1729 results about "Data Web" patented technology

The invention provides a security encryption communication method and system based on quantum key management. The method comprises the following steps: acquiring system state data, and generating node state data, network connection diagram data and a system state matrix; receiving a communication request data packet, and generating a request feature vector, a service weight coefficient and a service demand matrix; the method comprises the following steps: collecting real-time network state data, constructing an enhanced Bayesian network model, and generating a network state scoring matrix; acquiring key pool state data, establishing a key consumption prediction model, and generating a key resource distribution matrix; constructing a multi-objective optimization model, and generating a scheduling strategy matrix; and executing the scheduling strategy and performing performance evaluation. According to the method, the quantum state de-coherence effect and the channel noise influence are considered, efficient management and safe distribution of quantum keys are achieved by establishing a complete evaluation and prediction system, and the performance and reliability of a communication system are improved.

The invention discloses a supply chain warehouse management system and method based on AI digital intelligence, and relates to the technical field of intelligent warehousing, and the method comprises the steps that a data processing unit collects supply chain system data through a data processing module, and constructs an associated data network containing goods information, shipping space information and order information based on the supply chain system data; the goods allocation unit executes a goods allocation algorithm according to the associated data network, extracts an association score based on goods information, analyzes a collaborative ex-warehouse frequency, and calculates an affinity matrix; the storage scheme unit generates a storage position scheme based on the affinity matrix; the job scheduling unit constructs a scheduling model, performs task optimization and outputs a task allocation sequence; the order picking navigation unit is provided with an order picking path planning module, presents a path and guidance, collects execution data, and optimizes the system through analysis. The problems that in traditional warehouse management, the goods allocation efficiency is low, task scheduling is not optimized, and the goods picking path is unreasonable are solved.

The invention relates to the technical field of traffic management, in particular to a dynamic traffic scheduling method and system based on network configuration, and the method comprises the following steps: based on network configuration data, extracting network index data, network indexes including network delay, packet loss rate and bandwidth size, calculating a network quality score, evaluating a current network state, and obtaining network state information. According to the method, the key network indexes are extracted, the network quality score is calculated, and the current network state is comprehensively evaluated, so that network configuration can respond to network requirements and condition changes, and according to the real-time network quality, the bandwidth resources are dynamically adjusted, and a priority rating mechanism is introduced, the network quality is improved. The time sensitivity of different data contents is evaluated, it is ensured that important data can obtain enough transmission resources, the influence caused by network fluctuation is compensated by monitoring the network fluctuation in the data transmission process and adjusting bandwidth allocation in real time, and the continuity and efficiency of data transmission are ensured.

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a self-adaptive data processing optimization method, device and equipment and a medium, and the method comprises the steps: collecting operation data, host performance data, network state data and historical task data of a target data source, and constructing an analysis model in combination with recovery parameters and strategy preference, predicting a task load state, resource consumption and execution duration, generating a task execution strategy, completing task scheduling and execution monitoring, and collecting execution feedback data for iterative optimization of the analysis model. According to the method, an analysis model is constructed by fusing multi-source system data and historical task information, a task execution strategy is generated in combination with a dynamic prediction result and a strategy weight, intelligent task scheduling and process monitoring are realized, and feedback data is used for model iterative optimization. The task execution efficiency is improved, the resource use rationalization is realized, and the model adaptive capability is enhanced.

The invention relates to the technical field of communication management, and provides a communication base station traffic prediction management system based on deep learning, which comprises a multi-source data acquisition module used for acquiring base station space-time traffic data, user behavior data, network state data, external influence factors and data set slice service parameters; the spatio-temporal feature processing module is used for performing spatio-temporal alignment, noise filtering and slice feature coding on the multi-source data; and the dynamic model prediction module is used for constructing a multi-modal prediction network of a space-time Transform, a graph neural network and a slice exclusive sub-model. A base station association graph based on geographical distance and service correlation is constructed, spatial features are extracted through a multilayer graph convolutional network, slice features and spatial-temporal features are fused by using a gating mechanism, a Pareto optimal strategy is generated by using a multi-objective optimization algorithm, and a strategy library is updated in combination with a forgetting factor, so that the probability of forgetting is reduced. The response time of the system in an abnormal scene is shortened, and the strategy optimization efficiency is improved.

A web browsing application can implement a vector database to store session data. A web browsing application can automatically embed loaded web content into an embedded data store that maintains session data for a number of web sessions. A user can query the web browser using simple instructions. The web browser can interpret the instructions and use the embedded data store to quickly search across multiple modalities of embedded data to retrieve relevant results. The web browser can use machine-learned models to answer queries or perform other tasks by performing vector-based queries over the content of visited web data.

The disclosed cyber threat detection and deception system leverages metadata information collected by the data storage management system. Using the metadata collected by the data storage management system, the cyber threat detection and deception system analyzes that metadata to detect any anomalies. Once suspicious or abnormal behavior is detected in an asset, the cyber threat detection and deception system creates and deploys a cyber deception plan for that asset. The cyber deception plan is implemented by way of deploying sensors or emulation traps in any number of cyber-threat appliances within the data network. Lures or tokens are configured and deployed on the suspected assets themselves to redirect attackers to the emulation traps.

A method for transferring data from a medical device to a server comprises receiving a video stream from the medical device, capturing an image from the video stream, transmitting the image to the server via a data network, and extracting the data from the image. The image may illustrate and / or represent data over a period of time. The method may also comprise transmitting, from a data module receiving the video stream from the medical device, a signal to a router that indicates that the data module is connected to the network. The method may also comprise transmitting a command to the data module to start capturing the image, transferring the image to the router, broadcasting a signal indicating that the data module has captured the image, receiving the broadcasted signal at the server, and storing the image at the server.

The invention discloses a quasi-brittle material damage field inversion method based on a physical information neural network. The method comprises the steps of data acquisition and processing; presetting initial damage field data, and taking the modulus of each unit in the initial damage field data as an independent to-be-inverted parameter; taking the position information of each node in the space as the input of a neural network, processing through a hidden layer of the neural network, and taking displacement data corresponding to the position information as network output data; constructing a loss function of a neural network according to the preprocessed displacement data, network output data and a mechanical law; and performing training optimization on the initial damage field data by using a loss function of a minimized neural network, optimizing hyper-parameters of the neural network by adjusting weights of control item loss, boundary item loss and data item loss until a preset convergence condition is met, and determining target damage field data. According to the method, the dependence of a neural network model on a data set is remarkably reduced, and the inversion result is ensured to have relatively high physical interpretability.

The invention provides a 500kV GIS multi-mode working condition abnormity comprehensive monitoring system and method based on voiceprint recognition. A data acquisition module is used for collecting multi-mode original data from various sensors and devices; the network transmission module is used for transmitting the data to the voiceprint scoring module and the multi-modal analysis module through a network; the voiceprint scoring module is used for extracting sound features from the multi-modal original data, comparing the sound features with a preset voiceprint model, and giving a similarity or anomaly score; the multi-modal analysis module is used for comprehensively analyzing data from different modals, fusing various data and performing data analysis and pattern recognition by applying a deep learning algorithm; the fault judgment module performs fault detection and diagnosis by applying a preset rule or model based on an analysis result of the multi-modal analysis module, and judges whether the system or the equipment has a fault or not; and the result output module is used for displaying the final analysis and judgment result in the form of a chart, a report or an alarm.

The invention provides an infrastructure security analysis method and system based on a large model, and the method comprises the following steps: collecting multi-source heterogeneous data of an infrastructure, including physical equipment sensor data, network flow data, operating system logs and external threat intelligence; converting the multi-source data into standardized feature vectors of a unified time axis through a space-time alignment module; and inputting the standardized feature vector into the fine-tuned large language model for semantic understanding, and generating an equipment behavior semantic description and threat intention analysis result. According to the method provided by the invention, a large language model and reinforcement learning collaborative architecture is provided, and the large language model is improved, so that end-to-end mapping from multi-source data to threat semantics is realized, and the attack detection rate is improved.

Systems and methods are for mitigating hindsight bias related to designing and using artificial intelligence models for outlier events. More specifically, systems and methods for the use of synthetic data in the training and / or validation of model predictions in order to prevent overfitting and generate predictions that attempt to predict the unpredictable.

The invention relates to the technical field of smart power grids, and discloses a source-grid load-storage cooperation method, which comprises the following steps: carrying out data fusion on real-time acquired source-side distributed power supply output fluctuation data, grid-side topology constraint parameters, load-side time-sharing load demand data and storage-side charge state data to obtain a multi-dimensional system state set; performing short-medium-long term multi-scale coupling prediction on the multi-dimensional system state set to obtain a source load dynamic balance prediction curve; constructing a multi-target collaborative optimization function according to the power fluctuation interval, and generating a target adjustment instruction set based on the multi-target collaborative optimization function; updating the target running state based on the adjustment instruction set; based on the deviation degree of the updated operation state and the prediction state, correcting a weight parameter of the multi-target collaborative optimization function, and based on the corrected multi-target collaborative optimization function, performing source-network-load-storage collaborative regulation and control on the target power platform; according to the invention, the efficiency of source-network load-storage collaboration can be improved.

Methods and apparatuses are described herein for wireless transmit / receive unit (WTRU)-based edge compute autoscaling. For example, a WTRU may receive a message from a network device. The message may include a list of one or more edge application servers (EAS) associated with an un-instantiated application. The WTRU may select an EAS from the list of one or more EAS associated with the un-instantiated application. The WTRU may transmit a request to an edge data network (EDN), the request indicating a request to instantiate the un-instantiated application at the selected EAS. The WTRU may receive a response from the EDN, the response indicating that the un-instantiated application has been instantiated at the selected edge application server and indicating information for accessing the now instantiated application at the EAS.

A method of creating a SMARTPASS with computed estimated value including, a SmartHub computer processor system and data network, method including: receiving electronic requests to validate / authorize members and a purchase reporting request from a buyer and a financing request from a supplier engaged in a trade transaction over the network; determining if the electronic requests from the buyer and supplier are authorized, if so, creating the SMARTPASS with computed estimated value on the purchase reporting request; saving / minting the SMARTPASS as a non-fungible token, assigning an initial NFT value; computing the estimated value, tariff, taxes, or other fees; updating a database of the SMARTPASS, NFT, and initial NFT value with updated value; determining if financing requested, and if so: posting an NFT proxy to a second blockchain public NFT repository, notifying parties of financing options, and notifying FinTech, DeFi or decentralized Lenders; or if not notifying FinTech, DeFi or decentralized Lenders.

Please delete the Abstract of the Disclosure, and replace it with the following: In the current 3GPP standard(s), there is no mechanism or procedure for Key issue. This disclosure provides a solution to this key issue. A method for a User Plane Function (UPF) includes receiving, from a core network node, at least one of Users Equipment ID (UE ID), Data Network Name (DNN), Single-Network Slice Selection Assistance Information (S-NSSAI) and information related to UE Route Selection Policy rule (URSP). The method includes checking whether user data for a User Equipment (UE) using a User Equipment (UE) session with the DNN and S-NSSAI matches the information related to UE Route Selection Policy rule (URSP) or not. The method includes sending to the core network node a result of the checking.

There is provided a method for providing energy data analytics for a data network associated with a wireless communication system, the method comprising receiving at least one input parameter associated with energy usage corresponding to the data network, deriving energy data analytics for the data network based on the received at least one input parameter, and sending an energy data analytics output parameter based on the derived energy data analytics, wherein the energy data analytics output parameter comprises an energy usage statistic or energy usage prediction for the data network.

The invention, which relates to the technical field of network security protection, discloses a photovoltaic power station 5G network security protection device comprising the following components: a data acquisition module, an intelligent fusion analysis and verification system and a protection mechanism triggering module. According to the invention, by integrating an intelligent fusion analysis and verification system, multi-dimensional data such as photovoltaic power station operation data, 5G network characteristic parameters, external environment factors and personnel operation behaviors can be comprehensively collected and deeply analyzed, and a potential association relationship and a mutual influence mechanism among different factors can be accurately mined; meanwhile, a dynamic association rule self-adaptive updating mechanism can adjust the association rule in real time according to newly collected data and a mined new association relationship, the accuracy and timeliness of an analysis result are ensured, the credibility of the data is improved, and network security risks can be found and coped with in time.

The invention relates to the technical field of network communication, in particular to a remote fault diagnosis method and system based on an FTTR network and a storage medium. The method comprises the following steps that a monitoring module used for monitoring the network state is deployed on a node of each FTTR network, each monitoring module is provided with a data acquisition unit and a state analysis unit, and each node collects network state data in real time through the monitoring module. The network state data comprises monitoring network delay, packet loss rate, equipment temperature and bandwidth utilization rate, and the network state data is preliminarily analyzed through the data acquisition unit and is compared with historical data to determine the change trend of the current state. According to the invention, through a distributed management mode, a single-point fault problem in traditional centralized management is avoided. Each FTTR node can independently monitor the state of itself and work cooperatively with other nodes, even if a certain node breaks down, other nodes can still work normally, and the stability of the whole network is ensured.

Aspects of the subject disclosure may include, for example, a device including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of receiving a query for 5G Session Management Functions (SMFs) to handle a connection request, wherein the query includes a slice ID and data network name; determining a list of SMFs that each have a blockchain ID immutably recorded on a blockchain, wherein the blockchain ID is authorized to provide service to the slice ID; and sending, responsive to the query, the list of SMFs. Other embodiments are disclosed.

The invention belongs to the technical field of logistics information, and discloses a multimodal transport trusted data space construction method based on privacy calculation and a block chain. The method comprises the following steps: deploying a modular intelligent data gateway at a core node of a multimodal transport line, unifying the format of multi-source heterogeneous original data, and chaining a hash value; after the multi-source heterogeneous data is subjected to customization processing, key metadata attributes of all the data are registered and linked in real time; constructing a core ontology model, and establishing a cross-domain semantic interoperation capability; establishing a privacy calculation and block chain collaborative dual-channel architecture, and cooperatively training an ETA prediction model in a local data isolation environment by adopting transverse federal learning to ensure that original trajectory data is not out of a domain; constructing a dispute arbitration and credible verification system, and realizing an arbitration process of judicial verification when a transportation dispute occurs; data minimization disclosure and compliance sharing are achieved through dynamic access control. According to the method, multimodal transport data can be available and invisible, and the whole operation process can be audited.

The invention provides a financial risk real-time management method and system based on multi-source data and a block chain, and the method comprises the steps: carrying out the real-time collection of library table data, webpage data and log data, endowing different data sources with dynamic weights according to the importance and real-time performance of the data sources, and adjusting the weight values through an adaptive algorithm; performing right confirmation operation on each piece of data by using a block chain technology, and endowing the data with a block chain certificate; establishing a risk assessment model, and continuously optimizing and dynamically adjusting the risk assessment model by combining a reinforcement learning algorithm with a traditional machine learning algorithm; financial risks are monitored in real time through a built-in multi-dimensional early warning index system; and in combination with an AIGC technology, carrying out deep analysis on a risk prediction result, and automatically generating a detailed risk analysis report. According to the invention, through the dynamically adjusted and optimized risk assessment model, the real-time and accurate financial risk management and prediction capability is provided, and the security and transparency of data are ensured.

An edge enabler server of an edge data network is configured to receive a verification request comprising an edge enabler client identification (EEC ID), wherein the EEC ID uniquely identifies an edge enabler client (EEC), determine whether the EEC ID is an authorized EEC ID and provide a verification response based on whether the EEC ID is authorized.

This application discloses an access control method and an apparatus, which may be used in a secondary authentication process. The method includes: An SMF receives a session establishment request sent by a terminal. The SMF determines, based on the request, to send an authentication request to a campus server. The SMF receives an authentication failure response message sent by the campus server. When the SMF determines to accept the session establishment request, the SMF sends a policy association establishment request to a PCF and receives a policy association establishment response sent by the PCF. The SMF enforces a policy for a session of the terminal according to a policy rule in the policy association establishment response. The SMF sends a session establishment response message to the terminal. Although accessing a campus DN by the terminal is rejected during secondary authentication, the terminal is still allowed to continue to establish the session, and a related policy of a public network DN can still be enforced for the terminal, so that the terminal cannot initiate a campus service, but can still initiate a public network service, to meet a valid requirement of the terminal to access a data network.

A system described herein may receive a communication session request associated with a particular User Equipment (“UE”) and a network that includes a plurality of network slices. The communication session request may include a particular identifier, such as a Data Network Name (“DNN”) or other type of identifier. The system may output a request for network slice information, wherein the request includes the particular identifier; and may receive network slice information that indicates multiple network slices that were selected based on the particular identifier. A Unified Data Management function (“UDM”), Unified Data Repository (“UDR”), and / or other element of the network may provide such information. The system may identify network slice priority information that indicates relative priorities of the network slices, may select a particular network slice based on the network slice priority information, and may respond to the communication session request in accordance with the selected first network slice.

This disclosure provides systems, methods and apparatuses for discovery, by a user connected to a first network, of an edge application server (EAS) associated with a second network. In some aspects, techniques described herein provide a shared EAS discovery function (EASDF), which allows users connected to different home networks to discover EASs across a plurality of networks. Some techniques described herein provide communication between EASDFs of different networks. Thus, a user connected to a first network can discover an EAS of a second network, which enables sharing of EASs across networks. Furthermore, techniques described herein provide communication between a UE connected to a first network and an edge data network (EDN) (such as an EAS hosted by the EDN) of a second network, such as by establishing a connection between a user plane function of the first network associated with the UE and a network entity of the second network.

The invention discloses an intelligent operation sewage pipeline system and an implementation method thereof, and the intelligent operation sewage pipeline system comprises monitoring equipment, a cloud server, a GIS map model, a management platform and an operation management terminal which achieve data interaction through a 5G data network or the Internet. And the data is processed and analyzed by combining big data analysis, an intelligent algorithm and an image algorithm, so that prompting, decision making, renovation and evaluation of the defects of the sewage pipeline are realized. The specific implementation method comprises the steps of constructing the intelligent operation sewage pipeline system, arranging monitoring equipment, establishing a GIS map model, setting a management module, constructing functions of each module and the like. Effective scheduling of the sewage pipeline operation system and reasonable allocation of operation and maintenance resources are realized, and the efficiency and quality of sewage pipeline operation are improved. The problems of external water entry, sewage leakage, sewage overflow, road collapse and the like caused by sewage pipeline defects are solved.

Embodiments of the present disclosure includes methods for a client in an edge data network. Such methods include obtaining an initial access credential for a server in the edge data network, before accessing the server; providing the initial access credential to the server for authentication of the client; and obtaining an updated access credential for the server based on expiration of one of the following: the initial access credential, or a further access credential provided by the server; and providing the updated access credential to the server for authentication of the client. Other embodiments include complementary methods for a server and for a credential provider, as well as UEs, network nodes, and / or computing systems configured to perform such methods.

The invention relates to the technical field of garbage collection and transportation full scenes, and particularly discloses a garbage collection and transportation full scene data monitoring system which comprises a full scene data acquisition module, a dynamic collection and transportation queue generation module, a collection and transportation vehicle distribution module, a transfer station distribution feedback module and a database. Garbage collection full scene data is collected in real time through sensor groups of garbage cans, garbage collection vehicles and transfer stations, traffic road condition information is fused to construct a data network, regional demands are predicted based on historical data, a dynamic priority queue is generated in combination with a garbage amount exceeding threshold proportion, and a path is optimized through capacity matching and real-time road conditions, so that the real-time garbage collection is realized. And the congestion compensation duration is calculated, it is ensured that classified garbage is precisely matched with vehicle partitions, the transfer station capacity is dynamically calculated, and overload tasks are automatically distributed to adjacent stations. According to the method, the problems of data islands, static scheduling and mixed loading of a traditional system are solved, the resource efficiency and environmental protection benefits are remarkably improved, and the clearance efficiency and the service quality are improved.

The invention discloses a substation dispatching data network security communication method based on quantum tunnel encryption, relates to the technical field of large-scale power grid security assurance, and aims to solve the problems of endpoint security loss, boundary solidification, weak quantum threat resistance and inflexible deployment of a dispatching data network. According to the method, the establishment of a secure session and the encapsulation, encryption and restoration of data are realized by integrating quantum key distribution and a post quantum cryptographic operation module through quantum secure communication terminals deployed in a dispatching master station and a transformer substation. According to the method, application layer encryption and signature are carried out on telecontrol protocol data, a double-layer encryption tunnel is constructed, and transmission of various physical network media is supported. By means of the scheme, end-to-end protection is achieved, quantum attacks are effectively resisted, double encryption is provided, and networking flexibility and smooth evolution are improved.