a candidate reasoning path; constructing a multi-objective reward function which fuses a topological connectivity reward and a semantic consistency reward, and performing reinforcement learning; calculating dynamic propagation weights of nodes in a reasoning process by using an information propagation model, and integrating the dynamic propagation weights into node feature representation; and adopting a deep reinforcement learning method to perform end-to-end training on an agent, and outputting a reasoning path and a path evaluation score. The application is suitable for multiple actual reasoning scenes, and provides a technical path for deep knowledge discovery and intelligent decision-making of a large-scale knowledge graph.

The invention relates to a medium-and-long-term wind power generation capacity prediction method, system, medium and equipment based on multi-model fusion, and belongs to the field of medium-and-long-term wind power generation capacity prediction.The method comprises the steps that historical power generation data and weather forecast data of a wind power plant are obtained; correlation analysis is carried out on the data, key variables influencing wind power output are extracted, and an optimal input data scheme is screened out; inputting the data into a trained BP-Adaboost model, a generalized regression neural network model and a fuzzy neural network model for medium and long term prediction of the wind power generation capacity; performing dynamic optimization on the weight of each sub-model by using a particle swarm optimization algorithm, and establishing a medium-and-long-term wind power generation combined prediction model with multi-model advantage fusion through the optimized weight; according to the method, different meteorological data are utilized to select the data with high correlation degree with the wind power generation capacity, and the prediction results of the three sub-models are fused, so that the advantages of each model are fully played, and the prediction precision of the model is improved.

The invention discloses a self-adaptive collaborative oversampling and unbalanced multi-classification method and system, and the method comprises the steps: selecting a minority class sample which is difficult to classify as a target sample for synthesis through the weight generated by AdaBoost before each iteration, and can avoid the synthesis of an invalid sample. In addition, a sample synthesis mode is changed, the phenomenon of uneven local interpolation is avoided, meanwhile, the interpolation position and range of the synthesized sample are adjusted in a self-adaptive mode along with the increase of iteration, and the diversity of minority class samples is increased. And finally integrating T classifier results so as to improve the multi-classification accuracy of the model for the unbalanced data set. Compared with the prior art, the method has the advantages of wide applicability, high classification accuracy of minority class boundary samples and the like.

The invention belongs to the technical field of meteorological element prediction methods, and particularly relates to a meteorological element class imbalance data processing method based on machine learning. The invention particularly relates to a thunderstorm gale prediction method and system based on a space-time sampling strategy, and the method comprises the steps: obtaining thunderstorm gale observation data and forecast factor data of a target region, carrying out the processing of the obtained observation data and forecast factor data, carrying out the space-time mixed sampling, and obtaining a thunderstorm gale prediction result; the method comprises the steps of space sampling optimization and time sampling optimization: forming a physical mechanism needing 6-12 hours of energy accumulation and water vapor transportation based on thunderstorm gale, designing three time sampling schemes, namely N1, N2 and N3, coupling the three schemes with the determined space optimal proportion, and adopting the sampling data after coupling in the step 3 to obtain the time sampling optimization of the thunderstorm gale. Three machine learning models of MLP, AdaBoost and SVM are subjected to parameter tuning and training, the trained models are utilized to predict thunderstorm and gale in a target area, and an evaluation system is constructed based on a confusion matrix.

The application relates to the technical field of artificial intelligence fault diagnosis, and discloses an alternating current machine fault diagnosis method based on ISBOA-Adaboost. The collected data is pretreated through a VIKOR algorithm, the snake heron optimization algorithm SBOA is improved, a Sinusoidal chaotic mapping is introduced in the SBOA population initialization stage to improve the population initialization uniformity, and the snake heron position is updated according to the current iteration number of the algorithm in the SBOA hunting stage; an adaptive weight factor is introduced in the SBOA escape stage to optimize the snake heron position update, the improved snake heron optimization algorithm ISBOA is used to optimize parameters of an iteration algorithm Adaboost, an ISBOA-Adaboost fault diagnosis model is constructed, and the blindness of parameter selection in the training process is compensated.

The invention belongs to the technical field of medical detection and biological medicine, and discloses a serum protein marker combination for glioma diagnosis and prognosis evaluation and a processing system of the serum protein marker combination. Eight algorithms including KNN, SVM, random forest, XGBoost, AdaBoost, LGBM, Gaussian naive Bayes and decision tree cover traditional machine learning and integrated learning, and model robustness is improved; according to the method, interpretable feature screening is adopted, SHAP and LIME tools are combined, the contribution degree of each marker to a diagnosis result is clarified, and the problem of'black box 'of machine learning is solved; the invention provides a machine learning algorithm-based mass spectrometry system for glioma detection, which is good in detection performance, high in speed, convenient to operate and low in cost, so as to meet the requirements of clinical early diagnosis and non-invasive examination of glioma.

The invention discloses a dual-coding adversarial learning and multi-scale expansion fusion attention image cartoonalization method, and relates to the technical field of image processing, and the method comprises the steps: constructing a generator comprising a dual-encoder structure, and enabling a content feature encoder to extract the structural semantic features of an input real image, a style feature encoder extracts texture style features of the target cartoon image; the style features are injected into the content features through adaptive instance normalization; processing fusion features by adopting a multi-scale expansion fusion attention mechanism, and adaptively enhancing a key channel and a space region; the decoder reconstructs and generates a cartoon image; meanwhile, a double-discriminator framework composed of a global discriminator and a local discriminator is adopted, the overall style consistency and the local detail authenticity of the image are evaluated respectively, and the generation process is optimized through adversarial learning. According to the method, the high-quality cartoon graph can be generated, the color texture is natural, the edge is clear, and details are rich.

This invention relates to the field of temperature field prediction for vehicle transmission components, and discloses a method, system, medium, and device for predicting the temperature field of wet friction components based on a cross-domain transfer learning model. The method includes: acquiring temperature field data and interface morphology data of the friction components, constructing a Transformer-LSTM-AdaBoost hybrid neural network model using heterogeneous physical quantity corresponding sample inputs, and pre-training the model as a feature extractor; constructing a DGDAN cross-domain transfer model, using interface morphology data as the source domain and temperature field data as the target domain, and inputting both into the DGDAN cross-domain transfer model for training, initially extracting common features from the two sets of data through the feature extractor; conducting adversarial training between the feature extractor and the domain discriminator through a gradient inversion layer, explicitly aligning the feature distributions of the two domains using the maximum mean difference metric, and obtaining the final common features; and inferring the full surface temperature field distribution online through the input interface morphology data, achieving dynamic state synchronization between the physical entity and the virtual model.

An embodiment of the present invention discloses a permission hierarchical control method and an operation and maintenance bastion host system based on dynamic desensitization and real-time monitoring. The embodiment of the present invention significantly improves data security and operational compliance in operation and maintenance scenarios by integrating a database firewall and a dual-mode desensitization engine, combining permission hierarchical control and real-time semantic analysis. Dynamic desensitization rewrites query statements in real time according to user roles to limit the exposure of original data; static desensitization pre-generates isolated copies to reduce direct access to production libraries; based on semantic analysis and rule engines, the response time is shortened to within 1 second; the static desensitization library supports direct use in development and testing, reducing the permission application process; and the rule library is dynamically updated through the Attention-GRU-Adaboost model to improve adaptability to new attack patterns.

The invention belongs to the technical field of electric heating load prediction, and particularly relates to an electric heating load prediction method and device, equipment and a medium, and the method comprises the steps: collecting the historical load data and influence factors of electric heating of a user in a region; adopting a maximum correlation minimum redundancy algorithm to select an optimal influence factor set; clustering the users based on the historical load data and the optimal influence factor set to obtain a clustering result; extracting a feature set containing a plurality of feature indexes according to the historical load data, taking the feature set, the historical load data and the optimal influence factor set as input of a corresponding Adaboost-BiLSTM prediction model, selecting a corresponding Adaboost-BiLSTM prediction model according to a clustering result of each user, and outputting an electric heating load prediction value by the corresponding Adaboost-BiLSTM prediction model; according to the method, the adaptive enhancement algorithm and the bidirectional long-short-term memory neural network are combined, different weights are given to a plurality of weak learners, a strong learner is constructed, meanwhile, the time sequence characteristics of the electric heating load data are mined through forward and reverse bidirectional calculation, and finally the load prediction precision is improved.

The invention discloses a network model and method for egg type recognition and a storage medium, and relates to the technical field of agricultural product production, and the network model comprises a self-attention module which is embedded in a convolution block and achieves the adaptive enhancement of key features through calculating the semantic association in a feature sequence; the semantic complementation module is used for realizing semantic complementation of different scale features through bidirectional feature flow from top to bottom and from bottom to top; and the multi-task learning module is used for separating target classification from positioning regression tasks, so that the distinguishing capability of the model on egg types is improved. The method provided by the invention has the beneficial effects of accurate target identification, high accuracy, high recall rate and high automation degree, and can overcome the interference of the surrounding environment on the identification process to a great extent.

The invention relates to the technical field of machine learning, and particularly discloses a disease monitoring platform intelligent detection system based on machine learning, which comprises a signal acquisition and processing module, a signal enhancement and model prediction module and a model output optimization module, the system collects original physiological signal data in real time through a multi-modal sensor, and performs preprocessing and preliminary noise suppression to obtain a high-quality physiological signal data set, thereby providing reliable basic data support for disease monitoring; aiming at the initial weak signal, the system adopts a self-adaptive enhancement strategy to improve the identifiability of the initial weak signal, and performs classification processing according to a signal-to-noise ratio so as to generate an accurate disease prediction result; and on the basis of credibility of a prediction result output by the machine learning model, signal processing and machine learning model identification capabilities are continuously improved. According to the system, weak physiological features are effectively enhanced, the prediction accuracy is improved, and adaptive closed-loop optimization is realized.

This application discloses a method, device, and apparatus for identifying the driving style of vehicles at intersections without signal lights. The method includes: obtaining driving data features of vehicles passing through the intersection without signal lights; using a preset kernel PCA algorithm to perform dimensionality reduction processing on the driving data features to obtain a reduced-dimensionality principal component vector; using a preset GMM model based on the K-means algorithm to perform cluster analysis on the reduced-dimensionality principal component vector to obtain cluster analysis results; training and testing an initial AdaBoost model for driving style identification based on the driving data features and the cluster analysis results to obtain a target AdaBoost model; and using the target AdaBoost model to identify the actual vehicle driving style to obtain a style identification result. This application can solve the technical problems that the existing technology lacks analysis of lane vehicle characteristics at intersections without signal lights, and that the recognition results lack accuracy and reliability due to the relatively simple data features.

The invention provides an AI contract tamper-proofing method based on an OCR + large language model, belongs to the technical field of large language models, and improves image quality by performing multi-scale adaptive enhancement and super-resolution reconstruction on a contract scanning image. A semantic bridging fusion model is adopted to deeply fuse an OCR recognition result and an enhanced image under a sparse coding framework to realize context correction of low-confidence characters, semantic normalization is performed on a corrected text, and a term semantic vector sequence is established; the steady-state distribution field of the semantic concentration is calculated based on the fluid dynamic diffusion equation, the concentration gradient matrix is extracted to serve as the text fingerprint, contract tampering detection with robustness to the OCR recognition error is achieved through fingerprint similarity comparison, and the technical problem that the false alarm rate is high due to the fact that contract tampering detection is sensitive to the OCR recognition error is solved.

The application discloses a multimodal large model feedback emotion enhancement method and device, equipment and a storage medium, which can be applied to the technical field of emotional data processing. The application obtains feedback literacy scores and feedback literacy levels by analyzing and quantifying a plurality of feedback literacy elements, simultaneously performs emotional recognition on multimodal interaction data of the current student to obtain interaction emotional tags, and analyzes feedback history data of the current student to obtain feedback history features. The feedback emotion preference personalized model is inputted with the feedback literacy scores and the interaction emotional tags to obtain feedback emotion preference tags. The multimodal large model is inputted with a feedback content theme to be generated, the feedback literacy level and the interaction emotional tags to perform emotional enhancement and obtain emotional enhancement feedback content. The emotional enhancement feedback content is optimized based on the human-machine symbiosis paradigm, so that the efficiency of the artificial intelligence empowerment student learning process feedback literacy self-adaptive enhancement can be effectively improved.