1391 results about "Information networks" patented technology

The invention provides a multi-dimensional data authority management and privacy protection method for an electric power information network, and the method comprises the steps: dynamically selecting the type of a watermark according to the leakage risk level of a screenshot event, and enabling the watermark type to comprise a simple character watermark, a character and image mixed watermark, an invisible watermark, and a two-dimensional code watermark; user screenshot content factors are added in a screenshot event distribution curve of user historical data to judge the risk level of a user, the risk level of a user group is updated, and when the screenshot frequency, time interval, screenshot mode, distribution mode and curve volatility of the user are all normal and screenshot content only aims at sensitive data screenshot, the risk level is improved; a screen capture auditing and monitoring mechanism is established, full-life-cycle recording and tracking are carried out on screen capture behaviors of a user, and screen capture events, watermark embedding conditions and abnormal behavior information in a system are monitored in real time through a visual instrument panel and a report.

Provided is a method for driving behavior modeling based on spatio-temporal information fusion, relating to the field of driving behavior simulations. The method includes: constructing a driving behavior model, where the driving behavior model includes a spatial information encoding network, a temporal information encoding network, a feature fusion network, and a feature decoding network, with the feature fusion network being connected to both the spatial information encoding network and the temporal information encoding network, and the feature decoding network being connected to the feature fusion network; determining a future trajectory sequence of a target main vehicle at future time points based on the trained driving behavior model according to spatial information and temporal information of the target main vehicle, where the target main vehicle is controlled to travel according to the future trajectory sequence at the future time points.

An active defense system and method for an unknown threat. The system comprises: an intelligent threat early warning module (10), an unknown threat detection module (20), and an adaptive defense processing module (30). The intelligent threat early warning module (10) is configured to perform threat prediction on a power grid situation data set acquired in real time from a power information network, so as to obtain threat early warning information, and send the threat early warning information to the unknown threat detection module (20); the unknown threat detection module (20) is configured to perform threat detection and analysis on acquired unknown threat network data when receiving the threat early warning information, generate a threat analysis report, and send the threat analysis report to the adaptive defense processing module (30); and the adaptive defense processing module (30) is configured to trigger, on the basis of the threat analysis report, a defense processing operation corresponding to a preset threat defense strategy.

Computerized systems and methods for training a trajectory prediction model for autonomous driving vehicles. The systems and methods receive agent dynamics data, high-dimensional (HD) map data; a training data set; and a plurality of meta paths through the HIN representing lane transitions, each meta path comprising a sequence of lane nodes and edges. Scene encoding is performed on the agent dynamics data and the HD map data to produce a directed Heterogeneous Information Network (HIN) as a graph comprising nodes and edges. The trajectory prediction model is trained based on a comparison between a prediction, by the trajectory prediction model, of a positive or negative presence of the meta paths between nodes and meta path ground truth obtained from the training data set and between motion trajectory predictions, by the trajectory prediction model, and motion trajectory ground truth obtained from the training data set.

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.

The invention relates to an optimization method and device based on intelligent distributed network topology and electronic equipment, and the method comprises the steps: obtaining network state information monitored in real time on network nodes, the network state information comprising a bandwidth utilization rate, communication delay between the nodes and a loss rate of a data packet in a transmission process; calling a dynamic topology adjustment strategy in response to abnormity of the network state information; a decision algorithm is added for each network node, each node shares a decision result and network state information, all nodes coordinate decisions according to a consensus mechanism, and the decision algorithm is used for analyzing the network state information of the nodes. According to the optimization method, the data transmission path is optimized by dynamically adjusting the network topology, the overall performance of the network is improved, continuously changing communication requirements and states in the network can be conveniently adapted, and meanwhile, the reliability of key data transmission is improved and the network maintenance cost is reduced through node autonomous decision-making, inter-node cooperation and a consensus mechanism.

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.

A network management system may collect processor usage statistics from one or more network devices. The network management system may determine, for each network device of the one or more network devices, aggregate processor usage statistics across a time window based on the processor usage statistics and, based on an aggregate overall processor usage for a given network device exceeding a baseline threshold, analyze aggregate per-process processor usage for the given network device to determine one or more processes as a root cause of anomalous behavior of the given network device. The network management system may generate a remedial action to remediate the root cause.

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.

Systems, methods, and instrumentalities are provided for a network control node for connecting a wireless transmit / receive unit (WTRU) to a network slice in a network. The network control node may receive service information associated with a service being requested by the WTRU, wherein the service information comprises one or more of a service class, a quality of service (QoS) requirement, or a mobility characteristic. The network control node may determine slice information associated with a plurality of network slices, wherein the slice information comprises one or more of an identifier of the network slice, priority of the network slice, a service class served by the network slice, a target device class, a QoS target, mobility support, security service, charging information, and / or performance information. The network control node may determine subscriber information for the WTRU. The network control node may select at least a first network slice of the plurality of network slices to serve the WTRU, wherein the network control node selects the first network slice based on the service information, the slice information associated with the first network slice, and the subscriber information for the WTRU. The network control node may determine whether the network control node or another network control node should service the WTRU for accessing the first network slice. On condition that the network control node determines that the network control node should service the WTRU for accessing the first network slice, the network control node may provide at least one network service associated with the first network slice to the WTRU. Or, on condition that the network control node determines that the another network control node should service the WTRU for accessing the first network slice, the network control node may send a request to the another network control node that indicates that the another network control node should service the WTRU for accessing the first network slice.

The invention provides a 5G portable WiFi fault diagnosis and recovery method and system. The method comprises the following steps: collecting network connection data, system information and operation logs from a communication terminal connected to 5G portable WiFi equipment; analyzing the data, and generating a fault diagnosis report; selecting and executing corresponding recovery operation from a recovery scheme library according to the report; monitoring equipment state and network connection in real time, and judging whether recovery is successful or not; if the fault is not solved, an advanced solution is provided, and equipment information, program attributes, scheme library information, network connection data, system information and operation logs are sent to an edge server; the edge server obtains fault data and diagnosis reports of a plurality of similar devices as references, potential fault laws are mined through big data analysis, and reference analysis data are generated; and optimizing an intelligent diagnosis program and a recovery scheme library according to the reference analysis data and other information. The fault type can be accurately identified, the fault recovery time is remarkably shortened, and the user experience is improved.

The invention discloses an AI service connection establishment method and device, a terminal and network side equipment, and belongs to the technical field of communication, and the AI service connection establishment method comprises the steps that the terminal sends a first request message to a network side, and the first request message is used for requesting the network side to allocate network resources for an AI service; and the terminal receives a first request response message returned by the network side, wherein the first request response message comprises at least one of the following items: information of a first network element allocated for the AI service by the network side, information of a second network element allocated for the AI service by the network side, and information of a model. In the technical scheme of the invention, the network side can allocate the communication resource and the AI resource for the AI service according to the request of the terminal, and the problem that a wireless mobile communication system cannot provide the AI service for the terminal is solved.

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.

Methods, systems, and devices for wireless communications are described for a user equipment (UE) to report information associated with neighbor cells when one or more of the neighbor cells are operating in an energy saving mode. A serving cell of a network entity, such as a primary cell (PCell) may request a neighbor cell report from a served UE and, if a neighboring cell is in a NES mode, the served UE may provide an indication that the neighbor cell is in the NES mode. The UE 115 in some cases may transmit a demand for an on-demand signal from the neighbor cell, and monitor for the subsequent signal. The UE may transmit the neighbor cell report to the serving cell based on information provided from one or more of the transmissions of the neighbor cell.

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.

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 and systems. A system and method (e.g., a custom workflow actions system and related processes) for creating and using a custom action (e.g., custom code action). The custom code action may be created and added to a workflow. The workflow may be executed based on the occurrence of one or more events such that the new custom code action may be triggered as part of the workflow (e.g., resulting in an occurrence of a customized action corresponding with a custom instruction code).

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 invention relates to the related technical field of digital data processing, in particular to an intelligent ancient book information processing method and device based on artificial intelligence, and the method comprises the steps: scanning an ancient book, constructing a knowledge graph, dividing a domain database, and training a vertical processing model; introducing a multi-form historical carrier to form a multi-mode sensing set, establishing a multi-information network, and embedding a processing model; the method comprises the following steps: establishing a dialog box, realizing multi-round interaction and combining threshold optimization output, solving the technical problems of digitization and fragmentation limitation in ancient book information processing and incapability of effectively integrating and utilizing ancient book information due to diversity and complexity of ancient books and literatures, and realizing construction of a knowledge graph and division of a domain database. A knowledge system with a hierarchical structure and logic association is formed, deep learning and optimization are performed according to specific requirements in the field of ancient books by training a vertical processing model, a dialog box is established, multi-round interactive feedback with the user is performed, and the user is assisted to quickly and deeply explore the ancient books.

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.