94 results about "Web learning" patented technology

The invention relates to the technical field of data processing, in particular to a robot control method, device and system based on a diffusion model and a medium, and the method comprises the steps: collecting the position information of the tail end of an index finger and the state information of a flexible linear object in a human hand teaching process, and constructing a training sample; training the training sample by using a diffusion model, and learning mapping from a high noise state to a clear target state through a noise prediction network in the training diffusion model to obtain a trained diffusion model; the training process of the diffusion model comprises forward noise addition and reverse noise reduction; the state of the flexible linear object and the tail end position of the mechanical arm are collected, a current observation environment is constructed, the current observation environment is input into the trained diffusion model, a target action sequence is generated, and the mechanical arm is controlled to execute control actions based on the target action sequence; according to the invention, the accuracy and real-time performance of the robot to control the flexible linear object can be improved.

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 relates to a low-carbon energy-saving operation method and system for an edge node of the Internet of Things, and relates to the technical field of data processing, and the method comprises the steps: constructing a state vector of space-time correlation through dynamic weighted fusion based on the state information of the edge node collected in real time; constructing a comprehensive energy efficiency evaluation index based on the state vector; taking an energy efficiency evaluation index as an optimization target, and generating an action strategy through a deep Q network learning state and action mapping relation; according to the action strategy, dynamically adjusting the operation frequency of the node equipment, the task queue priority and the node working mode; based on the deviation between the actual energy efficiency and the target value, adaptively adjusting the weight and learning rate of each model; and performing dynamic attenuation correction on the execution strategy of each edge node through each updated model to obtain a target strategy, and controlling node equipment to operate in a low-carbon and energy-saving manner based on the target strategy. According to the invention, the low-carbon energy-saving capability and the operation safety of node equipment can be improved.

The embodiment of the invention provides a big data analysis visualization method and device based on a big model, a medium and a product. The method comprises the following steps: receiving a natural language demand input by a user; performing semantic layer, logic layer and task layer decomposition on the natural language demand by adopting a preset hierarchical analysis model to generate an executable data analysis instruction; obtaining target data required by the data analysis instruction; according to the data analysis instruction, the target data, a preset chart recommendation algorithm and an aesthetic evaluation optimization algorithm, generating a basic visual chart; and learning the user preference characteristics through the visual generative adversarial network, dynamically adjusting the basic visual chart according to the user preference characteristics, and generating a personalized visual result corresponding to the natural language demand. According to the scheme, the defects of an existing dragging type visualization system and an existing programming type visualization scheme in the visualization process are overcome.

The invention discloses a black box large language model-oriented knowledge-guided reinforcement learning discrete cue word optimization method, and belongs to the technical field of natural language processing and artificial intelligence optimization. According to the method, a KPE-RL (Known-gued Prompt Evolution with Reforming Learning) algorithm is put forward, editing operation is constrained by using a structured phrase-level knowledge base, cue words are optimized and modeled into a Markov decision process, and a discrete editing strategy is learned through a deep Q network. According to the method, a mixed state coding mode fusing semantic embedding and task statistical characteristics is designed, and a diversity regular reward is designed, so that efficient exploration is encouraged, and the optimization capability of the model in a limited API calling scene is improved. The method is suitable for automatic prompt optimization of the large language model in the API black box scene, and has generalization, robustness and practical application value.

The invention provides a collaborative distillation method and device based on semantic prompt, and belongs to the technical field of industrial defect detection. The method comprises the steps that a coupling teacher collaborative distillation structure composed of a main teacher network and an auxiliary teacher network is constructed, the main teacher network is used for modeling local detail information of an image, the auxiliary teacher network is used for extracting an abstract semantic relation of the image, and the main teacher network and the auxiliary teacher network complement each other and collaboratively guide students to learn representation characteristics with better distinction; the student network receives the feature guidance of the two teacher networks at the same time, and the modeling ability of the student network to a normal mode and the identification ability to an abnormal area are improved. In order to alleviate the problem of insufficient prior knowledge, a hierarchical cross-modal alignment mechanism based on semantic prompt is further introduced, the image is guided to be transited layer by layer from pixel-level details to image-level semantics through predefined text semantic prompt, effective fusion of multi-scale features and semantic information is realized, and effective defect detection is carried out. According to the invention, defects of different sizes and different types can be accurately detected, and the robustness is high.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health, intelligent robots and the like, and discloses a decision-making method, device, equipment and medium based on an environment semantic atlas. Constructing and updating an environment semantic map based on the initial multi-modal fusion feature, fusing the environment semantic map and the initial multi-modal fusion feature to generate a deep fusion feature, determining current action distribution based on the deep fusion feature, and learning transmission mapping through a flow matching network to generate post-mapping action distribution; and generating an action sequence, adjusting the action sequence according to the environment interaction feedback, and updating the flow matching network parameters in combination with the environment interaction feedback and the reward function. According to the method, the environment semantic map and the multi-modal features are fused, the action distribution is optimized and the action sequence is dynamically adjusted in combination with the flow matching network, and the multi-modal decision-making performance and the environment adaptability are improved.

The invention discloses an e-commerce platform product selection and pricing method based on multi-agent reinforcement learning, aiming at the game decision problem between an e-commerce platform and a supplier, a platform agent and a supplier agent are constructed, the platform agent uses a deep Q network DQN to learn a product selection strategy, and the supplier agent uses an Actor-Critic algorithm to learn a pricing strategy. Through Stackelberg master-slave game modeling, a platform is used as a leader to make a decision for selection, and a supplier is used as a follower to make a decision for pricing after observation. A state space (including commodity sales volume, inventory, user score, supplier fulfillment rate and the like), an action space (discrete commodity selection and continuous pricing) and a multi-target reward function (balance profit, user satisfaction, inventory turnover and the like) suitable for an e-commerce scene are designed. The strategies of the two parties are converged to game equilibrium through alternate training, and online continuous learning is supported to adapt to a dynamic market environment. Experiments show that compared with an existing method, the method has the advantages that the overall income can be improved by 18-30%, the win-win situation of the platform and the suppliers is achieved, and the blank of multi-agent reinforcement learning in e-commerce product selection and pricing scenes is filled.

Disclosed herein is the framework for Causal Reinforcement Learning which combines Causal Cooperative Networks with the Actor-Critic algorithm, introducing reverse dynamics, and invertible-reasoning policy within the framework to enable bidirectional transitions while maximizing accumulative rewards. The framework involves a redesigned Critic and Actor, as well as a newly developed Reverse-environment network. During the iterative training and exploration phases, the cooperative network learns a policy that identifies actions capable of reversing a future state back to its prior state, through the reverse-environment network. It allows agents to consider the consequences of their actions, which facilitates deeper decision-making and exploration strategies.

Embodiments of the present application provide a deep learning-based phase unwrapping method and apparatus, a device, and a medium. The method comprises: acquiring a wrapped phase map; and inputting the wrapped phase map into a pre-trained deep learning network model and performing phase unwrapping, such that the deep learning network model outputs, on the basis of a non-linear mapping relationship between wrapped phases and absolute phases, an absolute phase map corresponding to the wrapped phase map, wherein the deep learning network model adopts a U-Net framework and comprises an encoder, a decoder, a deep-supervision feature fusion module, and an atrous spatial pooling pyramid module, and the encoder and the decoder comprise Inception modules. On this basis, even in high-noise environments and in the presence of phase discontinuity, the embodiments of the present application enable a trained deep learning network model to predict a high-quality absolute phase map in just one step, thereby achieving accurate phase unwrapping.

The invention relates to a holographic semantic content efficient transmission method based on hierarchical reinforcement learning, and belongs to the technical field of wireless communication. The method comprises the following steps: S1, establishing a target-oriented holographic content interactive semantic transmission framework; s2, establishing an SSP optimization problem mathematical model aiming at minimizing the total accumulated resource cost; s3, the SSP optimization problem is decomposed into a two-stage hierarchical decision-making structure, and the two-stage hierarchical decision-making structure comprises a high-layer element controller used for strategic semantic planning and a low-layer controller used for tactical resource allocation; and S4, an algorithm based on hierarchical reinforcement learning is designed to solve an optimization problem, a high-layer element controller adopts a deep Q network to learn a long-term semantic sorting strategy, and a low-layer controller executes an instant optimal power distribution strategy. According to the method, the total cumulative cost can be remarkably reduced, and the excellent efficiency of layered semantic transmission in a dynamic wireless environment is proved.

This application provides a big data analytics visualization method, apparatus, medium, and product based on a large model. The method includes: receiving a natural language input from a user; using a preset hierarchical parsing model to decompose the natural language input into semantic, logical, and task layers, generating executable data analysis instructions; acquiring the target data required by the data analysis instructions; generating a basic visualization chart based on the data analysis instructions, target data, a preset chart recommendation algorithm, and an aesthetic evaluation and optimization algorithm; learning user preference features through a visualization generation adversarial network, and dynamically adjusting the basic visualization chart according to the user preference features to generate a personalized visualization result corresponding to the natural language input. This application's solution addresses the shortcomings of existing drag-and-drop visualization systems and existing programmatic visualization solutions in the visualization process.

The application relates to the field of computer vision processing, in particular to a video retrieval method based on a deep neural network model and multi-example learning, which comprises the following steps: obtaining initial features by pre-training a query text, extracting I 3D-RGB features, ROI features and connection features from a video; updating frame-level visual features and word-level text features; constructing a graph for training, learning word-level text features by using a graph attention network; calculating the residual error of the word-level text features and the word-level text features, and taking the mean value of the residual error as a sentence-level text feature; performing segment dimension average operation on the frame-level visual features to obtain pipeline-level visual features; calculating the alignment score of the sentence-level text features and the pipeline-level visual features, constructing positive sample pairs and negative sample pairs, and training a video retrieval network; and the application constructs a graph neural network by acquiring discriminative features in multiple query texts through deep learning features, so as to provide text features with more representation meanings and multi-modal alignment supervision signals under weak supervision settings.

The invention relates to the technical field of computer vision, artificial intelligence and multimedia security, in particular to a 3D field speaking face generation defense method, system and device and a medium, and relates to a reinforcement learning-based anti-interference method and system which can effectively protect a reference video from the source and enable the reference video not to be used for generating a high-quality speaking face video. Modeling a reference video protection problem into a sequence decision process by constructing a reinforcement learning environment; generating an antagonistic interference action by utilizing strategy network learning, designing a dual-objective reward function, and optimizing an interference effect and visual concealment at the same time; and finally, iterative training is carried out through a deep reinforcement learning algorithm, so that the model autonomously masters the optimal interference strategy.

The unsupervised domain self-adaptive semantic segmentation method, system, device and medium based on inter-domain consistency use a multi-scale generative adversarial network based on GAN to translate source domain image data to target domain style while preserving its content; then input the inter-domain consistency training network based on the Transformer model for adaptive training, including sending the source domain image data after data enhancement to the student network for source domain semantic segmentation training and updating the teacher network weight using the EMA strategy; input the target domain unlabeled image data into the teacher network to generate pseudo labels to further guide the student network training; use a hybrid strategy to randomly combine the source domain image data and the target domain image data to guide the student network to learn domain invariant knowledge; finally, the student network is optimized according to the attention feature map consistency, and an unsupervised domain self-adaptive semantic segmentation model is output, which can complete the segmentation of the target domain image data without using the target domain label; the present application can train a model with excellent performance using less real image data set in the field of unmanned driving, greatly reducing the time and money cost of training, and improving the work efficiency.

The application discloses a lightweight adaptive network learning method for multi-modal multi-task learning, comprising the following steps: 1, constructing a downstream task dataset, 2, constructing a deep self-attention network model, 3, pre-training weight pruning, 4, constructing a task adapter, 5, adapting a pre-training model, 6, designing a progressive guided distillation training and training a model. The method prunes part of the weights of the pre-training model and adapts it with an efficient task adapter. The application proposes a progressive guided distillation training algorithm to better fill the gap between the pre-training task and the downstream task, ensuring the performance of the model on the downstream task. The application can be combined with any existing pre-training model based on a deep self-attention network, and through training, an adapter model with superior performance in the downstream task, total storage overhead during model deployment, calculation overhead during model inference and model configuration flexibility is obtained.

This invention relates to a scalable industrial vision task offloading method and scheduling device based on energy consumption awareness, belonging to the field of task scheduling and resource management technology. To address the problems of current task offloading methods failing to simultaneously consider reliability, latency, and energy consumption, and lacking flexibility in adjusting latency and energy consumption, this invention first introduces energy consumption concern weights to establish a hierarchical transmission and computation energy consumption model, and constructs a utility function that comprehensively considers reliability, latency, and energy consumption. Then, task offloading scheduling is modeled as a Markov decision process using deep reinforcement learning, with the utility function as the immediate reward. Through the interaction between the deep reinforcement learning agent and the environment, a policy network is trained to learn the optimal joint decision on execution location and offloading layer under different channel and computing power conditions, thereby maximizing long-term average utility and satisfying latency constraints. Finally, intelligent scheduling is achieved based on deep reinforcement learning.

This invention relates to the field of data processing technology, specifically to a robot manipulation method, device, system, and medium based on a diffusion model. The method includes: collecting the position information of the index finger tip and the state information of a flexible linear object during a human hand teaching process to construct training samples; training the training samples using a diffusion model, and learning the mapping from a high-noise state to a clear target state through a noise prediction network in the training diffusion model to obtain a trained diffusion model; the training process of the diffusion model includes forward noise addition and backward noise reduction; collecting the state of the flexible linear object and the position of the robotic arm end effector to construct the current observation environment, inputting the current observation environment into the trained diffusion model to generate a target action sequence, and controlling the robotic arm to perform manipulation actions based on the target action sequence; this invention can improve the accuracy and real-time performance of robot manipulation of flexible linear objects.

The present invention provides a method for extracting Chinese ancient book entities based on a deep active learning strategy, which relates to the field of natural language processing. In the present invention, the active learning strategy is integrated with an entity extraction model based on a deep neural network, which can not only give full play to the model fitting ability of the deep network learner, but also make full use of the improved active learning strategy to further alleviate the problems of insufficient labeled data and high expert annotation costs. At the same time, a query strategy that combines uncertainty, minimum boundary, and multi-space feature representation is used to select the data pool, effectively improving the learning efficiency. Further, for the entity extraction network model, a dual encoder structure is designed, which integrates a global encoder based on pre-trained learning and a local encoder based on character-level embedding to obtain the fused semantic representation of the sentence, so as to comprehensively capture the more comprehensive context semantic knowledge of the sentence, thereby improving the performance of the entity extraction model based on the deep neural network.

The application discloses a kind of based on information bottleneck's debiasing recommendation method, comprising:1. Construct original data: user-product interaction matrix of user interaction to product, user biased attribute matrix;2. Using biased attribute encoder learns user biased representation in user-product interaction data;3. Based on deep graph neural network learns user and product representation matrix;4. Based on information bottleneck theory minimizes the mutual information between user representation and biased representation, user subgraph representation and biased representation, calculates loss function;5. Recomputes interaction matrix based on user and product representation matrix, calculates loss function;6. The loss function of joint step 4-step 5 is carried out information bottleneck learning, and model parameter is updated to model convergence.The application is based on the thought of information bottleneck, learns unbiased user representation under the demand of meeting recommendation task, so as to effectively reduce recommendation bias, guarantee the accuracy of recommendation.

A method for cooperating packing actions and unpacking actions of mechanical arm based on deep reinforcement learning (DRL), wherein a synergistic effect between a packing action and an unpacking action is learnt by means of a packing-unpacking network (PUN), thereby improving the packing precision and efficiency; and a packing heuristic algorithm and an unpacking heuristic algorithm are provided on the basis of human experience and are combined with the PUN, thereby further improving the packing precision and efficiency. Also provided are a system for cooperating packing actions and unpacking actions of mechanical arm based on DRL, a computer-readable storage medium, and an electronic device.

The application discloses a kind of based on AI model's place name classification method, system, equipment and storage medium, design surveying and mapping technical field, method includes: convergence multi-source professional surveying and mapping place name data, execute data preprocessing, based on the structured place name text feature vector, generate the semantic vector that can be directly identified by general AI big model, input big model extraction place name deep layer semantic structure feature, while extracting the geographical attribute characteristics corresponding to place name;Structural place name field knowledge system library is built;Introduce the basic reasoning framework built by Transformer big language model and traditional deep network learning technology, adopt knowledge enhancement strategy to optimize model, cross-modal reasoning is carried out by fusing place name semantic structure feature and geographical attribute characteristics, preliminarily determine the category of place name;Adopt prompt engineering driven zero sample and few sample learning mode, generate multiple candidate classification results, output final place name classification result after screening verification.

The application discloses a spectrum graph convolution network heterogeneous graph representation learning method based on path collaborative graph enhancement, relates to the technical field of graph neural networks, and comprises the following steps: obtaining to-be-processed heterogeneous graph data, projecting node features of different types into a unified latent feature space by using type-aware linear transformation; calculating structure prior weights; constructing a path collaborative graph, learning path interaction weights by using a graph attention network; constructing a collaborative polynomial spectrum filter; performing spectrum graph convolution based on path collaborative graph enhancement, introducing a learnable positive definite diagonal matrix into the collaborative polynomial spectrum filter, performing weighting and feature transformation on the filtered node features, and obtaining final node representation. The application can learn the importance of meta-paths in a fine-grained manner and capture semantic interaction between meta-paths, solves the problems that existing spectrum heterogeneous graph convolution cannot distinguish the importance of fine-grained paths and lacks semantic collaboration, effectively improves the classification performance of heterogeneous graph nodes, and has a good application prospect.

The application discloses a phishing website detection method and system based on a capsule neural network. Different components of a website URL are segmented, character-level word segmentation, truncation padding and coding are implemented to realize fine-grained feature discrimination of different components, component-level and URL overall features are extracted, the overall space structure is learned by using a capsule network, and normal and phishing URLs are distinguished by a joint classification network. In the method and system, an adversarial training mechanism is introduced, independent adversarial training is performed on multiple embedding layers, the accuracy, F1-Score of the model is improved, and the false positive rate is reduced, so that the robustness and generalization ability of the model are enhanced. Experimental results show that the technology disclosed by the application surpasses the prior art on a million-level sample data set, and effectively improves the recognition performance of phishing URLs.

The present invention discloses a method and system for remote sensing image scene classification that guides deep network learning using a knowledge graph. First, a land cover concept knowledge graph is constructed, and then, through knowledge graph representation learning methods, the remote sensing scene semantic categories in the land cover concept knowledge graph are expressed as semantic vectors, forming a semantic benchmark for remote sensing scene categories. In the knowledge graph-guided deep network training stage, by imposing cross-modal alignment constraints between the semantic vectors of remote sensing scene categories and the shallow visual feature vectors of the deep network, the shallow part of the deep network is guided to more effectively learn the shared features of different categories of remote sensing image scenes. In the deep part of the deep network, the scene category labels are still used as constraints to guide the deep network to learn discriminative features for distinguishing different remote sensing scenes. In the testing stage, the optimized deep network model can achieve high-precision remote sensing image scene classification without relying on any prior knowledge.

This invention discloses a gait recognition method based on sample-adaptive representation. By incorporating meta-knowledge into gait recognition, this method achieves sample adaptation, enabling the model to better perceive a wide range of complex scenarios, such as angles and conditions. The method specifically involves the following steps: acquiring gait data; defining an optimization objective; learning meta-knowledge using a meta-hypernetwork; applying attention to the spatial, temporal, and channel dimensions using meta-knowledge; integrating temporal information using meta-knowledge; and iterative training. This method is suitable for gait recognition in complex scenarios, leveraging meta-knowledge to achieve good results and generalize to diverse internal and external conditions.

1. The name of the design product: wisdom in the screen (TB-CE007). 2. The use of the design product: the product is used for wisdom in the screen, and the main functions are audio and video interaction, Internet television playing, smart home control, home security, office video conference, home network learning, etc. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The present invention provides a semi-supervised image segmentation method based on knowledge distillation, comprising the following steps: obtaining a dataset of raw images to be processed; preprocessing the raw image dataset to obtain labeled data and unlabeled data; constructing a teacher-student network model, using the teacher network to extract and learn features from a small amount of labeled data to generate initial labeled information, which is used as training guidance for the student network; employing a feature distillation method to guide the student network in learning and correcting target feature information using the feature information provided by the teacher network; adaptively training the teacher-student network model using unlabeled data based on a semi-supervised learning strategy to obtain a trained teacher-student network model; and inputting the image to be segmented into the trained teacher-student network model to achieve image segmentation. The present invention is applicable to a variety of image segmentation tasks and provides reliable technical support for fields such as medical image analysis, autonomous driving, and video surveillance.

A Petri net-based task scheduling method for heterogeneous environments relates to the field of collaborative decision-making and resource optimization in edge computing systems. During scheduling, the input tasks and their dependencies are first modeled as directed acyclic graphs (DAGs), and then converted into Petri nets to uniformly represent task states, resource constraints, and device collaboration relationships, in conjunction with device status information. Subsequently, graph attention networks are used to extract PN topological features, and a dynamic transition triggering strategy is learned using a dual-deep Q-network. The task unloading and resource allocation sequences are optimized through a reward function, and the sequence validity is further verified by a sequence detection module. During task execution, tasks are scheduled in real-time according to a reinforcement learning strategy, and the network state and decision model are dynamically updated based on environmental feedback, forming a closed-loop optimization. This invention significantly improves task throughput and resource utilization in heterogeneous edge environments, and is particularly suitable for device-to-device collaborative scenarios with complex task dependencies and dynamically changing resources.

The invention provides a semi-supervised training method based on strategy evaluation network learning and a voice recognition method and device. The semi-supervised training method comprises the following steps: training an initialized RNN-T by adopting labeled voice data to obtain a trained RNN-T model; introducing a strategy evaluation network into the trained RNN-T model, combining the output of an encoder and the output of a prediction network under the same time step into an output pair as a state, taking a text symbol needing to be predicted as an action, and training the strategy evaluation network by adopting labeled voice data to obtain a trained strategy evaluation network; wherein the strategy evaluation network is used for calculating an action value function of each state-action pair under a given strategy function in the speech recognition process, and the strategy function is used for calculating the probability that RNN-T outputs a next text symbol; and according to the trained strategy evaluation network, training the initialized RNN-T by adopting unlabeled voice data to obtain a finally trained RNN-T model.