1377 results about "Global information" patented technology

Provided is a method for training a scene recognition model. The scene recognition model includes a core feature extraction layer, a global information feature extraction layer, an LCS module of at least one level with an attention mechanism, and a fully-connected decision layer. The method includes: acquiring parameters of the core feature extraction layer and the global information feature extraction layer by training based on a first scene label of a sample image and a standard cross-entropy loss; training a weight parameter of the LCS module of each level, based on a loss value acquired by performing a pixel-by-pixel calculation on a feature map output from the LCS module of each level and the first scene label of the sample image; and acquiring a parameter of the fully-connected decision layer by training based on the first scene label of the sample image and the standard cross-entropy loss.

According to the medical image segmentation method and device based on spatial perception and frequency domain information, the precision and robustness of medical image segmentation are effectively improved by combining frequency domain information guidance and a multi-head state spatial perception technology. Frequency domain transformation is performed on a medical image, high-frequency and low-frequency components of the image are separated by using a multi-scale decomposition technology, and low-frequency features are extracted to obtain global information. By introducing a learnable noise filtering mechanism, noise and irrelevant background information in a frequency domain are suppressed, so that the model can be focused on a lesion area more accurately. A multi-head perception visual state space module is designed on a bottleneck layer, lesion features of different scales are captured through a multi-scale adaptive feature fusion mechanism, and the capability of segmenting small-size lesions and complex structures is enhanced. A context focusing attention mechanism is introduced into jump connection, fusion of global information and local details is further enhanced, and the accuracy of a segmentation result is ensured; and finally, recovering a high-resolution segmented image through a decoder.

The invention relates to the technical field of medical image segmentation, in particular to a few-sample medical image segmentation method based on edge perception multi-prototype learning, and the method comprises the steps: inputting support and query images into a feature encoder, and extracting support and query feature maps of different sizes; inputting into a local attention fusion prototype generator to generate a support foreground prototype; processing the support mask through dynamic corrosion operation to generate an inner boundary prototype; generating a multi-foreground local prototype through a multi-layer perceptron; local and global information is optimized through multi-scale feature extraction, and a multi-scale prototype is obtained; fusing to obtain a multi-prototype foreground prototype; dynamic calculation weighting is carried out on the multi-prototype foreground prototype by using a double-stage prototype optimization network, and automatic calibration is carried out; then prediction is carried out through a prototype prediction module, and finally collaborative optimization is carried out through a loss calculation module; the method can effectively solve the problem of edge detail loss involved in the background technology.

The invention relates to the technical field of biomedical engineering, in particular to a multi-mode brain anomaly detection method and system based on machine learning. The method comprises the following steps: acquiring brain medical image data of different modalities, and realizing spatial registration and alignment through a multi-modal registration algorithm based on mutual information; a multi-branch feature extraction model including a convolutional neural network, a converter and a state space model is utilized to perform feature embedding on the original image of each modal; performing frequency decoupling on the features of each mode through adaptive approximate wavelet transform, and decomposing the features into high-frequency detail information and low-frequency global information; a frequency band fusion strategy based on an attention mechanism is implemented on high and low frequency features of different modal images, and fused frequency sub-band features are input into a space-frequency Mama module. Through the adaptive frequency domain decomposition and cross-modal fusion mechanism, the multi-modal brain image information is effectively integrated, and the accuracy and robustness of brain anomaly detection are remarkably improved.

The invention relates to a micro-expression recognition method based on a cross-source double-branch dynamic space-time diagram convolutional network model, and belongs to the technical field of deep learning and pattern recognition. According to the method, a cross-source double-branch twin space-time diagram structure network is provided to mine subtle motion features of a facial structure when expressions change, and domain invariant features are learned through a twin structure. According to the design, facial global information is modeled through a global information and dynamic spatio-temporal feature extraction network, fine motion information of a facial key structure is extracted through an attention enhancement twin spatio-temporal diagram fusion network, facial structure association when expressions occur is fully modeled, and domain adaptation is performed by constructing cross-domain joint loss.

The invention relates to the technical field of industrial defect detection, and discloses an overflowing surface defect detection and identification method and system of a multi-modal feature and prompt mechanism, and the method comprises the steps: collecting image data and point cloud data of the surface of a water turbine runner overflowing surface, and converting the point cloud data into a surface defect two-dimensional image; obtaining a high-resolution defect image based on an image super-resolution reconstruction method of a deep recursive network; inputting a defect image into the generative adversarial network, judging a data source, generating a high-quality defect image through an objective function optimization generative model, and expanding a data set for a defect identification detection model to use; and inputting the point cloud data into the PDE-Net to construct a dynamic adjacency graph, and mapping the point cloud data to a specific defect category after graph convolution and multi-layer feature extraction. The method has the beneficial effects that a mixed attention mechanism is introduced in a multi-modal feature extraction stage, global information and local details in a defect image are fully extracted, and the detection capability of complex defects is improved.

The invention belongs to the technical field of natural language processing and multi-modal information extraction, and particularly relates to a multi-modal named entity recognition method based on semantic alignment and cross-modal graph fusion, which comprises the following steps: S1, acquiring a data sample containing a text sequence and image content; s2, encoding the text and the image into vectors respectively; s3, similarity is calculated through a trainable bilinear function, and optimization is carried out through loss comparison; s4, cross-modal attention is used to enhance association information between modals; s5, determining the proportion of reserved image information through a modal matching module; s6, introducing a gating mechanism to dynamically fuse visual and text features; s7, realizing local and global information complementation by a cross-modal graph fusion model; and S8, inputting the fused representation into the CRF layer to predict the entity type. According to the method, fine semantic alignment can be realized in a weak image-text correlation context, and balance between local entity recognition and global semantic understanding can be achieved.

The invention discloses an aerospace network hierarchical cooperation routing optimization method and system, and relates to the field of aerospace information network communication, and the method comprises the steps: constructing a system model comprising an unmanned plane, satellite nodes and a communication link, defining link attributes, building a routing optimization objective function, introducing a reputation value-based multi-agent cooperation mechanism, and carrying out the routing optimization of the unmanned plane, the satellite nodes and the communication link. A hierarchical reinforcement learning framework is designed, inter-cluster decision and intra-cluster decision are divided, an upper layer selects an inter-cluster path according to global information, a lower layer plans an unmanned aerial vehicle path and defines a state, an action space and a reward function according to local information, an intelligent agent interacts with an environment to collect empirical data, and optimal routing communication is selected in combination with a real-time reputation value after training. Adjustment can be carried out according to network changes; according to the invention, the cooperative reliability is improved through a reputation value mechanism, the decision complexity is reduced through hierarchical learning, the routing performance is optimized, the network robustness, expandability and adaptability are enhanced, and the cooperative communication efficiency of the unmanned aerial vehicle and the satellite and the system stability are improved.

The invention relates to a multi-modal understanding optimization method based on fine-grained feature extraction and global information integration. The method comprises the following steps: segmenting an input image into a plurality of local image blocks, extracting local fine-grained visual features and global visual features, and interacting the local fine-grained visual features and the global visual features based on an attention mechanism to obtain local context features; secondly, performing feature fusion on the local fine-grained visual features to generate fused visual features; mapping the fused visual features to a semantic space which is the same as the text features of the large language model to obtain projected visual features, and dynamically screening through attention weight based on the text features to obtain key visual features; and fusing the key visual features with the text features to generate joint feature representation, and inputting the joint feature representation into a large language model to generate a semantic analysis result. According to the method, global information and dynamic feature selection are introduced, so that the ability of the model to understand multi-modal content in a complex scene can be improved, and the model calculation overhead is reduced.

In order to solve the problems that day lily plants are densely shielded and categories are difficult to distinguish, the invention provides a field dense day lily pixel classification and picking information acquisition method. The method comprises the steps of designing a double-branch decoder fusing local and global information, constructing a progressive stepped feature mining module (PLFM) and a multi-scale hierarchical category enhancement module (PCEN), optimizing hyper-parameters through a PALA algorithm, and constructing an adaptive double-branch loss enhancement and category enhancement network (DLCE-Net) to improve semantic segmentation performance. And designing a picking point positioning algorithm (MW-GCA) of Mini-Window guided angular point analysis based on a segmentation result, screening angular point coordinates through a U-shaped window, obtaining an attitude line segment, and realizing picking point pixel coordinate and angle estimation. The method can reduce the labor loss and accelerate the engineering landing of the automatic crop picking technology.

The invention discloses an underwater target recognition method based on an improved YOLOv8 algorithm, and the method comprises the steps: inputting an underwater image into an improved YOLOv8n model for underwater target detection, and obtaining an output underwater target recognition result. The method has the advantages that the convolution blocks of the P5 layer of the backbone network and the last layer of the neck network adopt DSConv, so that the network complexity is reduced, and the reasoning speed is increased; a fourth C2f module of the backbone network adopts a C2f DiRMB module in which an inverted residual attention mechanism and dual-channel convolution are introduced, so that the capability of capturing key global information of the network is enhanced, training parameters are reduced, and the understanding of a complex scene is improved; and finally, a small target detection head for improving the small target detection capability is additionally arranged in the head network. According to the underwater target identification method, the mAP (at) is 0.5%, the mAP (at) is 0.5-0.95%, and the accuracy and the recall rate are respectively improved by 0.5%, 0.8%, 0.5% and 1.0%.

The invention discloses a railway turnout defect detection method based on an SCG-DETR detection model. Firstly, a C2f-SMP feature extraction module is designed based on SMPConv and C2f modules, local and global information aggregation of features is optimized through continuous convolution and a gating mechanism, and the receptive field and feature extraction capability is enhanced. Secondly, a CGFM module is provided, and by introducing a CAA attention mechanism, weight distribution of a feature map is optimized, and fine-grained extraction and context information fusion are enhanced. And finally, replacing an original loss function with an Inner-GIoU based on an auxiliary frame, and improving the precision of the model while accelerating the convergence of the model. Experimental results show that the score of mAP50 of the improved model reaches 71.0% and is increased by about 4.6%, the detection speed reaches 80.5 FPS, and the requirement for real-time performance in engineering is met.

The invention discloses an ultrasonic mammary gland image segmentation method based on fine-grained guidance, relates to the technical field of image segmentation, and attempts to use a fine-grained guidance attention decoding diffusion model. The defects of weak association of foreground and background semantics, unbalance of global and local features, poor fine-grained feature capture capability and the like in a traditional segmentation method are overcome. The core design is as follows: a clear de-noised image is obtained by using a de-noising diffusion probability model; based on a context cross-attention decoding diffusion network guided by fine granularity and a contained self-adaptive detail guide attention module, image prior information is learned and fused, and semantic correlation between a foreground and a background is enhanced; a context decoding cross attention layer is added, global information of input features and complex correlation between channels are effectively captured, and the image segmentation precision and efficiency are integrally improved. A contrast experiment result on a related data set shows that compared with a similar popular method, the method disclosed by the invention obtains a better image segmentation effect.

The invention provides a personalized federal learning method and system for a shared model, and the method comprises the steps: enabling a server to store and initialize the shared model, a global model and a global class prototype of a client, and enabling the client to initialize a local learning weight vector; the server sends sharing models of other clients to one client, and the client sets a local sharing model as a global model and trains the global model, and uploads the sharing model and a local class prototype to the server; and the server calculates and obtains a global class prototype and a global model according to all the received shared models and local class prototypes. According to the method, the knowledge sharing problem in federal learning is solved, and the performance of a personalized model is improved; the problem of offset in local model training of the client is solved, and the obtained personalized prototype contains more abundant global information than the personalized prototype; while state dependence and communication overhead are reduced, effective fusion of global knowledge and local characteristics is realized so as to improve robustness and adaptability of the model in a complex scene.

The invention discloses a solar wind speed prediction method based on a fusion prediction mode and a collaborative attention mechanism. The method comprises the following specific steps: collecting and processing solar wind speed and related data within a certain time; dividing the data into a plurality of patches through a patterning segmentation method, learning a plurality of modes in prediction information, and guiding mode fusion by using historical information to obtain fused prediction mode information; learning historical information and fused prediction mode information by using an attention mechanism to obtain time sequence feature representation, aggregating global information through down-sampling and the attention mechanism, and learning inter-variable relation feature representation of variable dimensions for the global information by using a collaborative attention mechanism; mapping the relationship feature representation among the variables through a flat layer to obtain a solar wind speed prediction result; and training and optimizing the model. According to the method, prediction mode information can be fully mined so as to realize interaction between historical information and prediction information, meanwhile, the relation strength between variables can be captured, and the prediction precision is improved.

The invention discloses a brain MRI image segmentation method based on an RWKV model, and belongs to the technical field of medical image processing and artificial intelligence crossing. Firstly, an RWKV linear self-attention module is introduced into a U-Net network framework, and association between remote pixels is established with relatively low calculation overhead, so that the recognition precision of a brain tumor area is improved, the reasoning time is effectively controlled, and the practicability of a model is enhanced. Secondly, according to the method, multi-scale coding and a feature fusion mechanism are combined, local and global information is extracted in a combined manner, features are mined from different resolution levels, and adaptive fusion is realized, so that the fine-grained segmentation effect is improved. And finally, in order to enhance the generalization ability and lightweight deployment performance of the model, the network structure is further optimized, and the overall computing resource demand is reduced, so that the method can better adapt to cross-patient MRI data in a complex clinical environment, and has good practical value and popularization potential.

The invention discloses a heterogeneous double-flow fusion method and system for diabetic retinopathy grading. The method comprises the following steps: obtaining an output result of diabetic retinopathy grading by utilizing a heterogeneous double-flow architecture; processing an input fundus image into images with different resolutions; extracting global context features from the low-resolution image by using a lightweight visual Transform model distilled by composite knowledge, and extracting local focus features from the high-resolution image by using a convolutional neural network model; performing interactive fusion on the global context features and the local focus features of the double-branch architecture through a symmetric bidirectional cross attention fusion module to obtain enhanced fusion feature representation; and finally, inputting the fusion features into a classifier, and outputting a severity grading result of the lesion. The method aims at improving the accuracy and robustness of hierarchical diagnosis through deep analysis of global information and local details, and can be applied to the medical fields of clinical computer-aided diagnosis, eye image analysis and the like.

The embodiment of the invention provides a target object detection method and device for unmanned aerial vehicle power inspection, equipment and a medium. The method comprises the following steps: acquiring an original picture to be detected, inputting the original picture into an improved single-stage target detection model YOLOv11, extracting multi-level features from three dimensions of local, global and convolution through a multi-level extraction module according to a feature map extracted from the original picture by a backbone network of the model; and carrying out channel and space attention weighted fusion on the multi-level features by adopting an attention fusion mechanism, processing a fusion result through a model, and outputting a defect detection result. By improving YOLOv11, local details and global information of an image can be extracted from multiple dimensions, and an attention mechanism is adopted to carry out feature fusion on two levels of channel attention and space attention, so that the feature extraction capability of a small target in the image can be improved, the features can be adaptively enhanced, and the adaptive capability to image change can be improved; therefore, the detection precision of the target object on the electric power facility is improved.

In a traffic scene, a traditional target detection algorithm always faces the problems of strong background interference, difficulty in small target detection and the like, and detection precision and robustness are affected. Therefore, the invention provides an improved YOLOv12n vehicle detection method in which an EMA (Empirical Multi-scale Attention) attention mechanism and an SFA (Space Feature Aggregation) attention mechanism are fused. The invention further provides a method for detecting the YOLOv12n vehicle based on the improved YOLOv12n vehicle based on the attention mechanism of the EMA (Empirical Multi-scale Attention) and the attention mechanism of the SFA (Space Feature Aggregation). The SFA module is deployed in a shallow network, key target area expression is enhanced by aggregating spatial features, and background noise interference is suppressed; the EMA module is embedded into a neck network, and the global information capture and multi-scale sensing capabilities are improved by adopting multi-scale convolution, cross-space modeling and feature grouping mechanisms. According to the method, the real-time performance is kept, meanwhile, the detection precision in a complex scene is remarkably improved, and particularly, higher robustness is shown in the aspects of small target recognition and shielding processing.

The invention discloses a flexible job shop scheduling method based on a graph neural network and deep reinforcement learning, and relates to the technical field of flexible job shop scheduling. The method at least comprises the following steps: S1, firstly, carrying out Markov Decision Process (MDP) on a flexible job shop scheduling problem, namely, FJSP, and initializing a scheduling state; and S2, representing a complex relationship between a job and a machine by using a heterogeneity graph, and effectively mapping different entities (the job, the machine, the operation and the like) of the problem and the relationship between the different entities into a graph structure, wherein the different entities (the job, the machine, the operation and the like) of the problem and the relationship between the different entities (the job, the machine, the operation and the like) of the problem are represented by the heterogeneity graph. According to the method, the graph neural network based on the meta-relationships is provided, different graph convolution modes are innovatively adopted for different meta-relationships to extract features, original semantic information is reserved, the global information capturing capability is enhanced, and a reinforcement learning agent is more accurate when making a scheduling decision.

The invention discloses a human body posture estimation method, system and device and a medium, and relates to the technical field of computer vision, and the method comprises the steps: obtaining a to-be-estimated picture containing a human body; the method comprises the following steps of: performing feature extraction and down-sampling on a to-be-estimated picture, performing dimension raising, depth separable convolution operation, channel aggregation operation and dimension reduction on a feature map with down-sampling resolution in an output branch after down-sampling to obtain a local feature map, performing up-sampling reconstruction on the local feature map by adopting dynamic weight interpolation, and fusing an output branch which is not down-sampled to obtain a local feature map; obtaining a first fusion feature; taking the first fusion feature as an initial feature, repeating the step of obtaining the first fusion feature, obtaining a second fusion feature, carrying out fusion to obtain a dual-scale fusion feature, extracting a depth perception feature in the dual-scale fusion feature, carrying out human body posture estimation through the depth perception feature, and obtaining a human body key point heat map. According to the invention, multi-scale and global information is obtained through a lightweight structure, and accurate key point positioning is obtained.

The invention discloses a fan gearbox fault diagnosis method based on a multi-core wavelet denoising network, and belongs to the technical field of fault diagnosis. The method comprises the following steps: 1, collecting vibration signals to construct a data set, dividing a training set, a test set and a verification set, and carrying out normalization processing; 2, multi-scale pulse features are extracted by fusing a wavelet kernel convolution module, adaptive fusion of a time sequence channel attention mechanism is proposed, and redundant features are suppressed; a third step of integrating a multi-scale adaptive soft threshold module to realize accurate denoising and processing multi-scale information; 4, a feature-driven Transform encoder is adopted, and global information is captured by using a multi-head attention mechanism; and 5, inputting the extracted features into a classifier for classification, performing fine adjustment on the model by using a verification set, performing test set evaluation, and completing fault diagnosis. The method effectively solves the problems of low accuracy and low diagnosis precision caused by the fact that fault features are easily covered by noise and the long-term dependency relationship is difficult to capture in the fault diagnosis of the gearbox of the wind turbine generator.

The invention relates to the technical field of urban three-dimensional modeling, in particular to an urban scene three-dimensional modeling method and system, and the method comprises the steps: recognizing a data missing region in an initial three-dimensional model; according to the space coordinate set of the data missing area and unmanned aerial vehicle flight constraint conditions, generating an initial unmanned aerial vehicle blind compensation path through a multi-objective optimization algorithm; collecting blind compensation data of the data missing region in real time, and calculating the global information entropy of the currently collected blind compensation data; when it is judged that the global information entropy reaches a preset information entropy threshold value, injecting the currently collected blind compensation data into the initial three-dimensional model for incremental updating; when it is judged that the global information entropy does not reach a preset information entropy threshold value, the mutability characteristic of the global information entropy is analyzed, and a subsequent unmanned aerial vehicle blind compensation path and a parameter collection instruction are dynamically adjusted according to the mutability characteristic. The problem that the modeling result is uncertain due to the fact that quality defects exist in collected data in urban three-dimensional modeling is solved.

A method and an apparatus for separating speeches based on local and global modeling network (LAGNet) are provided. A speech separation apparatus includes an encoder configured to progressively compress a sequence of mixed speeches by using a one-dimensional convolution-based local block to generate local information, a bottleneck configured to generate global information by using a multiple self attention-based global block, and a decoder configured to progressively reconstruct the sequence by using the local block. A speech separation apparatus also utilizes a skip connection that includes gates configured to filter the local information by using the global information.

The invention relates to a Markov game-based satellite cluster observation resource allocation method and system. Through a two-stage decomposition strategy, a multi-target balance problem of a task integrity rate, a resource utilization rate and a cost-efficiency ratio is effectively solved. In the first stage, a genetic algorithm (GA) is adopted to complete satellite-task-time window three-dimensional matching, and optimal distribution of limited visible windows is achieved. And in the second stage, a distributed decision-making mechanism is implemented based on an improved Improved-MADDPG framework, and after an intelligent agent adopts a random game strategy to execute exploration in a distributed manner, optimal dynamic configuration of observation resources is achieved through global information sharing and local strategy iteration, and key calculation indexes such as an average reward value and the like are remarkably improved.

The invention relates to a speech emotion recognition method and system based on multi-scale adaptive feature fusion. A speech signal is acquired and preprocessed to obtain a Mel-frequency cepstrum coefficient; time features and frequency features of Mel-frequency cepstrum coefficients are extracted and fused to obtain multi-scale features, the multi-scale features are divided into a global information estimation branch and an efficient self-attention branch through channel expansion, the efficient self-attention branch extracts fine-grained local features through self-attention, the global information estimation branch extracts low-frequency content through downsampling, and the high-efficiency self-attention branch extracts low-frequency content through down-sampling. Non-local information is captured in combination with global variance modulation; by fusing the global features and the local features obtained by the two branches, the relevance between different features is mined, deep feature representation is obtained, and fused deep time-frequency features are obtained; and classifying the fused deep-layer time-frequency features by using a full-connection network, and determining an emotion category corresponding to the voice signal.

The invention particularly relates to a ship multi-modal image fusion recognition method based on graph neural structure alignment, and the method comprises the following steps: 1, obtaining a ship multi-modal image, and constructing a backbone neural network to extract the features of the multi-modal image; 2, constructing graph nodes of the graph neural network, and generating an adjacent edge relationship; 3, for graph structures constructed in different modes, adopting a two-level graph attention mechanism to complete structure alignment; step 4, utilizing an optimal transmission mechanism to realize structure alignment between the infrared and visible light modal diagrams; step 5, feature re-injection is carried out to fuse space coordinates and global information, and the positioning and expression ability of node features is improved; and step 6, training the constructed ship multi-modal image fusion recognition network by adopting local feature alignment loss, graph-level semantic consistency loss and classification supervision loss. According to the method, the problem of alignment errors caused by inconsistency of infrared and visible light modal images is solved, the structure and semantic information of the infrared and optical images are fully fused, the accuracy and robustness of cross-modal target recognition are effectively improved, and the method is suitable for complex scenes such as multi-modal ship recognition.

The invention relates to an oncogene prediction method based on graph variation self-coding, and belongs to the field of bioinformatics. The method is based on a dual-path neural network framework: a main path processes an original network and features enhanced by a variational auto-encoder (VAE) by using a graph attention network (GAT) so as to capture a complex relationship between nodes; the auxiliary path generates an auxiliary network and features containing global information through an APPNP algorithm, and the auxiliary network and features are aggregated through GraphSAGE to retain structural information. The model introduces jump connection and residual connection to relieve gradient disappearance and enhance feature complementarity. And finally, integrating dual-path information output prediction through a linear layer. The method is verified on a plurality of biological network data sets, the prediction accuracy, robustness and hidden relation recognition capability are remarkably improved, and a reliable tool is provided for cancer research.

The invention relates to a federal location recommendation method based on heterogeneous graph semantics, which comprises the following steps: constructing a corresponding local heterogeneous graph by using a local private data set, learning POI node features in the local heterogeneous graph by using a heterogeneous neural network, and learning meta-path semantic features through an attention mechanism. Fusing the track sequence features and the corresponding meta-path semantic features to train a local model; and fusing the parameters of the local model obtained after the training into global information through a knowledge distillation method. And encrypting and uploading the local model parameter, the local data volume parameter and the training result after the knowledge distillation of this round to a server. And after the server receives the uploads of all the clients, global aggregation is carried out, and global model parameters are updated by using the aggregated parameters, so that one-time training is completed. And for a new user, inputting the historical trajectory data of the new user into the optimal local model on the corresponding local client, and outputting the historical trajectory data as the next point of interest recommended for the new user.

The invention discloses a graph neural network scheduling method and system for aluminum rolling multi-process production scheduling, and belongs to the technical field of production scheduling, and the method comprises the steps: constructing an aluminum rolling process dynamic graph model based on a constructed aluminum rolling production line digital twin model in combination with a production process logic relation and constraint conditions; dynamic graph features are extracted by using a graph neural network, a state vector fusing local and global information is generated, and an initial scheduling scheme is generated in combination with a multi-objective optimization algorithm; when disturbance occurs in a production line, sensing and quantifying the influence in real time through a digital twinborn model, updating a dynamic graph model and optimizing an initial scheme by adopting an incremental graph neural network, and verifying feasibility in a digital twinborn environment; issuing the optimization scheme to a physical production line for execution, and dynamically calibrating the digital twin model and the process dynamic graph model through a feedback mechanism; dynamic intelligent scheduling of aluminum rolling multi-process production is achieved, and the production efficiency and the anti-disturbance capacity are improved.