2040 results about "Learning network" patented technology

The invention relates to a multi-protocol transmission text data monitoring and warning method and system, and the method comprises the steps: generating a multi-source protocol transmission instance based on dynamic authorization and hardware security verification, and collecting and analyzing text data; a network connection state, a data backlog amount and sensor numerical value content parameters are monitored in real time through multiple threads, and a transmission state and content exception event queue is generated; learning a causal relationship among network congestion, equipment faults and alarm events by using a Bayesian network algorithm, calculating a root cause probability in combination with a dynamic weight distribution strategy, and generating a comprehensive alarm list of priority ranking; on the basis of user feedback data, protocol weights and alarm strategies are adaptively updated, abnormal early warning triggering, data snapshot binding and closed-loop optimization of alarm logs are achieved, and the problems that in a multi-protocol mixed transmission scene, safety adaptability is poor, the monitoring dimension is single, root cause analysis depends on static rules, and strategy updating lags are solved. And the real-time performance, the accuracy and the self-adaptability of data transmission of the industrial Internet of Things are improved.

The invention discloses a reinforcement learning unmanned forklift truck obstacle avoidance scheduling method and system for a dynamic obstacle, relates to the technical field of unmanned driving, and discloses the reinforcement learning unmanned forklift truck obstacle avoidance scheduling method for the dynamic obstacle. According to the method, the system and the system, the system and the system, global path planning and local obstacle avoidance decision making are carried out in combination with the reinforcement learning network, the problems of obstacle avoidance response delay and unreasonable path planning in a dynamic environment are solved, the obstacle avoidance response speed, the path planning rationality and the multi-modal data fusion precision in the dynamic environment are improved, and then the safety and efficiency of unmanned forklift dispatching are improved.

The invention relates to a drug-disease relationship prediction method and system based on a dual-channel fusion knowledge graph. The method comprises the following steps: S1, constructing a biomedical fusion knowledge graph; s2, constructing a drug-disease sub-graph based on the biomedicine fusion knowledge graph; s3, constructing a dual-channel adaptive fusion feature module, and embedding a drug-disease sub-graph; s4, performing enhanced splicing on the drug-disease sub-graphs in the dual-channel fusion knowledge graph pre-embedded network, importing an edge-node iterative updating learning mechanism, training a spliced sub-graph relation perception learning network, and obtaining enhanced sub-graph feature embedding; and S5, utilizing the trained enhanced subgraph features to embed, calculate and output the prediction probability of the drug-disease relationship. According to the method, the semantic representation capability of the knowledge graph and the topological modeling advantages of the graph neural network are integrated, and efficient and accurate prediction of the drug-disease relationship is realized through multi-modal feature interaction, sub-graph enhancement and a dynamic sub-graph learning mechanism.

The invention provides a deep learning-based avalanche early warning model construction method and system, and the method comprises the steps: firstly obtaining multi-source environment monitoring data, including meteorological time sequence, topographic space and accumulated snow layer physical data, of a target region, carrying out the time dimension alignment of the meteorological time sequence data to generate a feature sequence, carrying out the meshing of the topographic space data to generate a feature set, and carrying out the construction of an avalanche early warning model; the method comprises the following steps: extracting parameters from accumulated snow layer physical data to generate a state vector, inputting a deep learning network model containing time sequence attention, spatial convolution and cross-modal interaction units, generating a fusion feature vector, constructing a training set based on historical avalanche event annotation data, performing dynamic weight optimization on the fusion feature vector, and generating an avalanche risk prediction model. And finally, receiving current monitoring data in real time, outputting a risk level and an early warning trigger threshold value by the avalanche risk prediction model, and generating a multi-level early warning signal when a real-time risk value exceeds the threshold value, thereby realizing accurate avalanche early warning.

The invention relates to the technical field of power station operation and maintenance, and discloses a power generation equipment intelligent operation and maintenance method and system based on model autonomous selection, and the method comprises the steps: obtaining the multi-mode operation and maintenance data of a photovoltaic power station, and generating a multi-mode operation and maintenance data set; inputting each modal data of the multi-modal operation and maintenance data set into a corresponding module for feature extraction; inputting the extracted feature vectors into a contrast learning network for cross-modal alignment, and outputting an executable decision result by combining a retrieval enhancement generation technology with a knowledge graph; and constructing a privacy protection training framework through federated learning, inputting an executable decision result into a digital twin system for strategy verification, and generating a trained multi-modal large model for the photovoltaic power station to select a corresponding module in the trained multi-modal large model based on the feature data for real-time monitoring. According to the invention, the problems of poor accuracy and untimely reaction in the traditional operation and maintenance process of the photovoltaic power station are solved, and the operation and maintenance of the power station can be carried out timely and accurately.

The invention discloses a financial network security defense method and system based on multiple Agents and a dynamic large model. A detection Agent is deployed in an edge layer, financial network node flow data and system logs are collected in real time, time sequence features are extracted through a lightweight convolutional network, and a preliminary anomaly score is generated. And the cloud layer constructs a decision Agent, receives the feature abstract transmitted by the edge node in an encrypted manner, inputs the feature abstract into a dynamic large model for multi-modal feature fusion, and outputs defense action probability distribution. And the intelligence Agent constructs a cross-institution federated learning network. And constructing a dynamic game engine, constructing a revenue matrix based on the attack cost and the defense revenue, solving a Nash equilibrium strategy, and generating an optimal defense instruction set. And dynamically allocating detection tasks according to the threat level and the edge computing power state. According to the method, efficient acquisition and analysis are realized, the abnormal behavior recognition capability is improved, support is provided for making a defense strategy, the defense strategy is optimized, and the intelligent, automatic and efficient levels of defense are improved.

The invention discloses an amphibious unmanned aerial vehicle riverway sediment concentration real-time monitoring method based on underwater light field imaging and cross-modal fusion, and relates to the technical field of riverway sediment monitoring. Comprising the following steps: acquiring aerial multispectral data and preprocessing the aerial multispectral data; an integrated polarized light field camera is carried on an amphibious unmanned aerial vehicle to capture underwater light field data, and underwater three-dimensional light field information is reconstructed and sediment scattering characteristics are extracted through an optical model and a lightweight deep learning network; the air multispectral data and sediment scattering characteristics are fused through space-time alignment and an attention mechanism, a sediment concentration prediction model is generated, and then a spatial distribution diagram of the river sediment concentration is output; and carrying out real-time monitoring and early warning on the river sediment concentration according to the spatial distribution diagram of the river sediment concentration through edge calculation and cloud collaboration architecture. The method is combined with an advanced deep learning algorithm, high-resolution silt concentration inversion can be realized, and the method has real-time performance and adapts to a complex water body environment.

The invention discloses a short-time rainfall prediction method based on radar image and reanalysis data fusion, and the method comprises the following steps: collecting radar images and reanalysis data at continuous times, and generating input data in a unified grid format through spatial interpolation, time alignment and standardization processing; respectively extracting spatial and temporal features of the radar image and the reanalysis data by using a dual-channel encoder, and carrying out weighted fusion through a channel attention mechanism to generate a fusion feature tensor; inputting the fusion features into a ConvLSTM (Convolutional Long Short-Term Memory Neural Network), modeling a spatio-temporal evolution process of a rainfall system, and outputting a preliminary rainfall prediction image in 0-3 hours in the future; constructing a residual learning network, and performing deviation correction on the preliminary prediction result based on historical residual and observation information; when the radar image input is missing, the completeness of the input structure is maintained through the replacement feature generation module; generating a rainfall intensity image or a probability graph in 0-3 hours in the future; the method supports visual output.

The invention discloses a medical image super-resolution reconstruction method based on multi-level attention guidance, and the method comprises the following steps: S10, constructing a deep learning network model based on a generative adversarial network architecture, which comprises a generator and a discriminator; the generator is based on an improved U-Net architecture, a hierarchical attention module and a dual-path feature processing module are configured in an encoder and a decoder of the generator, the hierarchical attention module adopts different attention strategies according to network levels to consider structure and texture, and the dual-path feature processing module separates and processes low-frequency and high-frequency information; the generator further comprises a multi-level feature fusion module for integrating the multi-scale features of the decoder, and an attention guide up-sampling module for final enhancement and dimension raising. The discriminator adopts a spectrum normalization U-Net architecture and uses multi-scale features for matching; s20, training the network model by adopting a composite loss function comprising pixels, adversarial, perception and total variation loss; and S30, inputting the low-resolution image into the trained model, and outputting a high-resolution image. According to the method, through deep fusion of multi-level attention and multi-scale feature processing, the image restoration quality can be remarkably improved, the texture detail definition can be enhanced, the anatomical structure accuracy can be ensured, and the noise robustness can be improved.

The invention discloses a visual servo double-arm robot migration simulation learning method from simulation to reality, which comprises the following steps: S110, constructing a virtual simulation scene according to a real task scene, and establishing a relation between the virtual simulation scene and the real task scene; s120, the collected teaching mechanical arm and executing mechanical arm operation data are replayed and optimized in the virtual simulation scene, so that a data set used for final training is constructed; s130, a deep imitation learning network is designed and achieved, input of the deep imitation learning network comprises visual data obtained in the real task scene and state data of all joint motors of the teaching mechanical arm, and output of the deep imitation learning network is predicted states of all joint motors of the execution mechanical arm at the next moment; and S140, migrating the fully trained and converged deep imitation learning network from a virtual simulation scene to a double-arm robot in a real task scene. According to the invention, the exploration efficiency and generalization ability of the two-arm robot learning network are improved.

The invention relates to the technical field of autonomous navigation and robot environment perception, and discloses a multi-source sensor fused adaptive navigation system, which comprises a multi-source sensor space-time synchronization module, a quantum particle filtering positioning estimation module, a space-time element learning controller module, a cross-modal quantum fusion module and an adaptive navigation control module. Multi-source data space-time alignment is realized through Lie group SE (3) calibration and dynamic time warping; the positioning robustness of particle filtering is improved based on quantum state coding and annealing optimization; dynamically distributing a fusion weight and injecting a physical constraint by utilizing a meta-learning network; feature level fusion of laser radar, vision and inertial data is realized by means of a quantum entanglement mechanism; and constructing closed-loop adaptive navigation by combining model predictive control and quantum purity trigger feedback. According to the method, the navigation reliability problem caused by misalignment of multi-modal sensor data fusion, divergence of state estimation and insufficient cross-modal relevance in a dynamic environment is solved.

The invention discloses a federated learning-driven cross-domain supply chain elastic inventory optimization system and a method thereof, and aims at realizing inventory data collaboration among same-level enterprises or regional nodes through transverse federated learning and ensuring data security by adopting a self-adaptive differential privacy protection mechanism. The method comprises the steps of constructing a transverse federated learning network, locally performing data preprocessing, adding differential privacy noise, iteratively training a global model based on federated deep reinforcement learning, generating a transverse inventory allocation and replenishment decision, and performing model adaptive adjustment in real time based on key performance indicators. According to the method, multi-target balance is considered, inventory configuration is dynamically optimized through a multi-target reward function, the inventory turnover rate is remarkably increased, the inventory holding cost is reduced, the service level is improved, and the method is suitable for various scenes such as retail chain, manufacturing industry distributed storage and cross-regional logistics distribution; and global optimal inventory configuration is realized on the premise of ensuring data privacy.

A computer system for adaptive operation of deep learning networks through hierarchical supervision, meta-level pattern tracking, cross-network signal coordination, and selective activation prioritization. The system operates a layered neural network monitored by a hierarchical supervisory system that collects activation data, identifies operational patterns, implements architectural modifications, detects network sparsity, coordinates pruning decisions, and manages resource redistribution. A meta-supervisory system tracks supervisory behavior, stores successful pruning and modification patterns, and extracts generalizable optimization principles. The system manages signal transmission pathways that enable direct communication between non-adjacent network regions, with signal modification and temporal coordination. A greedy neural system selectively processes activation patterns based on utility metrics and includes a competitive bidding manager to allocate limited computational resources to high-value signals. This architecture enables real-time optimization of network behavior and resource usage while maintaining operational stability and responsiveness across diverse applications.

The invention discloses a physical information neural network-driven ocean island typhoon scene generation method and system. The method comprises the steps of collecting multi-source heterogeneous meteorological data and performing space-time alignment preprocessing; constructing a coarse-scale space-time probability prediction model, capturing space correlation of meteorological elements by using a graph topology learning network, efficiently processing long-time-sequence dependence of typhoon evolution by integrating a state space model with linear complexity, and generating a probabilistic typhoon scene with coarse resolution through a multivariable joint distribution probability model; further constructing a physical downscaling model, taking a coarse-scale prediction result as condition input, and performing physical consistency downscaling on a coarse-scale scene by embedding an atmospheric fluid mechanics equation in a loss function as a physical hard constraint; and finally, outputting a high-resolution typhoon scene with probability reliability and physical authenticity.

The invention relates to the technical field of equipment fault diagnosis, in particular to an intelligent equipment fault diagnosis system based on a knowledge graph and deep learning, which comprises a data acquisition module, a knowledge graph construction module, a deep learning reasoning module, a diagnosis output module and a feedback optimization module. According to the system, real-time reflection of the equipment operation state is realized through multi-source heterogeneous data acquisition and knowledge graph dynamic modeling, and the fault recognition capability is improved in combination with a double-branch deep learning network and an attention mechanism. A diagnosis result is displayed in a graphical interface, and closed-loop optimization is supported. The device state change can be comprehensively captured, the complex working condition adaptability is enhanced, and the diagnosis accuracy and the intelligent level are improved.

The invention provides a wafer mixed type defect detection method and system based on multi-modal deep learning. The method comprises the following steps: acquiring a multi-modal image of a wafer; wherein the multi-modal image comprises a bright field image, a dark field image and a differential interference comparison image; carrying out normalization processing on the multi-modal image; performing channel splicing on the normalized multi-modal image to obtain a multi-channel input image; detecting defect types, defect number, defect bounding box coordinates, first probability distribution of the defect types and second probability distribution of the defect number of the multi-channel input image based on a double-branch deep learning network; and carrying out weighted fusion on the first probability distribution and the second probability distribution to obtain a defect detection result, so that the defect detection precision can be effectively improved, the omission ratio is remarkably reduced, and meanwhile, the problem that a traditional single-mode detection method is insufficient in recognition capability under a complex background is solved.

The embodiment of the invention provides an artificial intelligence-based engineering cost method and device, and the method comprises the steps: synchronizing the market material price, design drawings and engineering logs of a total engineering project to a federated learning network in real time, obtaining the parameters of a building information model, constructing a dynamically updated engineering digital twinborn body based on the data, and carrying out the construction of the engineering digital twinborn body. Constructing a cost prediction model comprising design institute nodes, construction party nodes and material nodes according to the federated learning network, respectively extracting engineering drawing features, log time sequence data and market material prices to predict the total project price, compressing the cost prediction model into a lightweight model, and constructing the cost prediction model; and receiving real-time data of the engineering digital twinborn body, feeding back the real-time data to the block chain for evidence storage, optimizing the construction mode according to the current engineering amount and material demand, and updating the optimized construction mode to the engineering digital twinborn body. According to the method, the defects in the aspects of accuracy and real-time performance of the engineering cost are effectively overcome, and the accuracy, the real-time performance and the efficiency of the engineering cost are remarkably improved.

The invention discloses a high-low voltage switch cabinet feeder fault positioning method and system based on transient traveling waves, and belongs to the technical field of power system fault detection and positioning, and the method comprises the steps: synchronously collecting electric and acoustic multi-mode signals, and generating a weighted transient synchronization feature matrix; performing time-frequency transformation and feedback optimization on the matrix, and outputting a multi-scale time-frequency feature set; analyzing the feature set by using an integrated learning network, and outputting a layered preliminary positioning result; convergence to an accurate fault section is carried out through iteration calibration; and finally, fusing multi-model calculation and outputting a comprehensive fault positioning report. According to the method, a technical path of combining multi-modal signal fusion and a physical model is adopted, convergence from fuzzy region division to an accurate position can be realized in stages, and the accuracy, the speed and the anti-interference capability of switch cabinet feeder line fault positioning are remarkably improved.

The present disclosure discloses a multi-modal brain network calculation method, apparatus, device, and storage medium. The method is configured to train a brain disease prediction model. After the brain region structural feature and the brain region functional feature are separately extracted from magnetic resonance diffusion tensor imaging data and brain functional magnetic resonance data, a graph representation diffusion learning network is used to separate the universal feature and the unique feature in the brain region structural feature and the brain region functional feature. And then, multi-modal universal and unique feature fusion is implemented based on an alignment algorithm and adaptive weighting technology. Thus, complementary information between the multi-modal data is fully mining. The model can learn an effective feature of a related disease in a training process, and a finally obtained brain region disease prediction model has higher precision and better prediction effect.

The invention provides a speech recognition method and system based on artificial intelligence, and relates to the technical field of speech recognized.The speech recognition method comprises the steps that speech signals are collected in real time, and speech signal features are extracted through a Mel-frequency cepstrum coefficient after the speech signals are subjected to noise reduction; and combining the Mel-frequency cepstral coefficients and the first-order difference and the second-order difference of the Mel-frequency cepstral coefficients to form a speech feature vector. Meanwhile, a lip moving image is collected to serve as a visual signal, after graying processing is conducted on the image, an image feature vector is generated by calculating LBP values of pixel points in the image, the weight of the voice feature vector and the weight of the image feature vector are dynamically adjusted through a cross-modal attention mechanism, a fusion weight matrix is generated, and a fusion image is obtained. Different fusion weight matrixes correspond to different voice instructions, the original voice signals and the original visual images serve as a training set, the voice instructions corresponding to the fusion weight matrixes serve as labels to train a deep learning network model, and finally real-time voice recognition is carried out by inputting data collected in real time into the trained model.

The invention relates to the technical field of electronic manufacturing quality detection, and discloses a multilayer circuit board quality detection method and system, and the method comprises the steps: collecting the surface interference fringe data of a circuit board through a computer-generated holography technology, and reconstructing the three-dimensional position of an alignment mark; internal stress distribution is detected through a photoacoustic coupling system, and a three-dimensional stress tensor field is reconstructed; establishing a PCB deformation dynamic model by using a multi-layer time sequence deep learning network; establishing a stress deformation correlation model through a physical information graph neural network; realizing feed-forward compensation and process optimization according to a dynamic prediction result; according to the method, computer-generated holography, photoacoustic imaging and deep learning technologies are integrated, the registration precision and internal stress distribution of the multilayer circuit board can be detected in a lossless and high-precision mode, the dynamic change trend of the multilayer circuit board can be predicted, active compensation and process optimization are achieved, and the product quality and the manufacturing yield are effectively improved.

The invention relates to the technical field of medical image processing, and discloses a method for intelligently describing liver space-occupying lesion ultrasonic image content by using LLM. A liver ultrasonic image sequence, a patient historical medical record text and a blood biochemical index vector are obtained through a multi-modal data acquisition module, and features are extracted through a cross-modal contrast learning network to generate embedded vectors and align the embedded vectors. And inputting the aligned image embedding vector into a dynamic context sensing decoder, and generating a description text semantic mark sequence by using a layered multi-head attention mechanism. The confidence coefficient is evaluated through an uncertainty calibration module, and the text is optimized through a post-processing reordering mechanism when the confidence coefficient is lower than a threshold value. A real-time interaction optimization mechanism is further arranged, and the model is updated according to feedback of doctors. According to the method, multi-modal data are fused, description accuracy and reliability are improved, text quality is optimized, clinical requirements are met, and liver disease diagnosis is assisted.

The invention discloses a laser point cloud shielded vehicle completion method and device based on a thunder-vision fusion deep learning framework in the technical field of automatic driving environment perception. The method comprises the following steps: acquiring original laser point cloud data and an aerial image; constructing a fan-shaped shielding area based on the original laser point cloud data; identifying two-dimensional bounding boxes, orientations and category labels of all vehicle targets in the image; inputting the fan-shaped occlusion area and the two-dimensional bounding boxes, orientation and category labels of all the vehicle targets into a pre-trained double-branch deep learning network, and predicting whether an occluded vehicle exists or not and the position, orientation and category information of the occluded vehicle; and selecting a vehicle point cloud template based on the category information of the shielded vehicle, and then generating a scene point cloud after vehicle point cloud completion as a final result of vehicle completion in the shielded region. According to the invention, an image-point cloud space mapping mechanism based on the Leiyu fusion deep learning framework is introduced, so that the capability of automatically identifying and positioning the shielded vehicle is remarkably improved.

The invention provides a medical RFQ accelerator low-level signal phase stability control method and system. The method comprises the following steps: constructing a time-frequency energy spectrum feature vector based on wavelet packet transformation; extracting a second disturbance feature based on a lightweight convolutional neural network and an attention mechanism; constructing a phase dynamic trend prediction module based on a long short-term memory network, and obtaining a first prediction phase error; constructing a phase compensation module based on a residual control network to obtain a second phase compensation amount; and outputting a real-time driving control signal based on the extended Kalman filter. According to the method, the time-frequency energy spectrum feature vector based on wavelet packet transformation is constructed, accurate characterization of the multi-scale disturbance features of the low-level signals is achieved, a medical RFQ accelerator phase dynamic compensation system is established in combination with a deep learning network and an extended Kalman filtering algorithm, the control precision and the anti-interference capability of signal phase stability are remarkably improved, and the method is suitable for popularization and application. The method is suitable for a high-precision medical particle accelerator control system.

The invention discloses a fresh food supply chain full-link traceability and loss early warning method based on a block chain, and the method comprises the steps: 1, carrying out the lightweight collection of full-link data, processing the data, and obtaining a standardized data source, and 2, carrying out the construction of a block chain bottom-layer architecture based on the standardized data source; step 3, establishing a dynamic reputation consensus mechanism, and establishing a trusted environment for data sharing through reputation score driving node collaboration based on a block chain bottom layer architecture; 4, constructing a federated learning network; step 5, risk prediction modeling: performing multi-dimensional risk prediction by utilizing model output of collaborative modeling and combining real-time data; step 6, graded early warning response: based on a risk prediction result, realizing graded automatic response through a grading rule; step 7, dynamic decision optimization; the beneficial effects of the invention are that the dynamic decision optimization function adjusts the strategy of each link of the supply chain according to the early warning result, achieves the intelligent scheduling of logistics, inventory and production, and reduces the loss and cost.

The invention relates to the technical field of engineering cost control, and discloses an engineering cost control method and system based on big data. The method comprises the steps of collecting engineering project full-cycle cost data streams, and generating a standardized cost data set through data cleaning; constructing a dynamic cost feature library, and extracting multi-dimensional features such as time sequence fluctuation, resource allocation discretization and supplier association; inputting the feature library into a cost anomaly detection model constructed by a pre-training deep learning network, outputting a cost deviation index, and triggering a correction instruction if the cost deviation index exceeds a threshold value; matching historical cases to generate an optimization strategy set containing material replacement, construction period adjustment and supplier replacement; and virtual deduction is carried out on the optimization strategy through an engineering digital twin system, and the strategy with the predicted cost curve closest to the target value is screened as a final execution scheme. The method depends on big data and a deep learning technology, full-cycle dynamic management and control of the engineering cost are achieved, and the precision and feasibility of cost control are improved.

The invention discloses an intelligent agent digital image interaction generation method based on multi-modal perception, which comprises the following steps: collecting multi-modal input data of a user, and respectively carrying out preprocessing and feature extraction on the multi-modal input data; inputting to an improved efficient modal cross learning network, and carrying out multi-modal feature fusion processing; constructing a semantic intention map, introducing a time index edge weight and an emotion driving edge weight, and encoding the map by using a structure perception map neural network; a modal style vector is extracted through a cross-modal style contrast learning mechanism, and a personalized style coding vector is generated through a hierarchical nested structure; inputting a personalized regulation and control gating mechanism, and regulating and controlling the middle layer representation in the interaction strategy generation process by adopting a feature channel linear modulation method; inputting the representation vector into a behavior strategy generation module to generate a multi-modal behavior output sequence; and the sequence is output to drive the digital image to perform synchronous response, and natural response generation in the user interaction process is completed.

The invention discloses an urban drainage system multi-target prediction method based on an SWMM and a graph convolutional neural network, and the method comprises the steps: obtaining the actual measurement data of a target urban drainage system pipeline and an inspection well, constructing an SWMM model, and outputting the data; generating a space-time diagram sequence deep learning network GraphSAGE-GRU model on the basis of a graph convolutional neural network GraphSAGE and a gated cycle unit network GRU; and taking the preprocessed data as input, inputting real-time or predicted rainfall data by utilizing the trained model, and synchronously outputting node water head and pipeline load prediction results of the target urban drainage system. According to the method, the problem that the topological structure of an urban drainage system is not considered in a traditional method can be solved, and the hydraulic attributes of the inspection well and the pipeline can be comprehensively output. The problem that a traditional agent model is too black and lacks structural information is solved, and more simulation result output is provided by setting a space-time diagram structure.

The invention discloses a few-sample leather anomaly detection method based on domain confrontation and multi-scale fusion, and mainly solves the problem of few-sample anomaly detection in a current industrial scene. Comprising the steps that a multi-source-domain adaptive network comprehensively considering shape and texture information is established, a dual-encoder-decoder network based on multi-scale and shape-texture information fusion is used as a main network, and a domain adversarial learning network is added. The multi-source-domain adaptive network learns shape features, texture features and comprehensive features of the shape features and the texture features by designing three proxy tasks based on domain adversarial learning, joint optimization is achieved, meanwhile, multi-scale fusion is achieved by means of support of multi-source-domain data, and the multi-scale fusion effect is achieved. And the performance of the model in a target domain anomaly detection task is further enhanced by combining a small amount of target domain data.

The invention discloses a traffic scheduling method and an electronic device, and relates to the technical field of traffic scheduling, and the method comprises the steps: determining the priority weight of a micro-service, and predicting a target traffic according to the historical traffic information of a network device; constructing a graph model according to the topological information of the network equipment and the dependency relationship of the micro-service, and performing embedded learning on nodes in the graph model to generate a state vector representing a network state; the priority weight, the state vector and the target traffic of the micro-service serve as input of a reinforcement learning model, and a traffic scheduling strategy of the network equipment is obtained; performing iterative search according to iterative particles formed by encoding the strategy network parameters of the reinforcement learning model and the feature learning network parameters of the graph model to determine reinforcement learning model parameters; and issuing the traffic scheduling strategy to the network equipment and executing the traffic scheduling strategy so as to solve the technical problem that a traffic scheduling method in related technologies is difficult to adapt to a dynamic and complex network environment and service requirements under a micro-service architecture, and the reliability of traffic scheduling is improved.