211 results about "Network level" patented technology

The invention discloses a cross-domain collaborative interface dynamic configuration method and device and a computer program product. According to the cross-domain collaborative interface dynamic configuration method, service flow priorities are determined by analyzing service layer data, and an initial interface configuration scheme is generated in combination with network state information. And when the initial scheme does not meet the transmission requirements, optimizing the interface parameters by adopting a dynamic programming algorithm, generating a new configuration scheme, and issuing the new configuration scheme to the target network management system. In the service switching process, network performance is monitored in real time, interface parameters are dynamically optimized and updated to a network management system, and intelligent management of service flow and efficient utilization of network resources are achieved. The method can adapt to different manufacturer devices and network levels, guarantees the service quality of cross-domain services, and improves the operation efficiency and reliability of an electric power communication network.

A monitoring system is described for monitoring at least one slice of a communications network using at least one access network, an edge network and a core network. The system, comprises, for each slice, a plurality of intrusion detection modules configured to monitor elements associated with said section and comprising at least a first module for detecting intrusions at the access network level, a second module for detecting intrusions at the edge network level, and at least a third module at the core network level, each of the modules being configured to provide a piece of information representative of a local confidence level assigned to the section according to a behaviour of at least one element that it monitors. One of the third modules is additionally configured to evaluate, from the provided information, an overall confidence level for this section and to trigger an intrusion mitigation action for this section depending on the value of this overall confidence level.

The invention relates to the technical field of wireless communication, and particularly discloses a spectrum sensing method and device for a cooperative frequency switching network and a communication system, and the method comprises the steps: generating a service network air interface frequency utilization situation table according to a network-level cooperative frequency control instruction transmitted by a guard network; a frequency spectrum sensing control instruction is generated according to the service network air interface frequency use situation table, the frequency spectrum sensing control instruction is diffused through a guard network, and the service terminal guard module can sense and monitor a service network air interface according to the frequency spectrum sensing control instruction and a to-be-sensed frequency set; and sensing and monitoring a service network air interface according to the to-be-sensed frequency set. The spectrum sensing method of the cooperative frequency switching network provided by the invention can assist in improving the anti-interference capability of the cellular network.

The invention discloses a wireless sensor network node fault diagnosis system based on a multi-scale Bayesian network, and relates to the technical field of wireless sensor networks. The system comprises the following components: a data acquisition and preprocessing module which adopts sensor nodes distributed in a monitoring area to acquire environmental parameters according to a preset frequency, wirelessly transmits the environmental parameters to a data processing unit, performs preprocessing in a self-adaptive mode, and stores the processed data into a database; according to the method, a micro-scale Bayesian network model, a node-scale Bayesian network model and a sub-network three-scale Bayesian network model are constructed, so that cross-scale fault analysis from a component level to a network level is realized, the micro-scale captures fault association of internal components, the node scale evaluates the overall sensor performance, and the sub-network scale analyzes a cooperation state between nodes. The multilevel information interaction and fusion mechanism can comprehensively consider node internal fault propagation, external communication interference and subnet topology influence, and significantly improves the integrity and accuracy of fault diagnosis.

The invention discloses an object model access parameter dynamic optimization method and system based on reinforcement learning. According to the method, a multi-agent reinforcement learning model is constructed, a device-level state and a network-level state of the Internet of Things terminal device are used as environment state input, access parameters of the terminal device are modeled as an action decision of a reinforcement learning decision model, and the reinforcement learning decision model outputs optimized access parameters according to the current environment state. And continuously updating the strategy network through a reward function. And finally, the control platform deploys the trained strategy model, automatically selects the optimal access parameter configuration when the terminal equipment accesses, and adjusts the access strategy in real time according to the state change of the equipment and the network. Compared with an existing static strategy, the method has the advantages that efficient, low-time-delay and low-power-consumption access of the power internet-of-things terminal equipment can be achieved, and the system resource utilization rate and the communication reliability are remarkably improved.

The invention discloses a data message forwarding method and device in an SRv6 network, a medium and equipment. The invention belongs to the technical field of data transmission. The method comprises the following steps: after a data message is received, acquiring data identification information of the data message; analyzing the data identification information to obtain transmission demand information of data elements; mapping the transmission demand information to a network identifier, and packaging the transmission demand information to the head of a data message; and forwarding the data message according to the network identifier. By adopting the technical scheme, the equipment node at the network level can know the transmission requirement of the data element and forward the data element according to the transmission requirement, and the subsequent node can analyze the header content of the data message to obtain the transmission requirement, so that the data message forwarding link can meet the transmission requirement, and the transmission efficiency is improved. And the transmission requirement of the data element is perceived at the network level, so that the security and effectiveness of data transmission are ensured.

The invention relates to the technical field of optical access network equipment maintenance, and discloses a PON gateway fault self-checking method and a self-checking system, in particular to a PON gateway fault self-checking method integrating task priority scheduling, hardware abstraction layer adaptation, multi-gateway cooperative detection and intelligent reasoning diagnosis. Intelligent upgrading from single equipment detection to network-level fault positioning is realized by constructing a standardized detection instruction system, a time-space aligned cooperative detection network and a fault positioning model based on a Bayesian network.

The present disclosure relates to utilizing a hierarchical network communication system to efficiently manage and monitor a controlled hierarchical network. In particular, the hierarchical network communication system utilizes gateway services embedded among various architecture levels of a controlled hierarchical network to facilitate secure communications between levels of the hierarchical network as well as with an authorized external computing device or computing system. In various instances, the gateway service includes various components and elements that facilitate inter-network level communication as well as remote management, including monitoring, configuring, and upgrading components and resources at each level of the controlled hierarchical network. Indeed, the hierarchical network communication system facilitates the remote management of a controlled hierarchical network while upholding the strict security and communication protocols required for networks adhering to the Purdue Reference Architecture Model, ISA-95 standards, and ISA-99 standards.

The present disclosure provides solutions to registration and discovery of NFs in the vertical 5G networks, at the operator network, as well as handling tasks such as authentication of connecting end terminals at the operator network level. In one aspect, a method includes receiving, at network controller of an operator network and from an end terminal, a request for access to a network function (NF) of a vertical network, the request including a type of the NF in the vertical network; authenticating, at the network controller, the end terminal; upon authenticating the end terminal, identifying, at the network controller, the NF requested by the end terminal based at least on the type of the NF included in the request; and facilitating, by the network controller, access to the NF in the vertical network by the end terminal.

The invention discloses a self-adaptive distributed network integration system and method for virtual and real unmanned aerial vehicle cooperation, and relates to the technical field of unmanned aerial vehicle control, the system comprises a central control plane and at least one node agent, the central control plane comprises a global identity manager configured and distributed with globally unique network identity information, recording and recovering the network identity information; the network topology and state manager is configured to generate a virtual and real network state view, responds to query and issues a network simulation instruction to the node agent; the distributed network coordinator is configured to decide and coordinate the node agents to construct a cross-host logic two-layer network domain; the security policy manager is configured to define and store a network access control policy of the virtual unmanned aerial vehicle; the adaptive distributed network integration system and method aim to realize physical presentation of the virtual unmanned aerial vehicle on the network level, so that the virtual unmanned aerial vehicle can directly communicate with and collaboratively work with a real unmanned aerial vehicle in the same network plane.

The invention provides a repair processing method and system based on intelligent customer service, a medium and a program product, and relates to the technical field of electric digital data processing. The method comprises the following steps: acquiring repair information of a user, determining an associated repair information set, extracting geographic position information and fault phenomenon keywords of associated repair points, and generating a three-level pipe network association map; obtaining time-phased water consumption data in the map in real time, and determining an abnormal water consumption area; acquiring hardware state data of an abnormal area, and determining a fault type in combination with pressure fluctuation and a sound wave spectrum; and according to the fault type, generating an intelligent work order including the fault type, the geographic coordinate, the pipe network level and the maintenance tool accessory list. By implementing the method, the intelligence and precision of repair processing are realized.

The invention provides an automatic testing method based on a traffic copy playback strategy and a code interception technology. The method is characterized by comprising the following steps: S1, establishing a production environment snapshot; s2, copying the traffic; s3, performing simulation reconstruction on the playback environment context; s4, performing comparison verification on write operations of the database, and printing inconsistent places into a report for analysis; s5, optimizing a traffic replication and playback strategy; the method comprises the following steps: establishing a production environment snapshot, and recovering a test environment by using the snapshot in the test environment; and meanwhile, instrumentation does not need to be carried out in a production environment, only write request traffic of a service interface needs to be intercepted and recorded through a network level, and consistency of context and production of program operation is ensured through a code interception technology, so that consistency of an operation process and production is ensured, and a consistent result is generated.

The embodiment of the invention discloses a generative content risk prediction method and device, a storage medium and electronic equipment.The method comprises the steps that firstly, a risk problem text is obtained, then the risk problem text is input into a large language model for content generation, and internal representation of a middle network hierarchy of the large language model in the content generation process is extracted; and finally, based on the internal representation, determining the risk content output probability of the large language model in the content generation process by utilizing a target classifier. By predicting generation of the risk content in advance and performing risk management and control in time, the risk can be prevented from being exposed to the user, and the use experience of the user is improved.

The invention discloses a multi-type split part splicing error detection and position correction method based on a two-stage cascade network, and the method comprises the steps: 1, carrying out the data collection and processing of multi-type split parts, and enabling the data to serve as the input of two-stage network cascade training of a MoE-RT-DETR network and an HRNet network; 2, performing model training based on a two-stage cascade network; step 3, reasoning based on the trained two-stage network model, extracting pixel coordinates of key points, and converting the pixel coordinates into physical world coordinates through preloaded industrial camera calibration parameters; and step 4, carrying out splicing error detection based on the converted physical coordinates, calculating a key point difference value and guiding position correction adjustment. The invention further discloses a detection and position correction system for implementing the method, and the detection and position correction system has a wide application scene.

The invention provides a low-power-consumption cooperative sewer water body integrated monitoring method. The method comprises the following steps: carrying out model selection and deployment on sensing node hardware of an adaptive scene; a low-power-consumption internet of things network topology and power supply design; a time-sharing triggering acquisition strategy of the multi-modal data is carried out; node-level environment self-adaptive data calibration is carried out; node-level flow velocity-stink association anomaly recognition is carried out; carrying out network-level data fusion and validity verification; collaborative traceability, diffusion simulation and linkage control are carried out. According to the invention, the problems of data distortion, difficulty in source tracing of pollution sources, short equipment endurance and response lag caused by independent collection of flow and stink and environmental interference in traditional sewer monitoring are solved; the integrated effect of ultrasonic Doppler and electronic nose cooperative monitoring, environment self-adaption accurate calibration, odor source rapid positioning and diffusion simulation and low-power-consumption and long-endurance linkage control is achieved.

A Smart Service Analyzer obtains Call Direct Record (CDR) Data from Probing Devices of a mobile network. The CDR Data is processed at a Key Performance Indicators (KPI) Generator to generate User Level KPIs. The User Level KPIs are provided to a Customer Experience Index (CEI) Estimator. Machine Learning is applied to the User Level KPIs at the CEI Estimator to generate Generalized User Level CEI Estimates. The Generalized User Level CEI Estimates are provided to a Service Quality Index (SQI) Estimator. Machine Learning is applied to the Generalized User Level CEI Estimates at the SQI Estimator to generate Network Level SQI Estimates.

The invention provides a video high-definition coding and low-delay transmission method for an IPTV (Internet Protocol Television) large-screen short play, which relates to the technical field of low-delay transmission, and comprises the following steps: defining an end-to-end measurement boundary and a time reference, configuring an acquisition mechanism of a network environment real-time state index, extracting high-definition coding metadata by adopting a coding end, and transmitting the extracted high-definition coding metadata to a transmission end; and timestamp information is marked and a unified structured operation state object is configured. A short window prediction path and a middle window prediction path are configured, confidence interval correction and lower limit fusion are combined to form a conservative available bandwidth estimation value, and when a network level label is judged to be a weak network level, a protection buffer mode and a low code rate pre-pulling strategy are triggered. The mechanism effectively solves the problems of frequent playing lagging and overhigh switching delay in a weak network environment in the prior art, so that a short play can still keep stable and continuous playing experience in a complex network environment.

The invention relates to the technical field of industrial internet network security, and discloses an industrial internet network security situation awareness analysis method and system, and the method comprises the steps: carrying out the deep comparison analysis of a network instruction, an actual production process state, equipment operation conditions, material characteristics, final product quality requirements, and other information; instructions which seem legal in the network level but have potential destructiveness in the process level can be identified. For example, for a hidden attack which slowly destroys the production process through small parameter tampering or suboptimal path selection, the method can find and give an alarm in time through comparison of a simulation result and a safety threshold, thereby avoiding false alarm and a safety blind area caused by a fixed reference behavior mode of a traditional system. Therefore, the accuracy and effectiveness of industrial internet network security situation awareness are remarkably improved, and the method is particularly suitable for complex industrial environments such as high-precision aviation component manufacturing and the like which have extremely high requirements on the production process and the product quality.

Systems and methods are disclosed that relate to Key Performance Indicator (KPI)-driven alarm threshold optimization using machine learning. In one embodiment, a computer-implemented method comprises obtaining network-level KPI data for a network and radio log data for one or more radio systems in the network. The KPI data comprises KPI values for one or more network-level KPIs, and the radio log data comprises a number of faulty or uncalibrated antenna branches in the radio system and cell or user beamforming weights. The method further comprises pre-processing the KPI data and the radio log data, labeling the pre-processed KPI data as degraded or non-degraded, and training, with the labeled KPI data and the pre-processed radio log data, a fault classifier Machine Learning (ML) model to output a value(s) that represent a probability that the KPI(s) will be degraded for a given input feature set.

A method and system are provided at a local network level and a remote cloud level. The local network level includes compressors and a controller configured to create a short-term schedule having a short-term switching sequence for operation of the compressors, perform a validation assessment on the short-term schedule, and send the short-term schedule to a platform embedded with the main controller of the system. A prediction model, a long-term scheduling approach, and cloud storage that stores measurements from the compressors and executable instructions are provided at the remote cloud level. The long-term schedule is transferred to a safety check module at the local network level. Following validation, the embedded platform refines the long-term switching sequence and the safety check module allows the system to implement the long-term schedule, having the short-term schedule as a backup option.

The invention relates to the technical field of cross-network data management, in particular to a cross-network data aggregation method and system based on holographic perception, and the method comprises the steps: obtaining a network field path identifier, a grade and a time period, accumulating and sorting access frequencies, analyzing the frequency and grade combination distribution, extracting a field name, a network grade and a time label, and matching and comparing the frequency difference. And counting field hopping frequency, marking, and matching field paths and channel numbers to obtain cross-network fusion data sequence output. According to the method, through dynamic grouping, frequency analysis and fusion positioning of the data fields under the network level and the time dimension, the field distribution rule identification precision is improved, and the affiliation perception consistency is enhanced. And by utilizing residual error ratio screening and path sequence matching, the field screening precision and the transmission sequence structural property are improved, and synchronous enhancement of data fusion output efficiency, transmission stability and resource configuration coordination in a multi-network environment is realized.

Approaches are described for providing quality of experience (QoE) driven in-transport communications services. For example, in-transit communications services are provided over a network to content consumption devices on one or more transport craft. During a measurement window, delivery of the in-transit communications services can be measured to obtain network-level delivery (NLD) measurements, and consumption of the in-transit communications services by one or more of the content consumption devices can be measured to obtain customer-level consumption (CLC) measurements. The NLD and CLC measurements can be used to compute a QoE score that indicates a delivered quality of service as perceived by one or more passengers of the one or more transport craft. The QoE score can be used to update stored service level data. The update can cause generation of one or more service level triggers, which can trigger automatic remedial action to address QoE-related conditions.

The invention belongs to the technical field of computer vision and artificial intelligence, and particularly relates to a semantic image segmentation method based on hierarchical neural architecture search, which is characterized in that efficient architecture optimization is realized by adopting a differentiable search algorithm by constructing a double-layer search space comprising a unit level and a network level. 12 kinds of heterogeneous operations are designed in a unit-level search space, including a multi-scale context extractor and boundary sensing convolution; and a resolution self-adaptive adjustment mechanism is realized in a network-level search space. Through a two-stage gradient optimization strategy, search is completed within three GPU days, the decoded model reaches 80.4% mIoU on a Cityscapes data set, and the parameter quantity is only 10.15 M. Compared with the prior art, the method has the advantages that the segmentation precision and the model efficiency are remarkably improved while efficient search is kept, and the method can be widely applied to the fields of automatic driving, medical image analysis and the like.

The invention discloses an urban rail transit network level signal professional data management and interface method, and relates to the technical field of urban rail transit signal professional data management. A data resource pool platform is established between an online network signal professional communication front-end processor and an external interface; the data resource pool platform comprises a data access module, a data transmission module, a data storage module, a data subscription module and a data service module; the data access module is used for acquiring signal data from a network signal professional communication front-end processor, the data transmission module establishes labels for the signal data and performs classified storage, the data storage module performs standardized processing on the signal data and then stores the signal data, and the data subscription module manages an external interface and performs external interface subscription content configuration. And the data service module performs identity verification on the external interface and provides data. According to the method, the intelligence level and decision-making efficiency of line network operation are remarkably improved, and a core technical guarantee is provided for safe and efficient operation of urban rail transit.

Implementations for chiplet fault management through hierarchical error reporting and coordinated recovery in an accelerator interconnect. In some implementations, a system for a UALink-based network comprises a chiplet die coupled to an accelerator die, a management agent on the accelerator die, and a centralized controller. Upon fault detection, the chiplet die transmits a fault notification to the accelerator die, the management agent reports the fault to the centralized controller, and the centralized controller determines a recovery action based on the fault identifier and severity. In some implementations, the accelerator die reports the fault through dual paths, to a host processor via CXL or PCIe error reporting and to the centralized controller via the management agent, and the host processor and the centralized controller coordinate recovery, with the host managing workload-level recovery and the centralized controller managing network-level recovery.

The application discloses an APT flow detection method based on a knowledge graph, which comprises the following steps: analyzing network data flow characteristics in network flow, establishing a knowledge graph of network data flow, and classifying the established network flow knowledge graph by using a graph neural network algorithm to detect APT attack flow in the network, so that the attack behavior of an APT organization can be detected from the network level. The application can detect unknown APT attack network data flow, and is helpful for flexibly and accurately detecting APT flow.

The invention discloses a distributed system and method for providing end-to-end deterministic time delay guarantee, a terminal and a storage medium. The system comprises a super object model time sequence extension module, a global time synchronization module and a distributed deterministic scheduling module. The super object model time sequence extension module is used for defining distributed tasks or equipment capabilities in the system, and the global time synchronization module is used for synchronizing clocks of whole network equipment by using a high-precision synchronization protocol to obtain a unified time reference; the distributed deterministic scheduling module is used for planning a scheduling period of a hard real-time task based on an end-to-end time sequence requirement and a time reference, calculating an end-to-end communication path meeting the time sequence requirement of the hard real-time task according to a real-time network state and the scheduling period, and scheduling the hard real-time task based on the time reference. According to the invention, resource reservation is carried out on an end-to-end communication path on a network level and an operating system kernel level, a service layer time sequence demand is associated with bottom layer network scheduling, and end-to-end microsecond-level deterministic guarantee is realized.

The invention discloses a method, device and equipment for establishing a stacking system, a storage medium and a program product, and belongs to the field of communication. The method comprises the following steps: receiving a stacking system building instruction triggered by an administrator, and responding to the stacking system building instruction to build a stacking system on the basis of a topological connection relationship among a plurality of network devices, a network hierarchy where the plurality of network devices are located and a network hierarchy where a neighbor device of each network device in the plurality of network devices is located; and generating stack configuration parameters corresponding to the plurality of network devices, and sending the respective corresponding stack configuration parameters to the plurality of network devices, so that the plurality of network devices are configured according to the respective corresponding stack configuration parameters and establish a stack system. According to the method and the device, automatic configuration of the stacking configuration parameters can be realized, manual configuration is not needed, and a special stacking cable does not need to be purchased independently for wiring, so that the steps of building the stacking system are effectively simplified, and the cost is greatly reduced while the efficiency of building the stacking system is improved.

The invention belongs to the technical field of unmanned aerial vehicle countering, and discloses an unmanned aerial vehicle detection countering method and system. The method comprises the following steps: analyzing motion behaviors of the unmanned aerial vehicles based on spatial position information, collected in real time, of N unmanned aerial vehicles to obtain a behavior level data set; analyzing the network communication of the unmanned aerial vehicles based on the network data of the N unmanned aerial vehicles collected in real time to obtain a network level data set; performing threat diagnosis analysis on the N unmanned aerial vehicles based on the behavior level data set and the network level data set to obtain threat levels of the N unmanned aerial vehicles; performing intelligent countering on the unmanned aerial vehicles based on the behavior level data set, the network level data set and the threat levels of the N unmanned aerial vehicles; according to the invention, through a linkage mechanism of behavior-communication double-layer analysis, threat level region division and parameterized countering control, unification of accurate identification and intelligent countering of the unmanned aerial vehicle is realized.

The present disclosure relates to utilizing a hierarchical network communication system to efficiently manage and monitor a controlled hierarchical network. In particular, the hierarchical network communication system utilizes gateway services embedded among various architecture levels of a controlled hierarchical network to facilitate secure communications between levels of the hierarchical network as well as with an authorized external computing device or computing system. In various instances, the gateway service includes various components and elements that facilitate inter-network level communication as well as remote management, including monitoring, configuring, and upgrading components and resources at each level of the controlled hierarchical network. Indeed, the hierarchical network communication system facilitates the remote management of a controlled hierarchical network while upholding the strict security and communication protocols required for networks adhering to the Purdue Reference Architecture Model, ISA-95 standards, and ISA-99 standards.