927 results about "Network service" patented technology

A flow tagging technique includes tagging a data flow at a plurality of points in the data flow. For example, the data flow can be tagged at a socket and at a proxy manager API. By tagging the data flow at multiple points, it becomes possible to map network service usage activities to the appropriate initiating applications.

The invention relates to the technical field of network communication, in particular to a multipath transmission load balancing method and system based on network state analysis. The method comprises the steps of collecting network state information of each transmission link from network equipment, calculating and obtaining the weight of each transmission link according to the collected network state information, setting a flow distribution strategy according to the weight obtained by calculation of each transmission link, and distributing data flow to each transmission link in proportion, and collecting the network state information of each transmission link in real time, updating the weight of each transmission link according to the change of each parameter in the network state information, performing threshold judgment on the updated weight, and dynamically adjusting the flow distribution strategy according to the weight meeting the threshold requirement. According to the method, the defects of a traditional static strategy can be effectively overcome, link congestion or idle is avoided, high efficiency and stability of data transmission are guaranteed, and finally the requirement of a user for high-quality network service is met.

Data loss prevention systems and methods in an enterprise data management and monitoring system may intercept a request to a network service, e.g., a service using an artificial intelligence and / or machine learning model. The systems and methods may represent contents of the request via one or more vector embeddings, which may be compared to vector embeddings corresponding to respective ones of a plurality of sensitive data elements in the enterprise. The data loss prevention system and methods may apply various data sensitivity policies based on determinations of whether sensitive data of the enterprise is included in the request to the network service, e.g., by blocking or redacting the request to prevent exposure of the sensitive data to the network service.

The invention provides a dynamic reconstruction optimization method and system for communication information network topology, and relates to the technical field of communication networks, and the method comprises the steps: firstly obtaining node operation dynamic information, link transmission real-time information and network service demand dynamic data of a communication information network; generating a network topology elastic association body containing a plurality of association rules and logics based on the two; inputting the service demand data into a network topology elastic association body to generate a network topology dynamic demand adaptation factor; generating a network topology reconstruction path self-adaptive sequence containing a specific operation instruction and a time sequence based on the network topology dynamic demand adaptation factor and the network topology elastic correlation body; and adjusting a node connection mode and link transmission configuration in a linkage manner according to a network topology reconstruction path self-adaptive sequence, and synchronously updating an association rule to complete dynamic reconstruction optimization. According to the invention, the network stability, reliability and resource utilization rate are improved.

The invention relates to the technical field of IT support, and provides a time sequence processing method, device and equipment adopting a quantum pulse neural network, and the method comprises the steps: obtaining time sequence data which comprises network alarm data, network equipment performance index data and network operation and maintenance work order data; encoding the time sequence data into a first quantum state by using a quantum preprocessing layer, inputting the first quantum state into a pulse neural network layer, converting the first quantum state into a time sequence pulse sequence, and processing the time sequence pulse sequence to obtain an output result; by utilizing a quantum attention enhancement mechanism, calculating attention weight of an output result in a quantum state space, and weighting to obtain a second quantum state; and decoding the second quantum state by using the hybrid decoding layer to obtain a final prediction result. The final prediction result is used for realizing fault root cause positioning, abnormal work order identification or network service quality prediction. According to the method, the parallelism of quantum calculation and the superposition characteristic of the quantum state are utilized, and the calculation efficiency can be improved when large-scale time sequence data are processed.

Disclosed are various approaches for large language model (LLM) application risk mitigation. A large language model (LLM) application that interacts with a network LLM service can be identified. A portion of the LLM application can be provided as input to an LLM risk mitigation code generation function that outputs LLM-specific risk mitigation code. A runtime environment can be deployed to include a modified version of the LLM application that includes the LLM-specific risk mitigation code, a kernel-layer LLM risk mitigation program that can intercept LLM interaction system calls to apply the LLM-specific risk mitigation code, or any combination thereof.

A processing system including at least one processor may obtain a client intent for a network service in a communication network. The processing system may next apply an input vector comprising the client intent to a generative machine learning model to obtain an output of the generative machine learning model comprising a network configuration. The processing system may then reconfigure the communication network according to the network configuration.

The invention discloses a network service fault detection method, device and system, electronic equipment and a storage medium. The core network management device obtains a detection service, constructs a detection task according to the detection service and distributes the detection task to at least one edge network device, the edge network device executes the detection task to obtain a detection result and reports the detection result to the core network management device, and the core network management device analyzes the detection result and sends the analysis result to the detection server. And generating a whole network service state report. A complex detection task is decomposed into a distributed architecture of core definition and edge execution, the core network management device is responsible for definition and management of complex service logic, the edge network device executes a specific detection task, the architecture of service and data separation greatly reduces the performance requirement for the edge network device, and the service and data separation efficiency is improved. Therefore, common network equipment can complete complex service detection, high cost of special high-performance probe equipment is avoided, and hardware investment is remarkably reduced.

A container management module or a configuration management module changes a state of a network service for providing a communication service. An AI / big-data processing module calculates an index value indicating a use status of the communication service dependent on the state of the network service in a status before the change of the state at present or at a future time based on: an actual result value of a use status of the communication service independent of the state of the network service at a timing after the change of the state; and the state of the network service before the change of the state. A policy manager module determines whether the calculated index value satisfies a predetermined condition.

Various embodiments herein provide techniques to enable communication between a user equipment (UE) microservice and a microservice of a wireless cellular network via service mesh. A first solution is described, in which the service mesh is in the network, and the network includes a service mesh proxy to communicate with the UE. A second solution is also described, in which the UE is part of the cellular network service mesh and includes a service mesh proxy in the UE. Other embodiments may be described and claimed.

A method and system for providing network services in a region involves managing handovers (HO) between a source Public Land Mobile Network (PLMN) and a target PLMN. User Equipment (UE) receives radio signals from both PLMNs and receives / determines at least a Signal to Interference & Noise Ratio (SINR) and Reference Signal Received Power (RSRP) of the radio signal. A determination is made whether a HO is permitted or prohibited based on whether the target RSRP and SINRs from both PLMNs exceed certain thresholds. Additional criteria for permitting or prohibiting HO include cell load conditions. Predictive models, potentially using Artificial Intelligence / Machine Learning (AI / ML), may predict HOs based on roaming data. The PLMNs may operate across various cellular technologies, including GSM / 2G, UMTS / 3G, LTE / 4G, or NR / 5G.

Aspects of the subject disclosure may include, for example, receiving, over a network, a registration request (e.g., an IMS registration request) from a communication device; obtaining, by the network device over the network from a network server, information indicating enabled services associated with the communication device; and providing, by the network device over the network, a request successful message to the communication device, where the request successful message includes the information indicating the enabled services. Other embodiments are disclosed.

The invention relates to the field of network card firmware repairing and upgrading, in particular to a network card firmware hot patching method, a storage medium and electronic equipment. According to the method, a patch file is received and loaded to a reserved area of a network card SRAM (Static Random Access Memory), an address acquisition logic is executed to generate a patch symbol table, a function to be updated is determined by matching a function jump table, address pointing information is updated to point to a target function code in the SRAM, and indirect calling based on the jump table is realized. According to the method, memory resource waste is solved through function granularity updating, address accuracy is ensured through dynamic symbol table matching, non-inductive switching of services is achieved through an indirect calling mechanism, equipment does not need to be restarted, and the continuity and reliability of network services are improved.

The invention discloses a one-way off-line security intelligent decision center system with local deployment as a main and external network service as an auxiliary and a control method, and belongs to the technical field of artificial intelligence security and edge intelligent decision. A three-level security domain physical and logic dual isolation architecture is adopted, extranet multi-model intelligent security enablement is performed on a local core decision domain through a physical one-way import channel, the local core decision domain completes reasoning calculation in combination with an enablement result in a non-extranet interaction state, a top-layer task scheme is generated, and a top-layer task scheme is generated. Through a multi-mode man-machine interaction mode, after human rigid authorization, issuing to an intranet equipment scheduling domain for execution, and feeding back a state in real time to form a full-link closed loop; the method realizes external network multi-model intelligent security enabling, local agent professional decision and rigid closed loop dominated by human beings in the whole process, aims to solve the problems of cloud dependence, data leakage, out-of-control AI, high cost, poor adaptability and the like, and is suitable for families, industries, confidential scenes, field non-support operation and other scenes.

Systems and methods for managing resources across a global and / or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are offline (or may potentially go offline). For example, the systems and methods may mitigate issues related to providing services while resources are offline (or may potentially go offline) by monitoring network services at an aggregate level.

The invention relates to a deployment recommendation method and device of wireless access equipment and computer equipment. The method comprises the following steps: acquiring deployment demand information of a user and physical deployment conditions of wireless access equipment in a target deployment area; according to the deployment demand information, screening out a candidate deployment scheme containing a network service package and matched wireless access equipment from a pre-constructed information base; the information base comprises a plurality of network service packages and equipment information of a plurality of wireless access equipment; for each candidate deployment scheme, determining a deployment position and a communication quality index value of each wireless access device under the candidate deployment scheme according to the physical deployment condition; and displaying each candidate deployment scheme, the deployment position corresponding to each candidate deployment scheme and the communication quality index value to the user. By adopting the method, the deployment efficiency can be improved, and collaborative optimization between the network communication quality and the economic cost is realized.

Systems and methods for managing resources across a global and / or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are off-line (or may potentially go off-line). For example, the systems and methods may mitigate issues related to providing services while resources are off-line (or may potentially go off-line) by monitoring network services at an aggregate level.

A method is proposed for routing modification in a service system of a communications network, the method comprising, during a direct communication between a first service and a second service: a conditional establishment of an indirect communication between said services, via an intermediary service. A corresponding module is also proposed.

The invention relates to a personalized network guarantee service pushing method and device, equipment and a storage medium. The method comprises the steps that a specific scene where a user is located is recognized through a terminal side intention perception SDK, credibility verification is carried out, and the specific scene comprises at least one of access to a preset place network, poor network quality or starting of a high-network-demand application; based on a specific scene in which the user is located, obtaining signing state data of a current service of the user; comprehensively analyzing the user service demand and the network adaptation capability based on the signing state data of the current service of the user; formulating or updating a personalized QoS (Quality of Service) network guarantee strategy matched with the specific scene, or pushing a personalized network guarantee service ordering request to a user, and performing state feedback; and executing a network service guarantee operation according to the network guarantee strategy. By adopting the method, precise scene perception and network side multi-network element cooperation can be realized, and the reach rate and the order conversion rate of the network guarantee service are improved.

Artificial intelligence (AI) techniques for connection networking are described. A method comprises generating a first training prompt based on a set of guidelines for a network service of a connection network system, the guidelines defining an objective for the network service, sending the first training prompt and a first set of training datapoints from a first training dataset to a first generative AI model, a training datapoint from the first set of training datapoints comprising a content item, receiving a second set of training datapoints for a second training dataset from the first generative AI model, wherein a training datapoint of the second training dataset comprises a first label for the content item generated by the first generative AI model based on the objective, and training a second generative AI model using the second set of training datapoints based on the objective. Other embodiments are described and claimed.

A cell reselection method includes: determining, according to service time determined based on auxiliary information, an operation of performing cell reselection, wherein the service time is time in which a non-terrestrial network (NTN) service cell can provide service to a terminal, and the auxiliary information includes at least one of coverage information of the NTN service cell, ephemeris information of the NTN service cell, or information of reference service time in which the NTN service cell can provide service.

The invention provides a network RTK base station coordinate precision detection method and system, and the method comprises the steps: carrying out the base station networking based on a Delaunay triangulation algorithm, and determining a baseline list; obtaining satellite precision orbit information, selecting a to-be-solved baseline vector from the baseline list, and resolving the to-be-solved baseline vector by adopting a fast static positioning algorithm to obtain a preset high-precision relative coordinate result; the preset high-precision relative coordinate result is converted into coordinate values of all reference stations, the coordinate values of all the reference stations are compared with preset known coordinate values in a system, coordinate difference values of all the reference stations on all the base lines are obtained through calculation, gross error elimination is conducted based on the coordinate difference values, and processed coordinate difference values are obtained; and integrating the processed coordinate difference values of each reference station on the plurality of base lines, performing consistency analysis and statistical judgment, determining the reference station with abnormal coordinates in combination with a preset threshold value, and outputting a diagnosis alarm result. According to the invention, the reliability and precision of the network RTK service are effectively guaranteed.

The invention relates to the field of geographic information system data processing, in particular to a vector tile cutting and publishing system which comprises a heterogeneous data self-adaptive access and normalization subsystem used for receiving multi-mode heterogeneous input; the spatial-temporal feature analysis and index construction middleware is used for generating a metadata index reflecting data spatial distribution features; the vector tile streaming production engine drives a tile generation process based on the metadata index, converts the intermediate state data into a vector tile data packet by adopting a parallel computing architecture, and writes the vector tile data packet into a tile storage warehouse; and the standardized service publishing bus is used for monitoring the change state of the tile storage warehouse in real time and externally providing a vector tile network service interface which accords with a standard protocol. According to the method, the structure barriers of heterogeneous data sources are eliminated, the stability of data production is ensured through topology self-healing, the defect that one set of rules cut all is overcome through a density-based parameter inversion mechanism, and the tile size and the visualization precision are balanced.

The invention discloses a building cluster cloud-edge collaborative optimization control method and device and an application architecture. The architecture comprises an edge device, a radio frequency communication LoRa module and a cloud platform. The edge device and the cloud platform realize cloud edge two-way communication through the LoRa module; the edge device comprises a sensing layer, an edge calculation core and a control layer and is used for collecting real-time building data, executing a local model predictive control algorithm MPC based on a building thermodynamics RC model and generating an optimal control instruction to drive air conditioning equipment; the cloud platform comprises a network service interface module, an optimization algorithm module and a data storage system and is used for operating a global coordination algorithm based on an alternating direction multiplier method (ADMM) and generating differentiated price signals; the edge device uploads data such as power requirements to the cloud platform, the cloud platform issues differential excitation signals to the edge device, and the edge device and the cloud platform perform collaborative iteration. According to the invention, distributed optimization control of the building cluster is realized through cloud-side cooperation, and the value of the building cluster as a power grid flexible resource is significantly improved.

The invention discloses a discrete service flow predictive digital triggering method and system under time delay constraint, and relates to the field of network service quality assurance, and the method comprises the steps: collecting an arrival time sequence, a packet length sequence, network state data and service level protocol parameters of a plurality of discrete service flows; performing multi-scale dependency decomposition on the arrival time sequence, and predicting an arrival amount and a queuing length in a future time window; calculating and predicting a residual time delay budget in combination with the maximum tolerant time delay, and mapping the residual time delay budget into a digital triggering probability representing the scheduling urgency degree; and further fusing the stream importance index to determine the scheduling weight, and dynamically adjusting the service sequence and resource allocation of the edge nodes according to the scheduling weight. Therefore, advanced scheduling triggering of multiple paths of service flows is realized, and the time delay guarantee capability and the resource scheduling efficiency are improved.

The invention discloses a multi-modal network architecture, a multi-modal data transmission method, a storage medium and an electronic device, and relates to the technical field of network communication, the architecture comprises a modal deployment management and control system, a multi-modal controller, a multi-modal network element and a cloud platform, the modal deployment management and control system is connected with the multi-modal controller and the multi-modal network element, and the cloud platform is connected with the multi-modal controller and the multi-modal network element. The multi-modal controller and the multi-modal network element are deployed on the cloud platform; the modal deployment management and control system is used for performing centralized control and modal issuing on the cloud platform which is in network intercommunication with the modal deployment management and control system; the multi-modal controller is used for managing a local multi-modal network element in the cloud platform; the multi-modal network element is used for forwarding the multi-modal message on the data level in the multi-modal network architecture, and by adopting the technical scheme, the technical problem that a traditional cloud service network protocol is single and cannot flexibly adapt to diversified network service requirements is solved.

The invention discloses an intelligent recommendation method based on a large model fusion computing network service, and relates to the field of artificial intelligence and large model application crossing. Comprising the steps of 1, converting an original demand input by a user into a structured computing network demand index, 2, collecting and integrating state information of cloud resources and network resources in real time, and providing dynamic computing network resource data for large model reasoning, 3, carrying out deep matching on the user demand and the computing network resources, generating a candidate scheme set of multi-objective optimization, and carrying out multi-objective optimization according to the candidate scheme set of the multi-objective optimization. 4, optimizing a candidate scheme set output by the large model, and 5, collecting user feedback and scheme execution data, and continuously optimizing the large model and a recommendation strategy.

Techniques, apparatuses, and systems for facilitating efficient use of Unified Data Management functions, UDMs, and Unified Data Repositories, UDRs, that support centralized storage of any-UE-data related to a network service, where any-UE-data is data relating to service requests for the network service for all user equipments, UEs, in a wireless network. An example method, in a network exposure function, NEF, in a wireless network, includes discovering one or more UDMs that support centralized storage of any-UE-data related to a network service. The example method further includes, responsive to discovering the one or more UDMs, sending, to only one UDM among the discovered UDMs, a request for subscription to the network service.

This application provides a communication method and a communication apparatus. The method includes: a first NRF receiving first query information from a first SEPP, the first query information including identification information of a target network element, the target network element supporting cross-network services, the first query information being used to request information about the target network element, wherein the first NRF, the target network element, and the first SEPP belong to the home network; the first NRF sending the target network element information to the first SEPP based on the first query information. The method provided by this application can improve the success rate of service execution.