1800 results about "Network on" patented technology

The invention discloses an adaptive game-driven network defense method and system, and relates to the technical field of network security. The method comprises the following steps: generating a multi-modal bait according to network context information, and screening an optimal bait through credibility evaluation; constructing a time sequence feature tensor according to the network event sequence information of the bait, and obtaining predicted attack information by adopting a pre-trained attack prediction model; combining the network event sequence information of the bait and the predicted attack information to construct a defense income matrix, and carrying out iterative equilibrium solution to obtain an optimal strategy candidate pool; and taking the defense hybrid strategy of the optimal strategy candidate pool as an initial population, performing multi-objective optimization through a non-dominated sorting genetic algorithm to obtain a Pareto optimal strategy set, and performing screening to obtain an execution strategy set for dynamic defense decision making. According to the invention, the dynamic property, intelligence and self-adaptability of network defense are realized, and the ability of a network system to cope with complex attacks is effectively improved.

An efficient heterogeneous federated learning method based on hybrid distillation includes: initializing, by a server, global model parameters, and setting a preset total number of training rounds and a number of clients participating in each of the training rounds; loading local datasets in the clients respectively, performing random transformations on the local datasets to generate client distillation data for the clients, sampling multiple sub-networks from an original network of each client, training each sub-network on the client distillation data to obtain updated local model parameters of each client, and uploading the updated local model parameters to the server; and receiving, by the server, the updated local model parameters, performing, by the server, server distillation based on the updated local model parameters and a preset auxiliary dataset to obtain updated global model parameters and an updated global model, and sending, by the server, the updated global model to the clients.

The invention provides a smart power grid cloud edge collaborative heterogeneous data security access method, which comprises the following steps: collecting multi-source heterogeneous power equipment data in real time based on a deployed multi-mode sensor network; based on a lightweight federated learning edge inference engine deployed on an edge end network, performing adaptive semantic enhancement preprocessing on the multi-source heterogeneous power equipment data to generate an edge encryption feature tensor; performing three-dimensional credible security authentication and knowledge fusion on the edge encryption feature tensor based on an electric power space-time knowledge graph fusion engine deployed on the cloud side to generate secure and credible electric power data; and performing space-time causal reasoning and twin mirror image comparison on the secure and credible power data based on a federated block chain-driven digital twin decision engine deployed at a cloud end to generate a dynamic security policy map and store the dynamic security policy map. According to the scheme, the data security is enhanced, intelligent analysis and dynamic decision can be performed according to the real-time state of the power grid, and optimal scheduling and stable operation of the power grid are realized.

The present invention relates to the technical field of energy meter electrical parameter measurement. Disclosed is an intelligent anti-interference system for a power line carrier energy meter. The system comprises: collecting voltage and current signals of a power line, and constructing a sample database; performing time-frequency feature classification by means of a preset model, and establishing feature and scenario mapping by synchronizing scenario recognition results; building a scenario interference recognition network on the basis of the mapping result; collecting real-time data of N communication channels of a power line carrier energy meter, and performing channel coordination decision and anti-interference enhancement; performing communication data evaluation on the basis of a comprehensive scoring function; and performing anti-interference management on the basis of channel access decision, anti-interference enhancement, and a parallel access result. The present invention solves the technical problem of poor accuracy and reliability of metering data of existing power line carrier energy meters caused by interference signals and noise when the power line carrier energy meters are in complex electromagnetic environments, and achieves the technical effect of improving the anti-interference capability and the reliability of the power line carrier energy meters.

The invention discloses a space-time adaptive power prediction system and method for distributed photovoltaic power generation in a mountainous area, and particularly relates to the technical field of fault prediction and health management. The method is used for solving the problem that the accuracy of power prediction and equipment health state evaluation is influenced by environment characteristic data reconstruction reference drift caused by equipment performance degradation in the prior art. A spatio-temporal data sequence is constructed by acquiring historical data of a photovoltaic unit, a spatio-temporal association network of generated power among equipment is constructed, performance stability of a reference equipment group is evaluated, and when the stability does not meet conditions, a performance attenuation spatio-temporal mode is analyzed to identify common features and personalized features. The propagation path of performance degradation on the space-time correlation network is analyzed based on the characteristics, a critical point is evaluated, reference correction is performed on the environment characteristic data by using the common characteristics and the critical point, and finally, the corrected environment characteristic data is used for executing power generation power prediction and equipment health state evaluation. Therefore, the prediction accuracy and the health management reliability are improved.

The invention provides a ship target detection method based on nonlinear network enhancement, a medium and equipment, and belongs to the field of artificial intelligence. The method comprises the following steps of: performing type conversion, data division and enhancement on image data in a data set by adopting the ship data set acquired in a real river channel scene; a nonlinear network enhanced YOLO ship detection model is constructed; adopting a multi-task joint loss function to train the YOLO ship detection model; and inputting a port monitoring image and a sea surface aerial image into the trained YOLO ship detection model, outputting a category number, a confidence value and bounding box coordinates of each prediction box, and forming a visual image. According to the method, a nonlinear network enhanced YOLO ship detection model is constructed, and the modeling expression capability of the network on a ship target in a complex scene is effectively improved, so that the robustness and the accuracy of target detection are enhanced, and particularly, the performance is better under the conditions of small target detection and image degradation.

Embodiments herein provide methods for HPLMN control for discontinuous coverage in satellite access network by UE. The method includes determining whether DisCoNoserviceapplicability parameter is set to a value corresponding to true to indicate that UE remain in no service or whether parameter is set to a value corresponding to false to indicate that UE selects and camps on alternate network. In an embodiment, the method includes remaining in no service during discontinuous coverage duration and wait for completion of discontinuous coverage to resume normal services when DisCoNoserviceapplicability parameter is set to value corresponding to true to indicate that UE shall remain in no service. Alternately, the method includes performing PLMN selection procedure to camp on alternate network during discontinuous coverage to receive normal services when DisCoNoserviceapplicability parameter is set to the value correspond to false to indicate that UE selects and camps on alternate network.

Systems and methods disclosed herein automatically register, monitor, and authenticate distributed artificial intelligence (AI) agents and their operational contexts using a distributed or federated ledger-based agent knowledge registry. The system obtains a registration or query request (e.g., from an AI agent, orchestrator, or user interface) to identify or store operational context linked to each AI-based agent. The system determines a feature set of agent metadata and operational parameters using a first AI model set, and dynamically generates a cryptographically verifiable registry record set using a second AI model set (same as or different from the first AI model set) based on the operational feature set, to be stored in a distributed ledger database. The system automatically executes registry updating, agent selection, or operational verification workflows using a third AI model set (same as or different from the first and / or second AI model sets) to maintain records that trace inter-agent interactions.

The invention discloses a PON-based multi-protocol communication method and device, and the method comprises the steps: distributing a corresponding internal scheduling time slot to each network protocol processing unit for data uploading to each network protocol processing unit when a data processing module detects that the network protocol processing units connected with each field bus device have to-be-uploaded service data; and when one internal scheduling time slot is used up, interrupting the uploading of the service data until the next internal scheduling time slot allocated to the internal scheduling time slot is recovered from interrupting the uploading of the data. And the PON module monitors a global scheduling time slot allocation instruction issued by the optical line terminal in real time, converts an uploaded data frame into an electric signal, transmits the electric signal to the optical module through an internal link, converts the electric signal into an optical signal through the optical module in an allocated global scheduling time slot, and uploads the optical signal to the optical line terminal through an optical distribution network. Real-time performance and orderliness of transmission of service data in multiple field bus devices in a PON network are achieved, the requirement of industrial communication for data transmission is met, and transmission resource distribution is optimized.

Various embodiments of the present disclosure relate to providing AI-based generation of responsive content. In an example embodiment, generative response content is an AI-powered interactive interface embedded within presented content, allowing users to ask questions and receive relevant, real-time answers directly within the content space, without requiring a click or further interaction to access a landing page.

The invention provides an analysis method and device for flow, efficiency and water consumption rate of a hydraulic power plant generator set. A distributed flow measurement unit based on a domestic PLC controller and a domestic operating system is constructed, and each unit is independently configured with the flow measurement unit to realize decentralization of data acquisition and processing; active power, guide vane opening, water head difference, instantaneous flow and volute inlet pressure data are collected in real time through a distributed flow measurement unit, and a unit efficiency and water consumption rate calculation model is dynamically fitted based on the data; sending an output result of the calculation model to a monitoring system through an anti-interference communication network, wherein the communication network adopts layered wiring and a double-shielded twisted pair to reduce the signal bit error rate; and the IMC integrated management and control platform is used for remote monitoring and diagnosis of the flow measurement unit, and seamless connection with a smart power plant data medium station is realized. The system reliability and the maintenance flexibility are improved, and the autonomous controllable requirements of domestic substitution and intelligent power plants are comprehensively met.

The invention provides a bimodal substation foundation settlement deformation dynamic prediction method and system, and relates to the technical field of foundation settlement, and the method comprises the steps: deploying a bimodal sensing monitoring network on a target substation to collect foundation mechanical deformation data and foundation deformation positioning data; building a foundation deformation data processing channel to carry out preprocessing and weighted fusion on the foundation mechanical deformation data and the foundation deformation positioning data to obtain foundation settlement deformation fusion data; and training a foundation settlement deformation prediction network to dynamically predict the foundation settlement deformation fusion data, determining a foundation settlement deformation prediction quantity, and triggering a settlement abnormity response mechanism based on the foundation settlement deformation prediction quantity to carry out graded response early warning. The technical problems that in the prior art, transformer substation foundation settlement risk identification lags behind, the disposal efficiency is low, and safe and stable operation of a transformer substation is difficult to guarantee can be solved, and the technical effect of guaranteeing long-term safe and stable operation of the transformer substation is achieved.

A system and a method to detect an attack on an IoT network is disclosed. The IoT network includes interconnection of multiple IoT devices. The method includes receiving, by a network connection device, multiple ICMPv6 network packets from IoT devices and outputting multiple output packets; and matching, by a routing device, a network traffic pattern to attack signatures structured as a taxonomy according to which part of a packet is misused. The taxonomy includes a branch to a data plane attack and a control plane attack, respectively. When an IPv6 RPL packet is detected, the method includes checking for generating, modifying, and replaying attacks by an attacker. When a non-RPL packet is detected, the method includes checking for dropping and leaking packet attacks by the attacker. When the attack is detected, the method includes invoking a solution to the attack. The solution includes mitigation of the attack by the attacker.

The invention discloses a hydropower station data operation and maintenance method, which comprises the following steps of: arranging a sensor network on hydraulic facilities and electromechanical gold junction equipment, acquiring data, preprocessing the data, registering in space and time scales, converting data signals into acoustic, optical and electric signals, and transmitting the acoustic, optical and electric signals to a server; the method comprises the following steps: performing signal fusion of a three-dimensional sonar signal and an RGB image based on an attention concentration mechanism, performing deep analysis on data, extracting a sensor data mode of hydraulic facilities and electromechanical junction equipment under normal working condition operation, establishing a fault diagnosis model, performing real-time diagnosis on the hydraulic facilities and the electromechanical junction equipment, and constructing a response decision knowledge base. Establishing a hydropower optimization scheduling model, and displaying the information of the hydropower station; the system integrates the Internet of Things, big data, artificial intelligence, cloud computing, BIM three-dimensional modeling and digital twinning technologies, realizes real-time monitoring, intelligent diagnosis and optimal scheduling of equipment and facilities of the hydropower station, and improves the overall digital intelligent operation management level of the hydropower station.

The invention discloses a multi-unmanned aerial vehicle confrontation task execution method and device based on model reinforcement learning, and a medium, and the method comprises the following steps: inputting the current local observation into a reinforcement learning control network for each airframe in an own unmanned aerial vehicle group, and outputting meta-actions (including movement and attack), performing confrontation interaction with the enemy unmanned aerial vehicle group to obtain finite confrontation experience data; the collected historical interaction experience is used for supervised learning of a world model, so that the world model can approach probability distribution of an interaction rule of a real environment; data expansion is carried out on real interaction data by using a world model, an action network and an evaluation network are trained on expanded empirical data, and strategy optimization and parameter updating of a multi-unmanned aerial vehicle system are realized; repeating the process, and sequentially carrying out experience collection, world model training and enhancement strategy updating; and finally, the trained action network is reserved and is used for outputting a specific action strategy in an actual confrontation task.

The invention discloses a submarine cable construction ship extremely-short-term motion response prediction method and system, and relates to the technical field of ship motion control in ocean engineering. Comprising the following steps: acquiring six-degree-of-freedom time sequence data based on a ship-borne inertial measurement unit of a submarine cable construction ship, and constructing a data set; the data set is preprocessed; constructing a model CNN-RSBU-BiLSTM (Convolutional Residual Shrinkage-Bidirectional Long Short Term Memory) with fusion of the convolutional residual shrinkage; the model CNN-RSBU-BiLSTM is trained by means of the data set obtained after preprocessing is carried out on the model CNN-RSBU-BiLSTM; and inputting the six-degree-of-freedom time sequence data acquired and preprocessed in real time into the trained model, and outputting an extremely short-term six-degree-of-freedom motion response prediction result of the submarine cable construction ship. According to the method, the multi-scale feature extraction capability of the one-dimensional convolutional neural network, the noise reduction enhancement effect of the residual shrinkage building unit and the digging capability of the bidirectional long-short-term memory network on the time sequence dependency relationship are combined, so that accurate prediction of the extremely-short-term six-degree-of-freedom motion response of the submarine cable construction ship is realized, and the prediction accuracy is effectively improved.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Embodiments herein provide higher priority PLMN selection method during disaster situation in a wireless network (1000) by MS (100). In an embodiment, when MS (100) is registered for the disaster roaming service, the method includes selecting a higher priority PLMN in a priority order when the higher priority PLMN is available in the network and other non-forbidden PLMN when the higher priority PLMN is unavailable in the network, and registering the MS to receive the service in the network on one of the selected higher priority PLMN, and the selected other non-forbidden PLMN. In another embodiment, when the MS is not registered for the disaster roaming service, the method includes selecting a higher priority PLMN in a priority order when the higher priority PLMN is available in network, and registering the MS on the selected higher priority PLMN to receive the service in network.

OF THE DISCLOSURE Apparatuses, methods, and systems are disclosed for establishing a multi-access data connection with an external access path. One apparatus (500) includes a transceiver (525) that communicates (705) with first and second mobile networks and a processor (505) that sends (710) a request message to the first mobile network to establish a multi-access data connection, the request message containing an external access path request. The processor (505) receives (715) a response message that accepts the request to establish a multi-access data connection and that contains a set of rules indicating how the apparatus (500) is to route data traffic across a set of access networks in the first mobile network and across an external access path over the second mobile network. The processor (505) communicates (720) with a UPF in the first network over the set of access networks and the external access path.

A system, apparatus, and method to generate decoy honeypots by using generated adversarial networks. In some embodiments, a method for generating decoy honeypots, the steps comprising identifying a plurality of network device configurations on a network; instantiating a generative adversarial network comprising architecture properties; generating a plurality of decoy honeypots with the generative adversarial network, wherein the plurality of decoy honeypots imitate the plurality of network device configurations to deceive malicious actors, and wherein the generative adversarial network optimizes a distribution of the plurality of decoy honeypots according to a precision distribution and a recall distribution; activating the plurality of decoy honeypots to the network; and dynamically evolving the plurality of decoy honeypots towards one or more preferences of a network attacker.

Methods and systems for connection management are disclosed. A service entity may provide preferential treatment to a group of devices on a network. To identify itself as being entitled to receive such treatment, a user device may send one or more credentials to a server device associated with the service entity. The credential(s) may be used to determine if the user device belongs to the group of devices. If the user device belongs to the group of devices, the service entity may establish one or more enhanced service flows that the user device can use to communicate with and / or over the network.

A system and method for providing a randomness function to a smart contact on a decentralized network. An oracle receives a request for at least one random value to be supplied to a smart contract executing on a decentralized network, the request including a seed. The oracle generates the at least one random value based on the seed, and a secret key associated with the oracle, wherein the oracle is identified on the decentralized network by a public key associated with the secret key. The oracle generates a cryptographic proof based on the at least one random value and the public key and sends the at least one random value and the cryptographic proof to the decentralized network, whereby the proof, and thus the randomness of the at least one random value, can be verified.

The embodiment of the invention discloses a network asset inspection method, device and equipment and a storage medium, and relates to the field of network security. The method comprises the steps of searching first network asset data matched with a keyword on a network in response to the keyword which is input by an asset surveying and mapping tool and is related to a target object; the first network asset data is compared with historical network asset data of the target object, second network asset data are screened out, and the second network asset data comprise unknown network asset data and / or changed network asset data; and outputting at least partial data of the second network asset data. All network assets are searched through the asset surveying and mapping tool, and are compared with historical network asset data, so that unknown network asset data and changed network asset data are accurately screened and timely output, and the efficiency of network asset inspection is improved.

The application discloses a method, device and equipment for a virtual machine to access a cloud platform management network and a medium, the method comprising: creating a first virtual bridge interworking with a business network on a physical node of a cloud platform; creating a virtual management network interworking with a management network on the physical node based on the first virtual bridge; connecting a virtual machine on the physical node with the virtual management network and configuring a default route when the virtual machine accesses the management network, so that the virtual machine can access the management network based on the virtual management network. The method provided by the application enables the virtual machine to access the cloud platform management network without changing the physical isolation of the cloud platform management network and the business network.

Method and apparatus are described herein for paging and responding in different networks, i.e. a non-terrestrial network, NTN, to a terrestrial network, TN. A wireless transmit / receive unit, WTRU, e.g., camping in an idle or inactive state, monitors for paging on a NTN network. The WTRU receives a paging message on the NTN. The paging message provided an indication to respond on a TN network. The WTRU performs a cell reselection to the TN. The WTRU responds to the paging on the NTN network by sending a paging response message on the TN network.

Systems, methods, and software are disclosed herein for anomaly detection in a wireless communication network based on congestion notification in various implementations. In one example, a computing apparatus processes network telemetry data and congestion data using a machine learning model trained to detect anomalous behavior on wireless communication network. In response to detecting the anomalous behavior, the computing apparatus identifies a source of the anomalous behavior on the network and initiates an action with respect to a network function associated with the source of the anomalous behavior to mitigate one or more effects of the anomalous behavior.

A system and method to protect resource servers from DDOS attacks is disclosed. A proxy gateway hides resource server using IP subnet addressing and receives authenticated IP packets. A cryptographic hash is generated, and destination IP packets address / ports are encrypted using a secret, algorithm, client IP address, resource server IP address, and port. The cryptographic hash and destination server are mapped into existing IP packet addresses, ports, or payload bits. The IP packets are routed to a redirect node in the public IP subnet of the proxy gateway. At the redirected node, the cryptographic hash is validated, and the resource server IP address and the port are extracted from mapping. Redirect node maps the clients public IP / port to an internal IP address and port associated with resource server and tunnels the IP packets from the redirect node to the resource server using the internal IP address and port.

The invention provides a cloud edge-end collaborative deployment method and system for an image reasoning multi-mode neural network, and is applied to the field of artificial intelligence, and the method comprises the steps: abstracting a preset multi-mode neural network, and obtaining an original calculation graph; based on the calculation node set and the data dependence edge set, splitting the original calculation graph to obtain a plurality of connected sub-graphs, the categories of the plurality of connected sub-graphs including a single-modal processing sub-graph, a cross-modal interaction sub-graph and a task specific sub-graph; based on the topological sequence of the plurality of connected sub-graphs, determining segmentation points of each connected sub-graph; dividing the plurality of connected sub-graphs according to the segmentation points to obtain a plurality of segmentation parts; and integrally dividing the multi-modal neural network based on the plurality of segmentation parts according to a genetic algorithm, and respectively deploying the integrally divided multi-modal neural network to a terminal device, an edge server and a cloud platform. According to the invention, a flexible and efficient model segmentation and deployment mechanism can be realized.

This invention provides methods and systems for seamless mobile connectivity between public and private cellular networks. The system dynamically switches user devices between networks based on location, radio signal availability, or preconfigured policies that prioritize private networks when within range. For devices with physical SIM cards, an embedded applet enables switching between operator profiles, while ESIM profiles deploy applets for selecting among multiple identities within a profile. All cellular traffic, whether on public or private networks, is routed through a cloud-based system for centralized security and policy enforcement. Network selection may be influenced by defining the private network as the Home Public Land Mobile Network (HPLMN) or scanning available networks via applet capabilities. The system supports unified subscription, connectivity, and service management via a cloud-based portal, ensuring reliability and security across diverse network environments. This approach enhances mobility, security, and flexibility for enterprise and IoT applications.

The invention provides a day-ahead reactive power optimization method based on a graph convolutional network and a conditional value-at-risk strategy. The method comprises the following steps: firstly, utilizing Monte Carlo simulation to generate a plurality of joint scenes of wind power and load uncertainty, extracting a typical operation scene through a K-means clustering method, and establishing a multi-target random reactive power optimization model taking active power loss and node voltage deviation as optimization targets; secondly, based on the optimization model, constructing a weighted adjacency matrix fused with power grid physical parameters, and enhancing the recognition capability of the graph convolutional network on the relationship between the nodes; then, a graph convolutional network model is trained to establish efficient mapping between a power system operation state and a reactive power regulation scheme, and rapid strategy generation is realized; and finally, performing cross-scene evaluation on the performance of each scene strategy and the voltage stability risk based on the conditional value-at-risk, and screening out a day-ahead optimization strategy with the best robustness. According to the method, the calculation efficiency and strategy robustness of day-ahead reactive power optimization are effectively improved, and the method is suitable for a power system with high-proportion new energy access.

The invention discloses a neural network training method and system for few-sample target detection, and belongs to the technical field of machine learning. The method comprises the following steps: acquiring a cross-scene sample data set; constructing a feature extraction network based on deep meta learning, training the feature extraction network on a cross-scene sample data set by using a meta learning device, sampling from the data set to construct a plurality of learning tasks, updating network parameters based on an internal and external loop optimization strategy, and generating initial weight parameters of a target detection network; and using the initial weight parameter to initialize a target detection network adopting a deep separable convolution structure. According to the method, the cross-scene features are extracted through the meta-learning framework, the problem of poor model generalization ability caused by scarcity of labeled data in target detection is solved, a few-sample learning task in a new scene can be quickly adapted, and the method can be applied to the fields of airport bird detection, security monitoring and the like.