14 results about "Network characterization" patented technology

The invention provides an Internet infrastructure knowledge graph construction method based on network representation learning and classification and grading thought. The method comprises the following steps: constructing an ontology model of Internet infrastructure; collecting map data; utilizing the ontology model and the atlas data to create entities and extract relationships between the entities; classifying and grading the entities to obtain classification results and grading results corresponding to the entities; an initial knowledge graph is constructed through the relations between the entities, and each entity in the initial knowledge graph is additionally associated with the corresponding classification result and grading result; and based on the network representation learning and the graph data, updating the edges, the classification result and the grading result in the initial knowledge graph to obtain a final knowledge graph. The knowledge graph is constructed and updated through network representation learning and classification and grading thoughts, so that the dynamic association relationship between the entities is accurately captured, wrong association edges are screened out, and the network representation accuracy and availability of the entities are improved.

The invention provides a complex network key node identification method based on an attention mechanism and multi-scale feature fusion. The method comprises the following steps: S1, generating a feature matrix; s2, inputting the data into a training model composed of a multi-scale convolution branch and a channel attention branch; and S3, outputting an identification result by using the trained model. According to the method, sparse network representation can be solved through a dynamic mechanism, so that more robust and accurate key node identification is realized.

The application discloses a kind of text enhanced multi-modal road network representation learning method, belong to city computing field.The method includes: first, extract target area road vector data, road section static attribute and the panorama street view image of each road section center point;Multi-modal large language model is generated natural language description by street view image, combined with static attribute and prompt word is output structured text description by first large language model, further obtain the directed road network graph containing structured text description;Topology enhanced semantic representation is output based on structured text description by graph neural network, further by word element random masking, the graph neural network and topology enhanced semantic representation of training are completed;Second large language model can output specified downstream task result according to topology enhanced semantic representation.The application effectively solves the problems that the existing road network representation method is difficult to model the correlation of modes, the reasoning ability of data missing is weak, and the high-order semantic modeling is insufficient.

This invention discloses a circRNA-disease association prediction method based on multi-source feature fusion, belonging to the field of disease-aided diagnosis technology. The method first constructs a circRNA-disease association network and extracts the GIPK functional features of the disease and the Transformer sequence features of the circRNA; then, it systematically learns the network topology embedding at three scales: microscopic, mesoscopic, and macroscopic; finally, it deeply fuses the above multi-source features and inputs them into an XGBoost classifier. This invention systematically solves the three major technical defects of existing technologies—"lack of biological semantics," "single network representation," and "inefficient feature fusion"—by introducing biological attribute features, multi-scale network structure features, and a deep fusion strategy, achieving high-precision, high-robustness, and highly biologically interpretable association prediction.

The application discloses a coal body hydraulic fracturing fracture three-dimensional space network reconstruction and characterization method, which is based on real CT three-dimensional body data, can realize the transformation of fracture structure from geometric visualization to parameterization and evaluation, can directly reflect the real connection relationship of the fracture in the three-dimensional space by identifying and three-dimensionally reconstructing the internal fracture of the coal body before and after fracturing, avoids the connection misjudgment and scale deviation caused by two-dimensional slice inference, improves the authenticity of the evaluation result from the source, and simultaneously adopts skeletonization and node-edge modeling on the fracture network, converts the three-dimensional voxel fracture structure into a node-edge form fracture network model, significantly reduces the calculation complexity under the premise of ensuring the fracture space connection relationship, enables the connectivity and flow conductivity characteristics of the fracture network to be expressed in the form of unified parameters, and is especially suitable for the fracture network characterization of the laboratory true triaxial test and the digital coal rock sample data based on CT scanning.

The invention discloses a method for rapidly characterizing the average grain size of a ferromagnetic material based on magnetic Barkhausen electromagnetic nondestructive testing. The method comprises the following steps: preparing a sample; based on an EBSD material characterization method, carrying out quantitative characterization on the average grain size of the ferromagnetic material; based on magnetic Barkhausen noise detection equipment, electromagnetic nondestructive detection is carried out on the ferromagnetic material, and electromagnetic characteristic parameters representing the magnetic characteristics of the ferromagnetic material are obtained; and by taking the electromagnetic characteristic parameters as input and the average grain size as output, establishing a characteristic microstructure deep neural network characterization model of the magnetic Barkhausen characteristic parameters so as to realize nondestructive testing of the characteristic microstructure-average grain size of the ferromagnetic material. According to the invention, nondestructive testing of the average grain size of the ferromagnetic material is realized.

The application discloses a data processing and feature scoring method and device based on a supply chain heterogeneous network, and relates to the technical field of supply chain data management.The application is executed by a physical device with a data acquisition port, a data cache medium, a central computing unit and a data output port, a dynamic updating supply chain heterogeneous transaction network including multiple types of nodes and edges is constructed, the network is divided into a credit turnover frequency sub-network representing transaction frequency between enterprises, a credit turnover amount sub-network representing transaction scale, a financing frequency sub-network representing financing activity and a financing amount sub-network representing financing scale according to business dimensions, multi-dimensional features are extracted, preprocessed and output to a standardized score through two-layer mapping, and the model weight is iteratively optimized through supervised learning.The application overcomes the technical defects of homogeneous networks losing heterogeneous information, and realizes global graph data processing and quantitative output of multi-source heterogeneous data.

A method and system for enhancing large language model auxiliary network operation and maintenance capability, the method comprising: collecting a training sample, the sample collection process comprising: inputting analog network data into a network simulator, generating a text attribute graph of an analog network and a corresponding natural language query, and collecting network behavior data according to the natural language query; a large language model is constructed and trained, and the working process is as follows: natural language query and equipment configuration texts are generated through a configuration encoder, text attribute graphs are obtained through a topology encoder, then the text attribute graphs are spliced into network representation, and then the network representation is mapped into a Q layer and a V layer of an attention mechanism of the large language model in a low-rank fine tuning mode; and the large language model receives natural language query input by the user and the text attribute graph of the current network for reasoning output. The invention relates to the field of information communication networks, can efficiently model multi-modal network data, enhances the understanding ability of a large model for the current network state, and effectively assists network operation and maintenance.

The invention discloses a fracture network optimization characterization and uncertainty control method and system, and the method comprises the steps: S1, constructing an SA-UNet substitution model and a DAOCRN deep learning inversion model based on a self-attention mechanism according to the multi-source monitoring data of a high-level radioactive waste disposal site and a typical fracture network sample; s2, according to the SA-UNet substitution model, initial population generation and simulation calculation, mismatch degree evaluation and optimal set screening are carried out through genetic algorithm inversion, and a fracture probability result is obtained; and S3, according to the fracture probability result and a DAOCRN deep learning inversion model, through deep learning inversion optimization representation, obtaining a final fracture network representation result with the minimum mismatch degree. By adopting the technical scheme of the invention, accurate characterization and uncertainty reduction of the fracture network in a high-level radioactive waste deep geological disposal scene are realized.

The invention provides a cross-network user alignment method based on a user attribute information completion and correction algorithm, and the method comprises the steps: constructing a user alignment model based on attribute information completion and correction, and carrying out the training: carrying out the joint completion and correction of the attribute information of source network data and target network data; performing multi-level network representation on the complemented and corrected attribute information and network structure information; fusing the multi-level network representations with the adaptive weights through vector fusion to obtain final representations of the source network and the target network; dividing parts with anchor links in the final representation of the source network and the target network into a training set and a test set, performing mapping learning by using the training set through an improved RCCA method, and performing testing by using the test set to obtain trained model parameters; according to the method, to-be-aligned source network data and target network data are obtained, cross-network user alignment is performed by using the trained user alignment model based on attribute information completion and correction, and the precision of the cross-network user alignment model is improved.

This invention discloses a network representation method and system based on a multidimensional heterogeneous resource summary view, belonging to the field of communication network technology. The method includes: acquiring network topology, device, and link status information; constructing a global original view; initializing each node and link as a supernode and superedge respectively to form an initial summary view; generating candidate supernode pairs; calculating the combined merging benefit that balances storage compression and path quality distortion; and iteratively performing supernode merging under attribute purity constraints until the summary view size meets a preset target; during the merging process, aggregating and updating the multidimensional attributes of superedges; and retaining only superedges that reduce overall storage overhead based on the minimum description length principle. This invention reduces the network state size while maintaining effective representation of multidimensional resource characteristics and possesses good dynamic adaptability, making it suitable for efficient modeling and analysis in large-scale complex network environments.

A docking station for leashing a wireless peripheral device to a network of an enterprise includes a docking station hardware processor, a docking station data storage device, a docking station wireless radio to wirelessly couple the docking station to an information handling system and the wireless peripheral device. The docking station hardware processor to execute computer-readable program code instructions of a docking station network characterization detection module to generate a generic attribute profile (GATT) network profile based on the detected network characteristics. The docking station hardware processor or peripheral device microcontroller to execute computer-readable program code of a leash authorization module to compare the GATT network profile to a copy of the GATT network profile stored on a data storage device of the wireless peripheral device to leash the wireless peripheral device to the docking station using the GATT network profile.

The application discloses a network representation method and system based on a multi-dimensional heterogeneous resource abstract view, and belongs to the technical field of communication networks, and comprises the following steps: acquiring information of network topology, equipment and link state, constructing a global original view, and initializing each node and link as a supernode and a superedge respectively to form an initial abstract view; generating a candidate supernode pair, calculating a comprehensive income of merging which takes into account storage compression income and path quality distortion, and iteratively performing supernode merging under the attribute purity constraint condition until the size of the abstract view meets a preset target; in the merging process, multi-dimensional attributes of the superedge are aggregated and updated, and only the superedge which reduces the overall storage overhead is reserved based on the minimum description length principle. While reducing the size of the network state, the application can maintain effective representation of multi-dimensional resource characteristics, has good dynamic adaptation capability, and is suitable for efficient modeling and analysis in a large-scale complex network environment.

The application discloses a cloud-edge collaborative network representation method, which adopts feature decoupling technology to map each network feature to a common space and a difference space respectively, so as to reveal the inherent attributes and structures. On this basis, the features are recombined and spliced to construct more comprehensive feature representation. For the main network, the common features are spliced to form a joint feature vector to strengthen the feature expression of the main network. Meanwhile, for each sub-network, the common features of the main network and the difference features of the sub-network are spliced to form a new feature vector to highlight the uniqueness of the sub-network. Finally, the main network and the sub-network are jointly trained on the downstream node classification and link prediction tasks to obtain the final network node representation, and the cloud-edge collaborative technology is used for representation. The hidden relationship between the sub-networks is mined, and the network structure change caused by node failure or new node addition can be responded in real time, so that the robustness and accuracy of network representation are further improved.