70 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 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 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.

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.

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 an attack recognition method and system of an operating system, computer equipment and a medium, and belongs to the technical field of network security, the method comprises the following steps: collecting system logs from the operating system, an application program and network equipment, after preprocessing, generating a log text high-dimensional semantic vector by using a pre-training unsupervised semantic coding model, and storing the log text high-dimensional semantic vector in a database; the features are reduced into low-dimensional features through an auto-encoder; mapping system entities into nodes, mapping log interaction relationships into directed edges, and constructing a system traceability graph in combination with low-dimensional features; then, node high-dimensional representation is learned by using an enhanced graph attention network (EGAT), a bidirectional gating loop unit is input after time sequence serialization to capture behavior time sequence dependence, and a node anomaly classification probability is output; and finally, the federated server generates a global model to realize multi-system collaborative detection under privacy protection. According to the method, the complex attack chain can be accurately identified, the detection precision and the data privacy are considered, and the real-time intrusion detection and security response requirements of a large-scale network environment are met.

The invention discloses a method and a system for autonomously configuring a special network learning area, and belongs to the technical field of online education. The method comprises the following steps: acquiring user semantic description information and analyzing a target demand, preliminarily screening candidate components and resources, mining user dynamic interest preference characteristics in combination with a user-component-resource ternary interaction graph and a dynamic interest model constructed based on historical behavior data, and fusing to generate a personalized component and content recommendation scheme. According to the method and the device, the page layout is subjected to iterative optimization by adopting a multi-objective optimization algorithm, and finally, the customized learning special area page is dynamically generated, so that the problems of insufficient configuration reusability, insufficient personalized adaptation capability and high component expansion cost in the prior art are solved, and the configuration efficiency, the personalized level and the user experience of the network learning special area are improved.

This invention discloses an information retrieval optimization method based on few-sample knowledge graph completion. It constructs a few-sample knowledge graph completion model, GAKDN, and utilizes a gated and role-aware neighbor aggregator to effectively filter neighbor noise information, reducing the impact of noise on entity embedding learning. A role recognition network learns deep associations between entities and their neighbors, identifying the roles of entities under different few-sample relationships. Knowledge distillation extracts structural information between entity pairs corresponding to few-sample relationships, alleviating the problem of insufficient sample representation. An adaptive matching processor calculates the scores of the positive and negative query sets and the support set, finding the most suitable entity for completion based on the highest score. This invention solves the problems of noisy neighborhoods, multiple entity roles, and insufficient sample feature learning in few-sample scenarios, thereby more accurately predicting the tail entity to be completed, improving the accuracy of knowledge graph completion in few-sample scenarios, and optimizing the information retrieval capability of knowledge graphs.

The application discloses a fair personalized recommendation method and device based on mutual information decoupling and a storage medium, and steps of the method comprise the following steps: 1. constructing original data, including a user-product rating matrix and a user sensitive attribute matrix; 2. constructing a sensitive embedding network to learn sensitive embedding of a user and a product, including a biased one-hot encoding layer, a sensitive information encoder and a sensitive attribute prediction layer; 3. constructing a hybrid embedding network to learn hybrid embedding of the user and the product, including a hybrid one-hot encoding layer, a hybrid information encoder and a preference prediction layer; and 4. constructing a non-sensitive embedding network to learn non-sensitive embedding of the user and the product, including an unbiased one-hot encoding layer, a mutual information lower bound optimization layer and a mutual information upper bound optimization layer. Through the double mutual information fairness constraints on the embedding vectors, the application improves the fairness of the recommendation system while ensuring the recommendation accuracy.

The present disclosure provides a spatio-temporal dynamic perception point recommendation method, system and storage medium, data processing field. The method comprises: constructing a heterogeneous graph group according to each first check-in record in a plurality of user trajectories, and dividing by time slot, each heterogeneous graph in the heterogeneous graph group comprising a user node, a point of interest node, a region node, a category node, and an edge relationship between the nodes; learning an embedding vector representation of each node in each heterogeneous graph by using a relational graph convolution network; and training a dynamic information aggregation model based on the embedding vector representation of each node, each second check-in record in a target user trajectory, and a label value of each candidate point of interest, so as to recommend a point of interest to the target user by using the trained dynamic information aggregation model. The present disclosure realizes the capture of the dynamic behavior intention of the user in the location-based social network and the dynamic activity connotation of the POI, and improves the accuracy of the point of interest recommendation.

The invention relates to the field of artificial intelligence and point cloud segmentation, and provides a segmentation method and system for disaster scene point cloud data, and the method can effectively promote the network to improve the discrimination capability of new objects through carrying out the data resampling of new objects in a secondary subset in the network continuous learning of students. The teacher network prediction result is combined with the new-class original real label to generate a pseudo label, and training loss is obtained based on the difference between the generated pseudo label and the student network prediction result; point cloud features of a teacher network and a student network are subjected to synchronous layered pooling, and distillation loss is obtained based on the difference between the pooling features of the teacher network and the student network, so that the network does not need to construct total loss based on an old class, and the problem that a computer memory is greatly increased due to old class continuous storage is solved; and meanwhile, the trained teacher network on the main subset is utilized to guide the students to learn on the network, so that the post-disaster objects in the new class can be quickly identified.