80 results about "Interaction nets" patented technology

The invention relates to the technical field of computer mode recognition, and discloses a multi-source heterogeneous data fusion processing and key feature extraction method and system, and the method comprises the steps: achieving the adaptive caching and granularity normalization of streaming data through a dynamic buffering queue and a time alignment window; generating a structured vector of a unified space-time reference by using a structured analysis module; a high-dimensional fusion feature tensor is constructed through two-stage convolutional coding and a cross-source attention interaction network; and a key feature channel is screened based on gradient sensitivity through a differentiable channel pruning module. The system comprises a multi-source data access unit, a dynamic buffer management unit, a time alignment unit, a synchronous resampling unit, a structured analysis unit, a primary fusion coding unit, a cross-source attention interaction unit, a time sequence dependence modeling unit, a feature importance evaluation unit, a key feature screening unit and the like. According to the method, efficient, accurate and low-overhead multi-source heterogeneous data real-time fusion and task-oriented key feature extraction can be realized.

The invention discloses a transport capacity resource integrated intelligent scheduling method and system oriented to multiple service scenes, and relates to the field of intelligent scheduling, and the method comprises the steps: constructing a hierarchical collaborative decision-making architecture comprising a macroscopic strategic layer agent and a microscopic tactical layer optimizer; and inputting the structured feature vector into a hierarchical collaborative decision-making architecture, dynamically distributing proper solution algorithms and parameters for a microscopic tactical layer optimizer according to a real-time scheduling situation by utilizing an online element learning optimizer, and outputting a pre-scheduling scheme. According to the invention, through integrated integration of multi-service scene data, comprehensive utilization of static basic information, real-time transport capacity data and prediction environment data is realized, and the data support capability of scheduling decision is improved. The feature interaction network of the multi-layer perceptron structure can accurately extract core features and provide effective input for scheduling decisions. The hierarchical collaborative decision-making architecture is combined with an online element learning optimizer, so that a solution algorithm and parameters can be dynamically matched, and a better pre-scheduling scheme can be output.

The invention provides a blue-green algae image recognition method and system based on hierarchical self-adaption and domain driving, and the method comprises the steps: enhancing an image through employing an improved dark channel algorithm; constructing a blue-green algae biological attribute text database, and performing synonym replacement and sentence pattern recombination; multi-scale visual features are extracted through a hierarchical adaptive Swin Transform model, and key region characterization is enhanced in combination with dynamic spectrum attention; the text is input into a Bio-ALBERT model, and semantic embedding of field optimization is generated through term mask prediction and attribute relation pre-training; constructing a two-layer heterogeneous graph by using a graph attention interaction network GAIN, calculating a cross-modal association weight through a bidirectional graph attention mechanism, and outputting a cross-modal graph feature; multi-scale cross-modal association is modeled through a hierarchical graph attention fusion mechanism, and a comparison alignment loss optimization model is combined; and high-precision blue-green algae identification is realized. According to the method, through multi-scale perception, domain semantic adaptation and graph structure fusion, the accuracy of blue-green algae detection in a complex environment is improved.

The invention discloses a traffic scene training data generation method and device, electronic equipment and a medium, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining multi-modal traffic data, building a dynamic semantic interaction network according to the multi-modal traffic data, and enabling the dynamic semantic interaction network to comprise a plurality of traffic entities, the dynamic attribute of each traffic entity and the space-time relationship between the traffic entities are determined; in the dynamic semantic interaction network, labeling the target event and a core node of the target event according to a preset event specific sub-graph; performing causal chain backtracking on the labeled target event and the core node of the target event to obtain structured causal chain data corresponding to the target event; and performing question and answer pair generation processing on the structured causal chain data corresponding to the target event to obtain an instruction fine tuning data set for training the traffic large model. Therefore, automatic and high-quality generation of the traffic scene training data is realized, and the logical reasoning ability and interpretability of the model are improved.

The invention relates to the technical field of social network application, in particular to a social media viewpoint evolution simulation method and device based on coupling dynamics, and the method comprises the steps: extracting event topic interaction network data based on social network public information, carrying out the statistics of user historical interaction behaviors, and determining an information transmission network and an internal association network according to the data, the two forms a multi-layer association network; determining a transmission state of forwarding and a text viewpoint baseline for calculating a transmission state and a viewpoint value of a user in the social network data set; and inputting the propagation state and the viewpoint value into a coupling dynamics simulation model, carrying out iterative calculation until the viewpoint value converges so as to obtain a final propagation state and a final viewpoint value of the user, and generating forwarding situation distribution and user viewpoint distribution. Therefore, the problems that errors are generated, public opinion monitoring and early warning and public opinion guide strategy construction are affected and the like due to the fact that a viewpoint evolution model adopts a single-layer propagation structure and coupling modeling is not carried out on propagation and an internal correlation structure in the related technology are solved.

The present application relates to the field of OTN resource matching, and more particularly to an OTN resource intelligent matching method based on artificial intelligence, comprising: inputting the preprocessed service demand, available wavelength resource, real-time bandwidth utilization and support OTU level into a feature extraction model based on a residual multi-layer perception architecture to generate a prediction base feature; inputting the prediction base feature into a feature interaction model based on a compressed interaction network architecture to generate a service comprehensive interaction feature; and inputting the service comprehensive interaction feature into an OTN resource matching model based on an expert gate network architecture to generate a recommended OTU level. Through the hierarchical intelligent model processing of the residual multi-layer perception architecture, the compressed interaction network architecture and the expert gate network architecture, the present application realizes the significant improvement in the accuracy, efficiency, adaptability and network operation value of OTN resource matching.

The invention relates to the technical field of seabed engineering, and discloses a submarine cable construction simulation method under complex seabed geological conditions. The method comprises the following steps: slicing and recombining multi-source investigation time sequence data, and constructing a geologic feature space based on a multi-dimensional time sequence window and time-space synchronization. Through feature fusion and dimension mapping, three-dimensional geological semantic body units with consistent time and space are generated, and attributes of the three-dimensional geological semantic body units and construction process parameters are associated and coded. And a recursive segmentation algorithm is adopted, and layered and nested dynamic construction decision units are automatically divided according to an attribute parameter mutation threshold. On the basis, a simulation agent is initialized for each decision-making unit, and dynamic re-evaluation of attribute parameters is driven through an agent interaction network. According to the method, the fidelity of the simulation model to the space-time evolution characteristics of the complex geological conditions is improved, the construction decision can adaptively respond to the local mutation of the geological parameters, and the simulation accuracy and the engineering practicability are enhanced.

The invention relates to an intelligent logistics POI recommendation method based on semantic knowledge distillation and interpretability. The method comprises the steps of obtaining data for training; according to a feature engineering module, encoding each type of data, inputting obtained user identification features, POI attribute features and user behavior features into a deep interaction network of a teacher model, and calculating a preliminary interaction score between a user and a POI based on node features of user nodes and POI nodes after updating of a graph neural network; a lightweight model is used as a basic framework of a student model, semantic knowledge of a teacher model is inherited through adaptive semantic knowledge distillation, test data is input into the student model inheriting the semantic knowledge, final interaction scores of a user and POIs are output, a plurality of corresponding POIs with the highest final interaction scores are used as recommendation results, an SHAP value is calculated, and a recommendation result is obtained. And the recommendation result is explained. According to the method, efficient recommendation, semantic maintenance and decision transparency can be realized at the same time.

This invention discloses a multi-scale, cross-domain interactive network for image tampering localization. Addressing the problems of existing methods that suppress semantic information to highlight forensic features, leading to the loss of key context and insufficient generalization ability, this invention constructs a multi-scale, cross-spatial, and noise-domain bidirectional interaction mechanism between semantic features and forensic features. Specifically, the network introduces a bidirectional cross-attention and adaptive gating fusion module, enabling high-level semantic information to guide the discovery of low-level tampering artifacts. Simultaneously, low-level forensic inconsistencies correct high-level semantic understanding, forming a closed-loop learning paradigm where semantics and forensic clues mutually reinforce each other. This method does not rely on specific target semantics and can effectively capture general patterns of image consistency violations, thus exhibiting excellent localization accuracy and robust generalization performance in both traditional editing and AI-generated tampering scenarios.

The application discloses a multi-agent distributed situation interaction device and method based on a multi-modal large model, and relates to the technical field of computer model application. The device comprises the following steps: constructing a double-layer distributed interaction network; setting an environment monitoring time window according to a preset feedback adjustment period, collecting the number of node survival, the available communication bandwidth value and the task urgency level as the environment state parameters; inputting the environment state parameters into a pre-constructed interaction strategy adjustment model to output the adaptive local node interaction strategy and the adaptive agent node interaction strategy in the next feedback adjustment period; and inputting the adaptive local node interaction strategy and the adaptive agent node interaction strategy into the double-layer distributed interaction network in the next feedback adjustment period to control the local consensus node and the agent node to respectively perform distributed situation interaction and output a unified task situation cognition benchmark. The application realizes high reliability, low delay and consistency guarantee of the distributed situation interaction of the unmanned cluster in a complex battlefield or dynamic environment.

The invention discloses an intelligent driving data automatic labeling method based on a lightweight model, and relates to the field of data processing, and the method comprises the steps: A1, carrying out the preprocessing of to-be-labeled picture data; the preprocessing comprises the following steps: standardizing the picture data to avoid feature distortion; a2, segmenting the preprocessed picture into a fixed number of image blocks by adopting a lightweight visual encoder, compressing the spatial dimension through a pixel shuffling technology, reserving key visual features, and encoding the image data; a3, text prompt template features are introduced, labeling task requirements are converted into standardized text features, and the standardized text features and encoded image features are aligned to the same dimension space; step A4, constructing a global interaction network by adopting a self-attention mechanism, performing cross-modal global interaction on the image local features, the global features and the text prompt features, and realizing feature deep fusion based on matrix operation; and A5, presetting a structured label template and embedding a model generation constraint condition.

The invention provides an object sorting method and device, a model training method and device, a medium and equipment, and relates to the technical field of artificial intelligence, in particular to the technical field of machine learning and information retrieval. According to the implementation scheme, the method comprises the steps of obtaining a candidate list, a current search word corresponding to the candidate list and a historical behavior sequence; based on the historical behavior sequence and the current search word, utilizing a first interaction network in a sorting model to obtain a first fusion feature; for each candidate object in the candidate list, based on the object information of the candidate object and the first fusion feature, predicting to obtain a prediction score of the candidate object; and sorting the candidate objects in the candidate list based on the predicted score of each candidate object in the candidate list.

This invention belongs to the field of deep learning and relates to a multi-granularity AIGC text detection method and related apparatus. The method includes: mapping the text to be detected to characters using a word segmenter to obtain a text token ID sequence and recording the position of the period token ID; passing the text token ID sequence through an encoder model based on a bidirectional attention structure to obtain a text semantic representation vector and a text pure semantic representation vector; passing the classification tag vector and the text pure semantic representation vector through a text subject detection model to obtain a document-level text AIGC detection result; passing the separation tag vector, the text pure semantic representation vector, and the position of the period token ID through a text sentence discrimination model to obtain a sentence-level text AIGC detection result; and integrating these to obtain the multi-granularity AIGC text detection result of the text to be detected. This invention, from the perspective of semantic feature interaction, fully utilizes global and local information through a downstream network composed of strong sequence networks, activation functions, and cross-attention interaction networks to accurately capture the linguistic features of non-spoken AI, achieving accurate text AIGC recognition.

The application discloses a bidirectional GCN-BERT scientific data classification method based on rotation coding and dynamic gating. Firstly, the original text data is preprocessed, and a classification mark containing global information is obtained by using a pre-training model and added to the beginning of the input text. Secondly, the classification mark is optimized in features by a CorNet neural network, and then position enhancement and global interaction are performed on the mark by a rotation position enhanced multi-layer feature interaction network RP-MLFIN. Then, the mark is transmitted to a bidirectional graph convolutional neural network for feature interaction and graph convolution processing to generate a graph-level representation. Finally, the classification mark after feature optimization and the graph-level representation are transmitted to a classifier respectively to generate BERT prediction and GCN prediction, and the prediction results are fused by an attention mechanism to generate a final classification decision. The application improves the processing capacity of text data with strong context dependence and the stability and robustness of the classification result.

The application provides a cyanobacteria image recognition method and system based on hierarchical adaptation and field driving. In the recognition method, an improved dark channel algorithm is used to enhance the image; a cyanobacteria biological attribute text database is constructed and synonym replacement and sentence restructuring are performed; multi-scale visual features are extracted through a hierarchical adaptive Swin Transformer model, and key region representation is enhanced in combination with dynamic spectrum attention; text is input into a Bio-ALBERT model, and field-optimized semantic embedding is generated through term mask prediction and attribute relationship pre-training; a two-layer heterogeneous graph is constructed using a graph attention interaction network (GAIN), cross-modal correlation weights are calculated through a bidirectional graph attention mechanism, and cross-modal graph features are output; multi-scale cross-modal correlations are modeled through a hierarchical graph attention fusion mechanism, the model is optimized in combination with a contrast alignment loss, and high-precision cyanobacteria recognition is achieved. Through multi-scale perception, field semantic adaptation and graph structure fusion, the application improves the accuracy of cyanobacteria detection in complex environments.

The application relates to the technical field of artificial intelligence, in particular to a sustainable consumption behavior simulation system fusing a large language model and multiple intelligent agents, which comprises an external semantic generation module and an internal behavior evolution module; the external semantic generation module constructs multiple types of large language model intelligent agents, generates semantic intervention information according to state parameters of the internal behavior evolution module, and converts the semantic intervention information into standardized numerical signals through a preset mapping function; the internal behavior evolution module constructs consumer intelligent agents with double-layer attributes, adopts an optimized social influence model process based on an interaction network topology, completes cognitive interaction through social similarity calculation, uncertainty interval verification and nonlinear attitude updating, realizes prediction of cognitive behavior tendency through embedded neural networks, and simultaneously feeds back a group new state to the external module to form a closed loop. The system has both semantic dynamics and behavior simulation reality, and significantly improves the accuracy and practicability of sustainable consumption behavior evolution simulation.

The invention discloses an airborne hydrogen system safety analysis method, system, device and medium, and relates to the technical field of aviation safety, and the method comprises the steps: building a functional resonance confrontation hierarchy model; dividing the whole system through the functional feature description of the airborne hydrogen system of the functional resonance analysis method to obtain the functional resonance influence of the sub-components, and drawing a dynamic interaction network of the functional modules of the sub-components; performing dimension reduction and simplification on the dynamic interaction network, and obtaining a reachable matrix; performing adversarial layer extraction on the obtained data based on a reachable matrix to obtain overall division of a resonance model; and hierarchy importance and structure importance are obtained through topological structure data analysis, and airborne hydrogen system safety analysis is carried out. According to the method, the functional resonance confrontation hierarchy model is established, and a series of technical problems that an analysis method of an airborne hydrogen system in the prior art is greatly influenced by subjective effects of researchers, hierarchy fuzziness occurs in hierarchy division and the like can be solved.

The invention relates to the field of multi-modal sentiment analysis, in particular to a multi-modal data analysis method and system based on artificial intelligence, and the system comprises a multi-modal data collection module, a multi-modal feature extraction module, a multi-modal feature fusion module and a multi-modal sentiment analysis module. According to the method, the overall structure is grasped firstly, then detail correspondence is concerned, meanwhile, local and self-adaptive alignment capacity is provided through a deformable attention mechanism, and the defect that multi-modal data alignment is rigid is overcome; the multi-layer progressive interaction network is constructed, specific and local cross-modal association is captured through the word-level interaction layer, related units are organized into a semantic group through the phrase-level interaction layer, the recognition ability of the system for different modes is improved, bidirectional understanding among different modes is achieved through the sentence-level interaction layer, and the recognition efficiency of the system is improved. The relation type of the multi-modal data is identified through the relation modeling layer, and the problem of interaction shallow defects of multi-modal emotion data analysis is solved.

The invention provides a multi-dimensional physiological parameter prediction method, a storage medium and electronic equipment, and belongs to the technical field of data processing. The method comprises the following steps: acquiring a data stream of a tested user; generating a corresponding high-dimensional input vector based on the data stream, wherein the high-dimensional input vector comprises a recent observation value, data freshness and a data missing indication value at each observation moment, and change trend feature data and parameter linkage feature data corresponding to the recent observation value; inputting the high-dimensional input vector into a pre-constructed asymmetric Shenchang differential interaction network model, and processing the high-dimensional input vector through a variable selection layer, a continuous time evolution encoder, a prototype semantic interaction layer and an asymmetric security gating decoder which are cascaded in the asymmetric Shenchang differential interaction network model; and outputting a preset final predicted value of the multi-dimensional physiological parameter. The physiological parameter prediction accuracy of the tested user can be improved.

The application discloses an airborne hydrogen system safety analysis method, system, equipment and medium, and relates to the technical field of aviation safety, and comprises the following steps: a function resonance confrontation hierarchical model is established; the function resonance influence of a subcomponent is obtained by dividing the overall system through function resonance analysis method of the function feature description of the airborne hydrogen system, and a dynamic interaction network of the function module of the subcomponent is drawn; the dynamic interaction network is simplified by dimension reduction, and a reachable matrix is obtained; the obtained data is subjected to confrontation layer extraction based on the reachable matrix, and overall division of the resonance model is obtained; hierarchical importance and structural importance are obtained through topological structure data analysis, and safety analysis of the airborne hydrogen system is carried out. The application establishes the function resonance confrontation hierarchical model, and can solve a series of technical problems such as that the analysis method of the prior art airborne hydrogen system is greatly influenced by the subjectivity of researchers, and hierarchical blurring occurs in the internal hierarchical division.

The invention relates to a multi-time-step wind power prediction method and system based on a deep space-time network (DSTN), and aims to solve the problem that space and time features cannot be effectively fused in an existing wind power prediction method. According to the method, data are preprocessed through a variational auto-encoder (VAE), spatial features are extracted in combination with a graph attention network (GAT), time features are extracted through a gating loop unit (GRU), and multi-time-scale spatial-temporal feature fusion is achieved through sample convolution and an interactive network (SCINet). And finally outputting a multi-time-step wind power prediction result. Experiments show that the method is obviously superior to existing models (such as Bi-GRU and Autoformer) in the aspect of public data sets, and has higher prediction precision and robustness.

The application relates to the technical field of blockchain anomaly detection, in particular to a blockchain abnormal user detection method and system based on resistance perception. The method comprises the following steps: performing node feature extraction, structure coding and feature fusion based on preprocessed data to obtain a node feature matrix; performing feature enhancement on the node feature matrix by using an effective resistance-based feature enhancement mechanism; performing aggregation on the enhanced features by using a multi-hop aggregation mechanism based on resistance perception to obtain node high-dimensional embedding features; and performing classification and optimization training on the node high-dimensional embedding features and outputting a risk prediction result. The application realizes efficient modeling of a complex on-chain interaction network and accurate detection of abnormal users. Compared with a traditional graph neural network model which only relies on local neighborhood statistics, the application can simultaneously capture local interaction relationships and global structure dependencies, thereby significantly improving the accuracy and robustness of abnormal user identification.

The application relates to a user attribute inference method and device based on hierarchical multi-channel hypergraph modeling, wherein the method comprises the following steps: constructing hierarchical multi-channel attribute hyperedge groups and interaction hyperedge groups of users and items based on network data; constructing hypergraph pairs based on hierarchical multi-channel attribute hyperedge groups and interaction hyperedge groups fusion based on an attention mechanism; constructing a hierarchical multi-channel hypergraph convolution network according to the hypergraph pairs and corresponding hypergraph association matrices, and learning hypergraph embedding; and calculating user attributes according to overall representation and matrix decomposition, and optimizing a collaborative filtering task and a user attribute inference task. Thus, the problems in the related art that the collaborative filtering and the user attribute modeling inference task are not alternately optimized and mutually enhanced, the association in the user-item interaction network with attribute data is complex, it is difficult to accurately model high-order complex association therein, the attribute data and the interaction data are relatively sparse, and the user attribute inference effect is reduced are solved.

The application discloses a kind of based on deep learning electric power load multi-period prediction method and system, it utilizes instance-level reversible distribution adaptation mechanism, and the original tensor is stationary mapping in input end dynamic extraction local statistics, convert time-varying distribution into unified feature space that model is easy to converge.On this basis, design time-frequency double-flow interactive network, and extract local mutation feature in time domain and global periodicity feature in frequency domain in parallel, and compensate the information missing of single perspective through cross-domain gate fusion mechanism.Finally, cooperate with future-oriented generative decoder and distribution recovery module, and the stationary prediction result is reversely mapped back to the original dimension space with physical meaning.Through this closed-loop logic of stationary reasoning first and distribution restoration later, effectively eliminate the negative influence of statistical drift on model generalization, realize high-precision multi-period prediction under the premise of retaining environment-driven physical characteristics.

The application aims to provide a contour detection method based on a biological vision multi-level interaction mechanism, comprising the following steps: constructing a deep neural network structure, the deep neural network structure being specifically as follows: an encoding network, a decoding network; wherein the encoding network comprises a VGG16 network, an LFE module and a multi-level interaction network; the VGG16 network is divided into five stages with a pooling layer as a boundary, and the five stages are respectively a Retina stage, an LGN stage, a V1 stage, a V2 stage and a V4 stage; an original image sequentially passes through the encoding network and the decoding network to obtain a final output contour; the application guarantees the integrity of a target contour and can effectively solve the problem of discontinuous contour; and the application designs a multi-level interaction network in the encoding network, so that visual information at different stages is exchanged and optimized, and the contour detection performance can be improved.

The application discloses a disaster reduction and rescue path planning method and system based on Internet of Things, and the method comprises environment interaction, network updating, reward function design, inner layer network training, outer layer optimization search and disaster reduction and rescue path planning. The application belongs to the field of path planning, and particularly relates to a disaster reduction and rescue path planning method and system based on Internet of Things. The scheme reduces instruction conflict, overshoot and oscillation by executing control instructions in sections and delaying the issuance of the control instructions. The safety and reliability of the disaster reduction and rescue path planning are improved by introducing dynamic temporary priorities, combining distance progress, heading punishment and energy perception to design sectional rewards. The unique adaptive probability adjustment of crossover and mutation operations is combined with dynamic adjustment of performance index fluctuation to adapt to complex disaster conditions on site and avoid parameters from falling into local optimum. The gene reset mechanism of the enhanced stage is introduced to continuously explore potential optimal solutions and improve path efficiency and rescue effect.

The invention relates to the field of target detection, and particularly discloses an online dynamic learning model based on a YOLO target detection model. According to the model, a text-image interaction feature enhancement network based on a language large model and a teacher-student model, automatic tag generation based on the large model and data enhancement based on a detected high-quality target are used for constructing an overall architecture of the YOLO online dynamic learning model; and online dynamic learning of the YOLO target detection model is carried out based on the overall architecture of the online dynamic learning model of the YOLO. A text-image interaction network is enhanced in combination with a large language model and a teacher-student model framework, and a high-quality target frame is screened for data enhancement. A bimodal large model driven labeling method is used, a language large model and a teacher and student model are combined to improve generalization ability, and a data enhancement strategy based on a high-quality target is provided. The method effectively relieves the problem of disastrous forgetting, reduces the marking cost, improves the model adaptability, and is widely applied to real-time target detection tasks in complex scenes.

The invention discloses a man-machine interaction analysis and safety evaluation method for an astronaut out-of-cabin maintenance task, and the method comprises the steps: dividing out-of-cabin preparation and out-of-cabin operation scenes, and determining a key stage and a conversion condition; constructing a task execution process model, and analyzing a task target and a cooperation relationship; information flows among astronauts, ground control and equipment are analyzed and quantified through a subject interaction network; a knowledge distribution model is adopted to represent knowledge requirements and dynamic changes in the tasks; integrating the models, and identifying cooperation bottlenecks and high-risk nodes; constructing a performance influence factor system based on the characteristics of the space environment, and quantifying factors influencing the operation of the astronaut; establishing a mapping rule of the performance state and the operation safety risk, and determining a task control mode; by constructing a space environment performance influence factor system and combining a probabilistic reasoning model, the astronaut operation error probability is quantified, and support is provided for task optimization.

The application discloses a camouflage target detection method based on a cross-stage feature interaction network, and is applied to the technical field of data processing, and an implementation scheme thereof is as follows: 1) acquiring a camouflage target detection training data set; 2) constructing a camouflage target detection model; 3) constructing a loss function; 4) training a detection model; and 5) detecting a camouflage target. The cross-stage feature interaction network constructed by the application effectively utilizes the correlation of adjacent stage backbone features by designing a bidirectional connection structure and matching a multi-scale cross-attention modulation fusion strategy, so that the feature information of each stage is perfected, the model feature expression capability is enhanced, the difference between a camouflage target and a background is better detected, and therefore the accuracy of a camouflage target detection result is effectively improved.