1138 results about "Information networks" patented technology

The invention relates to a multi-source data fusion management method and system for medical record information, and the method comprises the following steps: carrying out the data collection of an electronic medical record system, a medical image workstation and an inspection information system of a medical institution, and obtaining an original medical record data set; performing semantic mapping and concept association on the original medical record data set based on a preset medical ontology knowledge graph to obtain a semantic association medical record information network; performing dynamic time sequence feature analysis on the semantic association medical record information network through a time sequence feature extractor to obtain a time sequence medical record feature sequence; and carrying out heterogeneous data fusion on the time sequence medical record feature sequence to obtain a unified medical record data view, so that the technical problem that the capability of deeply understanding and mining medical record information is limited due to the fact that an existing medical record management method usually ignores semantic relevance between data is solved.

The invention discloses an aerospace network hierarchical cooperation routing optimization method and system, and relates to the field of aerospace information network communication, and the method comprises the steps: constructing a system model comprising an unmanned plane, satellite nodes and a communication link, defining link attributes, building a routing optimization objective function, introducing a reputation value-based multi-agent cooperation mechanism, and carrying out the routing optimization of the unmanned plane, the satellite nodes and the communication link. A hierarchical reinforcement learning framework is designed, inter-cluster decision and intra-cluster decision are divided, an upper layer selects an inter-cluster path according to global information, a lower layer plans an unmanned aerial vehicle path and defines a state, an action space and a reward function according to local information, an intelligent agent interacts with an environment to collect empirical data, and optimal routing communication is selected in combination with a real-time reputation value after training. Adjustment can be carried out according to network changes; according to the invention, the cooperative reliability is improved through a reputation value mechanism, the decision complexity is reduced through hierarchical learning, the routing performance is optimized, the network robustness, expandability and adaptability are enhanced, and the cooperative communication efficiency of the unmanned aerial vehicle and the satellite and the system stability are improved.

Methods and systems are described for indicating and configuring machine learning (ML) model support in a user equipment (UE) in a network. A UE can provide model support information to a node, including a ML type and / or version information of at least one model associated with a certain functionality. A network node uses the support information, and optionally previous model ID allocation information in current or other nodes, to determine at least one model ID, and signals it to the UE. The model ID refers to the model in subsequent model handling-related signaling between the UE and the node. The node assigns the model ID such that the UE and the node are aware that a given model ID refers to a given model or model version (e.g., the mapping is unique for the UE). The model ID can also be unique within a functional area, for example CSI reporting.

Provided are an active defense system and method for an unknown threat. The system includes an intelligent threat early-warning module (10), an unknown threat detection module (20) and a self-adaption defense processing module (30). The intelligent threat early-warning module (10) is configured to perform threat prediction on a power grid situation data set collected from a power information network in real time to obtain threat early-warning information and send the information to the unknown threat detection module (20). The unknown threat detection module (20) is configured to perform threat detection and analysis on collected unknown threat network data when receiving the threat early-warning information to generate a threat analysis report and send the report to the self-adaption defense processing module (30). The self-adaption defense processing module (30) is configured to trigger a defense processing operation corresponding to a preset threat defense strategy according to the threat analysis report.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems. A system and method for providing conversation intelligence services may include pre-processing, transcribing, and post-processing. A conversation recording may be pre-processed generating a conversation record (e.g., conversation object). The pre-processed conversation recording may be transcribed into a transcript. The transcript may be post-processed which may include keyword extraction, topic extraction, feature extraction, event generation, trigger action, and / or search indexing. Conversation information may be presented based on the pre-processing, the transcribing, and the post-processing.

The present application provides a DTX transmission method, a network node, a network device and a storage medium. The method includes: receiving, by a network node, DTX parameter information of a network device sent by the network device; determining, by the network node, at least one of a DTX on-duration and a DTX off-duration of DTX transmission of the network device, based on the parameter information; receiving, by the network node, the DTX transmission of the network device based on at least one of the DTX on-duration and the DTX off-duration of the DTX transmission. The network node at least includes a user equipment (UE), and the network device at least includes a base station.

The embodiment of the invention provides a network fault diagnosis method, a network fault diagnosis system, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining multi-dimensional data of network equipment, the multi-dimensional data comprises equipment configuration information, network topology information, performance index data and historical fault records; constructing a knowledge graph according to the multi-dimensional data, wherein the knowledge graph comprises nodes representing the network equipment, equipment links, network events and equipment configuration items, and edges representing a physical connection relationship, a logic dependency relationship, an equipment dependency relationship, network configuration information and a causal relationship among the nodes; performing map reasoning based on the knowledge map to obtain a map reasoning result; and generating diagnosis information for a target fault according to the map reasoning result. According to the embodiment of the invention, through multi-dimensional data integration, knowledge graph construction and automatic reasoning, the diagnosis efficiency, accuracy and adaptability are remarkably improved.

System, methods, apparatuses, and computer program products are disclosed for network policy generation using a fine-tuned large language model (LLM). Client data associated with a user device is determined and provided to the fine-tuned LLM in a prompt requesting a network policy. Client data may include device information, network information, application information, and connection information. The fine-tuned LLM generates a network policy based on the client information. The generated network policy is provided to the client to enable the client to enforce the network policy on the user device.

The invention discloses an entity digitization and link framework algorithm based on a heterogeneous graph attention network, and the algorithm comprises the following steps: S1, heterogeneous information network construction: carrying out the unified modeling of all multi-source heterogeneous data into a heterogeneous information network containing various types of nodes and various types of edges, s2, meta-path definition and guidance: defining "meta-paths" connecting different types of nodes to capture a complex deep semantic relationship, S3, heterogeneous graph attention network embedding: adopting an attention mechanism to enable a model to automatically learn importance of different neighbor nodes and different meta-paths, generating a final embedding vector of each entity, and establishing a heterogeneous graph attention network model; according to the method, the information fidelity is higher, modeling is directly conducted on different types of nodes and relations on a heterogeneous graph, more abundant and heterogeneous semantic information in data can be reserved compared with a multi-view method, and the end-to-end learning ability is higher; and the complexity of manually designing a fusion strategy is reduced.

The invention relates to the technical field of resource scheduling, in particular to a smart park resource scheduling method and system, and the method comprises the steps: constructing a heterogeneous information network representing the current state of a park; performing feature extraction on the heterogeneous information network by adopting a graph attention convolutional network, and generating a state embedding vector fused with high-order neighborhood information; inputting the state embedding vector into a scheduling strategy model, and predicting the expected variation of the candidate scheduling action to future park energy consumption, security index and traffic efficiency; according to the state embedding vector and an external management instruction, determining an optimization target of a current scheduling period from a preset operation normal form, and allocating a dynamic weight; and calculating a comprehensive utility score of each candidate scheduling action, and issuing and executing the candidate scheduling action with the highest score as a final scheduling instruction. According to the invention, the refinement level and the overall operation benefit of park management are improved.

The embodiment of the invention provides a network security validity verification and quantitative evaluation method and system, and relates to the technical field of network security, and the method comprises the steps: obtaining global dynamic threat intelligence and a multi-dimensional global network security risk data source, and carrying out the preprocessing; constructing a global feature engineering system based on heterogeneous information network atlas and sequence analysis, forming a feature vector matrix, and mapping the feature vector matrix into an index state vector; inputting the feature vector matrix, the index state vector and the external environment information vector into an evaluation model, dynamically adjusting the weight of the feature vector matrix of each dimension, and outputting the validity score of each safety control point; based on the score, calculating a safety effectiveness index based on a time decay factor; identifying a weak link based on the index, and performing simulation verification to obtain a simulation attack actual measurement result; and an error vector is constructed based on the result and the validity score, and parameter adjustment and weight calibration are carried out. According to the scheme, the accuracy and the real-time performance of network security evaluation are improved.

The invention provides a method, a device, equipment and a medium for carrying out resource configuration on a multi-mode network, and the method comprises the steps: obtaining the network state information of each network mode in the multi-mode network, the network state information comprising network request information and network environment index information; based on the network state information, running a preset configuration strategy code to generate a configuration strategy for the multi-modal network, and completing optimization of resource configuration of the multi-modal network according to the configuration strategy; wherein the configuration strategy code is generated by performing nested iterative optimization on the basis of a pre-constructed database comprising a plurality of data samples by utilizing a large language model; the network state information and network state input data of a code predictor used for screening codes in the configuration strategy code generation process are from the same network environment. Therefore, the corresponding configuration strategy can be adaptively generated according to the change of the network environment so as to optimize the resource configuration of the multi-modal network, and the network performance and the resource utilization efficiency are improved.

The invention discloses a three-network fault propagation risk assessment method and system for complex network analysis, and belongs to the technical field of power system toughness assessment and disaster risk management, and the method comprises the steps: collecting node and edge data of a power network, an information network and a traffic network, and forming a heterogeneous network topology structure; constructing a node importance evaluation function; carrying out weighted correction, and outputting a three-network coupling node importance degree sequence; generating a fault event triggering list; updating the node state matrix until the node state does not change any more, and outputting a fault influence range and a fault propagation path; and calculating a three-network overall connectivity loss rate, function recovery time estimation, key node fault sensitivity and a coupling dependence vulnerability index, and outputting a three-network fault propagation risk assessment index. According to the method, the dependency relationship and the influence strength among the three networks can be truly reflected, the contribution degree of the nodes to the system toughness is quantified, and the multi-dimensional, quantifiable and explainable effective evaluation of the fault propagation risk under the complex network is realized.

The invention relates to the technical field of information network security, in particular to a network vulnerability identification method, system and equipment based on data analysis, and the method comprises the steps: extracting time sequence features and spatial features of network data, and fusing the time sequence features and the spatial features to obtain a high-dimensional feature vector; determining the type and confidence of the network vulnerability according to the high-dimensional feature vector; determining the hierarchy to which the network vulnerability belongs based on the type and confidence of the network vulnerability; performing authenticity verification and correlation analysis on the network vulnerabilities of the first level and the second level to determine the authenticity and attack chain of the network vulnerabilities; and identifying the network vulnerability of the third level again, and marking the type to which the network vulnerability of the third level belongs according to an identification result. Therefore, the identification precision of the network vulnerability is improved.

The invention provides an interest point recommendation method based on a graph-enhanced user context information network. The interest point recommendation method comprises the following steps: acquiring historical track data of a user and preprocessing the historical track data; utilizing an interest point supplementing module to fill the interest point between two adjacent interest points of which the road network distance and the sign-in time difference are greater than a threshold value in the historical trajectory data to obtain enhanced trajectory data; inputting the enhanced track data into a user context information embedding module to extract a context information embedding vector of the user; according to the enhanced trajectory data of all users in the network, constructing a global trajectory graph by taking the interest points as nodes and taking a sign-in sequence of the users to the adjacent interest points as edges; inputting the global trajectory graph into a general travel module, respectively extracting POI embedding and user embedding by utilizing a GCN network and an MCL network to obtain a POI embedding matrix and a user embedding matrix, and fusing the POI embedding matrix and the user embedding matrix to obtain a POI-user embedding vector of the user; and inputting the context information embedded vector of the user, the POI-user embedded vector of the user and the global trajectory graph into a prediction module for prediction to obtain an interest point recommendation result of the user. According to the method, the core problem in the prior art is effectively solved, the accuracy and scene adaptability of recommendation of the next interest point are remarkably improved, and the method has important application value and wide development prospects in the fields of intelligent transportation, personalized position service and the like.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects (and core objects) for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.

The invention relates to the field of community old-age care, and discloses a community old-age care-oriented medical-care-and-care-combined data intercommunication and service coordination system, which comprises a sensing module used for acquiring physiological data of old people, old-age care service data, environment data and service demand information to form multi-modal information; the network module is used for carrying out multi-modal information transmission, protocol conversion and edge calculation preprocessing to obtain processed data; and the data module is used for constructing a standardized data lake according to the processed data by adopting a unified data standard and a main data management technology, and carrying out fusion storage, treatment and safety protection to obtain multi-source information. Multi-modal information is collected to provide a data basis for cross-mechanism cooperation, data transmission and preprocessing are performed, and a standardized data lake is constructed through a unified data standard and main data management, so that medical institutions, pension institutions and community grids can share global data such as health states, service records and the like of old people in real time.

The invention relates to a computer data processing technology, in particular to a real-time pipeline safety early warning method and system. The method comprises the steps of performing computer data processing according to pipeline topology information, dividing risk units and generating a risk unit session configuration structure; performing automatic feature extraction to generate a real-time risk feature vector; through machine learning fusion modeling, real-time features and historical baseline features are fused, and a dual-time-scale fusion feature vector is constructed; a preset intelligent model is used for reasoning and event judgment, a structured event flow account and resource adjustment parameters are generated, periodic self-optimization of sampling configuration, a model threshold value and an early warning strategy is driven, and therefore closed-loop intelligent monitoring and early warning of the state of the pipeline auxiliary equipment are achieved. According to the method, the early warning accuracy can be effectively improved, false alarm and missing alarm can be remarkably reduced, the adaptive capacity of the system is enhanced through a closed-loop learning mechanism, and an efficient solution is provided for application of the next generation of information network industry in related fields.

A system includes a set of crawlers that find and retrieve documents from an information network, an information extraction system, a knowledge graph storing nodes and edges that connect them, wherein each node represents a respective entity of a corresponding entity type of a plurality of entity types, and wherein the knowledge graph further stores event data relating to events detected by the information extraction system, a machine learning system that trains models that are used in connection with at least one of entity extraction, event extraction, recipient identification, and content generation, a lead scoring system that scores the relevance of information to an individual and references information in the knowledge graph, and a content generation system that generates content of a personalized message to a recipient who is an individual for which the lead scoring system has determined a threshold level of relevance.

This application relates to a measurement method and an apparatus. If determining that a terminal device meets a first measurement reporting condition, the terminal device sends first information to a network device. After receiving the first information, the network device sends second information to the terminal device, and the terminal device receives the second information from the network device, where the second information indicates to enter a relaxed measurement state of a serving cell. For such a terminal device, frequent measurement of the serving cell is equivalent to an unnecessary measurement process, and causes high power consumption. Therefore, the network device may indicate such a terminal device to enter the relaxed measurement state of the serving cell.

The invention discloses an infrared unmanned aerial vehicle target detection method based on multi-scale self-enhancement cross-layer fusion, and aims to solve the problem of difficulty in small target detection of an infrared unmanned aerial vehicle under a complex background. According to the method, a novel target detection network is constructed, and a lossless down-sampling module and a cascade asymmetric convolution module based on spatial reconstruction are designed in a backbone network of the novel target detection network, so that multi-scale context features are extracted while information loss is reduced. The neck network adopts an enhanced pyramid structure, deep semantics and shallow details are integrated through a cross-layer feature fusion module, and a multi-scale adaptive channel attention module is introduced to dynamically re-calibrate fusion features so as to focus key information. The network adopts four detection heads to output multi-scale prediction, and uses an EIoU loss function to optimize small target positioning precision. The method can effectively improve the feature extraction and detection capability of'weak, small and dark 'infrared unmanned aerial vehicle targets, and has higher robustness and accuracy in a complex scene.

The invention belongs to the technical field of construction technical scheme compilation, and particularly relates to an intelligent construction special scheme paragraph intelligent compilation method based on an information network, which comprises the following steps of: 1, dividing content processing into two categories according to different input contents: project general content and project characteristic content; step 2, vector embedding; step 3, index construction; step 4, index storage; step 5, carrying out similarity retrieval; and 6, outputting a retrieval result. According to the intelligent construction special scheme paragraph intelligent compilation method based on the information network, through automatic word segmentation, vector embedding and index construction, the construction special scheme compilation efficiency is greatly improved, and the time needed by manual compilation is shortened; historical schemes and standard requirements are utilized to ensure that the generated scheme content conforms to industrial standards and specifications, and the standardization degree of the schemes is improved; by distinguishing the project general content and the project characteristic content, a customized scheme can be generated according to the characteristics and requirements of a specific project.

A method and system for attribute community search based on a relation tree are provided. In the method, a data relation of a target domain is obtained, and an attribute information network of the target domain is constructed in the form of a graph. The attribute information network is loaded over a unified access interface, and data processing, subgraph construction, and algorithm operation and maintenance search are performed on the attribute information network. A community structure parameter k value and a search node q are received through an interface for attribute community search provided for a user, and a community that contains the search node q and satisfies structural cohesion and optimal attribute cohesion is returned. Rendering and interaction are performed on a user interface, which renders the community search result returned into a dynamic community network diagram and presents the dynamic community network diagram to the user.

The invention discloses a cognitive feature extraction and classification method and system based on electroencephalogram signals, and the method comprises the steps: constructing a cognitive divergence mapping network, so as to integrate a multi-branch brain region topology module and a neurodynamics physical information network module; the multi-branch brain region topology module divides detection branches of five brain regions (frontal lobe, central lobe, parietal lobe, occipital lobe and temporal lobe) according to 10-20 systems, and through feature extraction of different brain region branches, the feature learning ability of the Alzheimer's disease patient under the condition of cross-brain region signal heterogeneity is remarkably improved; the neurodynamics physical information network module can effectively extract changes of low-frequency and high-frequency components in electroencephalogram signals of the Alzheimer's disease patient by introducing frequency band separation, Fourier transform power spectrum constraint, an adaptive weighting mechanism and neural representation embedding in a frequency spectrum congruence space. The accuracy and generalization of electroencephalogram signal analysis are remarkably improved, and an efficient and non-invasive detection tool is provided for early diagnosis of the Alzheimer's disease.

The invention discloses a functional gradient piezoelectric material optimization method based on a physical information network and deep regression. The method comprises the following steps of: 1, constructing a physical information neural network embedded with a piezoelectric constitutive relation; 2, developing a deep regression network based on symbolic regression, wherein the deep regression network is used for converting the piezoelectric phase volume fraction distribution predicted by the neural network into an analyzable mathematical expression; step 3, carrying out joint optimization on the two networks by adopting an alternate iteration strategy; under the condition that parameters of the deep regression network are kept unchanged, a physical information neural network is trained preferentially to meet the precision requirements of a displacement field, a stress field, potential distribution and a piezoelectric phase volume fraction; and then fixing the physical information network, optimizing the expression generation capability of the deep regression network, and finally analyzing the piezoelectric phase volume fraction expression. According to the method, the physical information neural network and the depth symbol regression technology are fused, so that multi-physical field collaborative optimization of component distribution of the functionally graded piezoelectric material is realized.

Methods and apparatuses provide a mechanism to account for timing errors of a wireless device (12) in positioning measurements. In one example, a wireless device (12) performs reference-signal transmissions or measurements and sends information to a network node (20) that is involved in positioning of the wireless device (12). The information indicates associations of the reference-signal transmissions or measurements with respective timing groups of the wireless device (12). Each timing group represents a related set of transmission or reception timing errors within the wireless device (12). Based on the information, the network node (20) accounts for the different timing-group associations when performing positioning calculations that are based on the reference-signal transmissions or measurements performed by the wireless device (12).

The invention discloses a geological archive structure integrity guarantee method and system based on map dynamic maintenance, and the method comprises the steps: obtaining an archive circulation path which is directly and indirectly connected with an archive for peripheral associated data marked as an undeletable archive, analyzing a potential breaking point in the path, and obtaining a risk region which may cause a data isolation problem; performing deep scanning on archive data in the risk area to obtain content features and historical circulation records of archives, judging whether the content features and the historical circulation records meet data accuracy standards or not, and determining a non-key archive list needing to be deleted; according to the non-key file list, analyzing the operation influence range of each file to be deleted, obtaining potential impact on the stability of the whole system after the file is deleted, and if it is judged that the information network integrity is damaged due to deletion, moving the file out of the deletion list; and for the finally determined deletion list, acquiring backup data and a flow path log of the archive, and updating the archive connection atlas through a dynamic maintenance mechanism to obtain a new information network structure.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.

A method of video encoding is provided. The method may include receiving, by a head portion of a Lightweight Multi-level mixed Scale and Depth information with Attention mechanism (LMSDA) network, an input image. The method may include extracting, by the head portion of the LMSDA network, a first set of features from the input image. The method may include inputting, by a backbone portion of the LMSDA network, the first set of features through a plurality of LMSDA blocks (LMSDABs). The method may include generating, by the backbone portion of the LMSDA network, a second set of features based on an output of the LMSDABs. The method may include upsampling, by a reconstruction portion of the LMSDA network, the second set of features to generate an enhanced output image.

The invention relates to the technical field of wireless communication protection, in particular to a wireless communication self-adaptive safety protection method and system based on edge computing. Capturing physical layer channel state information when the Internet of Things terminal equipment accesses the network gateway for the first time, a functional area semantic identifier of the network gateway and a current timestamp, and generating an anonymous session voucher; tracking operation track data of the anonymous session voucher, and constructing a track operation fragment; obtaining a privacy risk assessment value based on trajectory uniqueness, spatial information sensitivity and behavior association analysis of the trajectory operation fragment; when the privacy risk assessment value exceeds a preset risk threshold value, triggering a track link breaking mechanism; obtaining privacy enhanced trajectory data based on privacy protection processing and a trajectory broken link mechanism; training a virtual data generation model through the privacy enhancement trajectory data; and after the virtual data generation model converges, driving a virtual data generator in the virtual data generation model to generate a virtual operation and maintenance data stream so as to realize self-adaptive protection of wireless communication data.