3815 results about "Manet routing" patented technology

A method is provided for enhanced data routing in neural networks using Self-Organizing Maps (SOM) integrated with Autoencoder-GAN. The method comprises training an autoencoder to encode input data into a latent space representation; applying a Self-Organizing Map (SOM) to organize the latent space representation into a topological map; refining the latent space representation using a Generative Adversarial Network (GAN), wherein the generator generates enhanced latent space representations and the discriminator evaluates their quality; using the refined latent space representations to update the SOM topology dynamically; generating routing coefficients based on the updated SOM topology to guide data routing in a capsule network; and dynamically adjusting routing within the capsule network using the generated routing coefficients to enhance performance based on the refined latent representations.

Systems, methods, and devices that relate to routing requests to large language models (LLMs) are disclosed. In one example aspect, the system receives session-specific data elements in response to a request to generate an output using LLMs. The system determines a hierarchy of operational constraints including privacy protocols and performance requirements. Weights for a multi-variable optimization are dynamically updated using the session-specific data elements. The system executes the multi-variable optimization across candidate LLMs that satisfy privacy constraints and optimize performance constraints. Based on the optimization, at least one candidate LLM is selected and the request is routed to it. In response to performance feedback, the system automatically selects a different LLM to improve one constraint, resulting in degradation of another constraint.

The invention provides a dynamic routing selection method and system, electronic equipment and a storage medium, and aims to solve the problem that a routing strategy is difficult to adapt to a dynamically changing network, the method comprises the following steps: a terminal layer collects the state of a terminal and network data, and performs lightweight feature extraction; the edge node layer receives the data of the terminal layer, carries out space-time-semantic feature aggregation, and generates a region-level resource scheduling and routing decision strategy based on fragmented reinforcement learning; the central cloud service layer gathers whole network data, generates a global optimization strategy and issues the global optimization strategy; the edge node layer fuses global optimization and a region-level strategy, and executes dynamic routing selection; and security and privacy protection are provided through the trusted chain layer. According to the invention, adaptive path selection can be realized, the network resource utilization rate is improved, and the network stability is improved.

The invention relates to the technical field of communication, in particular to an underground power distribution room communication system and method based on heterogeneous network and multi-modal fusion, and the system comprises a three-dimensional heterogeneous network cooperation module, a cross-modal feature fusion module, a dynamic routing decision engine module, a self-adaptive interference suppression system and a cross-layer cooperation optimization module. The three-dimensional heterogeneous network cooperation module comprises a 4G / 5G wireless communication sub-layer, a power line carrier communication sub-layer and an edge computing management sub-layer; the cross-modal feature fusion module comprises a multi-modal encoder group, a shared feature projection layer, a twin network structure and a cross-modal attention fusion module; and the dynamic routing decision engine module comprises a state space monitoring unit, a routing optimization unit and a millisecond-level path switching strategy. Through the arrangement, the reliability, the real-time performance and the energy efficiency of communication of the underground power distribution room are systematically improved, and a high-robustness communication infrastructure is provided for an intelligent power grid.

The invention provides a large model lightweight reasoning deployment method under limited hardware resources, and the method comprises the steps: quantifying the weight importance of a large model through a composite index of gradient sensitivity and activation frequency, and carrying out pruning operation in combination with an improved index weighted moving average strategy, thereby obtaining a structured sparse model; the sparse model is divided into sub-networks by adopting double rules, a routing decision network is trained, and an adaptive feature shunting architecture model is constructed; a multi-precision weight set is generated through a nested quantization technology, quantization bit width is dynamically adjusted, and edge equipment hardware parameters are adapted to complete reasoning environment initialization; after a reasoning request is received, an optimal sub-network is selected based on the trained routing decision network, corresponding weights are loaded in parallel, and a reasoning result is fused and output; and converting a reasoning result format, and dynamically optimizing a scheduling strategy based on a system real-time monitoring index. The method is compatible with a mainstream large model and a hardware platform, and an efficient and universal deployment scheme is provided for end-side AI engineering landing.

The invention discloses a multi-agent collaborative data exchange dynamic routing optimization method and system, and relates to the technical field of network communication, and the method comprises the steps: obtaining the real-time state information of each node in a network, and outputting a node state data set; constructing a historical state sequence of each node, calculating a load change trend, and outputting a routing adjustment signal when the load change trend reaches a load critical value; calculating a collaborative weight according to the network contribution degree of each agent, establishing a task allocation mapping relationship among the agents based on the collaborative weight, and outputting a collaborative scheduling result; determining a candidate path set, performing index evaluation on paths in the candidate path set, and outputting an optimal routing path; and executing data transmission, obtaining a quality difference between an actual transmission effect and an expected effect, and performing parameter correction. According to the invention, active optimization and multi-target balance of network routing are realized, and routing efficiency and system stability in a dynamic network environment are improved.

The embodiment of the invention discloses a multi-agent task collaboration method and device and a medium, and the method is characterized in that the method comprises the steps: registering functions corresponding to all agents to a dynamic service directory through an MCP protocol; disassembling the to-be-executed task to obtain a plurality of to-be-executed sub-tasks, matching each to-be-executed sub-task with the capability range of each agent in the dynamic service directory, and establishing a communication channel between each to-be-executed sub-task and the corresponding agent; performing dynamic routing distribution on the to-be-executed sub-tasks through the cooperative bus, and selecting an optimal execution node according to the real-time requirements of the sub-tasks and the node load state of each corresponding agent; according to a subtask dependency relationship defined by a directed acyclic graph, determining an execution mode of each subtask to be executed; and executing each to-be-executed sub-task at the optimal execution node based on the communication channel and the execution mode of each to-be-executed sub-task.

The invention discloses a large language model progressive field fine tuning and knowledge fusion method for shield engineering. The method comprises the following steps: constructing a layered shield training course containing a wide-area academic theory and a proprietary enterprise construction method; parallelly training a plurality of physically isolated parameter efficient adapters based on the frozen base; performing singular value decomposition on the adapter, extracting a geometric feature subspace representing knowledge distribution, and calculating a conflict correlation degree; based on this, a uniform adaptation mechanism of resource awareness is constructed. The mechanism not only can generate a static fusion model for conflict removal, but also can dynamically activate a specific rank slice of an adapter through a routing network based on real-time hardware resource budget (video memory / FLOPs) and geometry-resource signature. According to the method, multi-source knowledge is reserved, and adaptive dynamic scheduling of edge hardware resources by model reasoning is realized.

The invention discloses a non-direct connection-oriented data routing method, which comprises the following steps of: S1, acquiring topological structures, link bandwidths, time delays, loads and energy consumption states of nodes in a network in real time, and constructing a time sequence dynamic matrix of the nodes and links; s2, on the basis of the dynamic matrix, adopting a neural network adaptive enhanced ant colony optimization method to generate alternative paths; s3, generating a grey wolf optimization algorithm initial population by using the alternative paths, and constructing a multi-dimensional composite fitness function; s4, according to the fitness function, driving the grey wolf optimization algorithm to perform multi-scale iteration to update the path; s5, topology and node state prediction is carried out based on the dominant path, and the prediction path is optimized in advance; and S6, issuing the optimal path and the alternative path at the same time, carrying out data parallel forwarding, and driving a neural enhanced ant colony optimization algorithm to update online. The method improves the network path selection efficiency and the resource utilization rate, and is suitable for data routing in a complex network environment.

The invention relates to the technical field of integrated circuits and computer networks, and particularly discloses a transmission control and intelligent scheduling system for an integrated chip, and the system sets a dual-mode decision and dynamic switching mechanism for each routing node. Calculating dynamic characteristic parameters including an instantaneous value, a first-order trend and a second-order acceleration; when the parameter is matched with a pre-stored abnormal feature set and the load exceeds a threshold value, the node is immediately atomized and switched to a predefined security scheduling strategy loaded from a shared storage area, and otherwise, the node generates a scheduling decision according to self-adaptive decision logic continuously optimized based on historical performance feedback; all the nodes carry out data packet forwarding control according to the current execution strategy; and the system also periodically realizes federated global knowledge evolution according to the quality evaluation result of the self-adaptive decision of each node.

The invention provides an SDN (Software Defined Network) inter-domain traffic engineering method based on reinforcement learning, which comprises the following steps of: deploying a data traffic demand monitoring platform and a control system, and constructing a global network topological graph; calculating a short link identifier for the link in the network and distributing the short link identifier to each network device; flow judgment is carried out, upward notification is carried out according to requirements, and pre-operation of intelligent routing is cooperatively completed; deploying a reinforcement learning model in the total intelligent body, outputting an optimal cross-domain path strategy to the cooperative controller, disassembling the optimal cross-domain path strategy into flow table rules which can be executed by each domain, and issuing the flow table rules to local controllers of related domains; and each local controller pushes the flow table configuration to the domain switching equipment to complete the forwarding decision of the flow. According to the method, a complete closed-loop process of flow measurement, intelligent decision making, cross-domain control and path issuing is realized, feasible reference is provided for actual deployment of an intelligent network, and the method has good engineering popularization value and is suitable for intelligent scheduling scenes such as an operator backbone network, an industrial internet and metro edge cloud.

The invention relates to the technical field of computers, and discloses a method and system for dynamically routing a back-end address by an API gateway, and the method comprises the steps: receiving an HTTP request, and analyzing a URL and a request header; extracting a tenant identifier, and supporting multi-mode identification of a request header, a sub-domain name or a path segment; loading an exclusive routing rule set based on the tenant identifier; sorting according to local priorities in the tenants, executing longest path prefix matching through a prefix tree, and determining an optimal back-end address; and finally forwarding the request. The system comprises a request analysis module, a tenant identification module, a rule loading module, a local sorting module, a longest matching module and a request forwarding module, and supports routing rule hot update and conflict detection. According to the method, cross-tenant interference is effectively eliminated through tenant context awareness and a two-stage matching mechanism, and the routing accuracy and the system isolation are improved.

The invention provides a hierarchical deep reinforcement learning routing protocol method based on a multi-link ad hoc network, relates to the technical field of communication routing, and solves the problem that the calculation efficiency in the current multi-link ad hoc network is degraded. The method comprises the following steps: firstly, constructing and dynamically updating a global network view for all nodes in a network, and further constructing a route selection model which is divided into a high-level controller and a low-level controller; a high-level controller plans a route from a global perspective and balances a global load, and a low-level controller selects a target node according to a planning result of the high-level controller and distributes an optimal communication link for the target node in combination with link state information; training and optimization of the network of each controller are carried out independently, external rewards are introduced to a high-level controller, and internal rewards are introduced to a low-level controller; the external reward is used for optimizing the overall performance of the high-level controller; the internal reward is used as an index value of the low-level controller for feeding back the link state in real time, and collaborative optimization of the high-level controller and the low-level controller is achieved.

The invention discloses a source network load storage route adjusting method based on an AI control strategy, and relates to the technical field of power system control, and the method comprises the steps: collecting a source side signal, a network side signal, a load side signal, a storage side signal and an exogenous signal, and generating a unified system state of confidence weight and topology embedding; the state is used for digital twinning online calibration, a feasible range and a constraint set are extracted, and a projection operator and guaranteed operation curve set is generated; generating a unified energy routing vector in the feasible range by a hierarchical strategy; performing simulation checking calculation in the digital twinning and compiling into a final instruction and execution plan table; performing closed-loop issuing and receipt checking, and performing event write-back to update guardrails and strategies; the method realizes collaboration, improves on-site consumption of new energy, reduces comprehensive cost, enhances boundary compliance and traceability, is suitable for parks, micro-grids and the like, and is convenient for deployment, operation, maintenance, expansion and upgrading.

The invention discloses an MQTT message transmission optimization method and system. The method comprises the steps of analyzing a theme, extracting a device type, a data feature and a geographic position triple, calculating a hash value, and mapping a device to a specified Broker fragment cluster node to generate a fragment mapping table; processing the equipment data in the edge domain in the fragment mapping table through an edge calculation layer, removing invalid data according to a preset rule, merging the equipment data in the same fragment node, and embedding a fragment node ID for an aggregation message generated after merging; identifying a fragment node ID and routing to a target fragment node, positioning a corresponding shared memory pool, and writing the message into the shared memory pool; and responding to a direct access request of the client, so that the client directly accesses the data from the shared memory pool through the user mode network stack. According to the method, the problem of uneven load is effectively solved, multiple times of state switching in the data transmission process is avoided, and the MQTT message transmission efficiency is improved while the transmission cost is reduced.

A scheduling method for managing network congestion includes: determining, in response to link congestion in a network, a plurality of queue pairs (QPs) on a congested link in the network; determining, from the plurality of QPs on the congested link, a target QP for scheduling out from the congested link; determining a target traffic-forwarding path based on traffic of links in the network, the target traffic-forwarding path being configured for forwarding traffic of the target QP; determining a changed route of the target QP based on a target address of the target QP and the target traffic-forwarding path; and delivering the changed route to a target source local area network access (LA) device of the target QP, the target source LA device being configured to output, based on the changed route, the traffic of the target QP through the target traffic-forwarding path.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive, at a first modem and from a first device, one or more data packets. Based on an explicit congestion notification (ECN) capable transport (ECT) codepoint indication in respective header portions of the data packets, the UE may route the data packets from the first modem to two or more queues associated with a second modem of the UE. Based on receiving buffer and link state information associated with the second modem, the UE may perform a congestion indication procedure that is associated with congestion of a communication link associated with the UE. The UE may thereafter transmit, to a second device, packet data from the two or more queues associated with the second modem of the UE based on a respective priority associated with the two or more queues.

The invention relates to the technical field of software-defined wide area network data transmission, and discloses a software-defined wide area network data transmission method and system based on edge computing. According to the method, network state data are collected through distributed probes deployed at edge nodes, and a dynamic transmission path between the nodes is calculated after data cleaning and feature extraction. A collaborative filtering algorithm is utilized to analyze historical and real-time data to generate a transmission optimization strategy, and a fuzzy logic decision maker is combined to generate a dynamic routing strategy. And predicting a future congestion state based on network characteristics and a routing strategy, and adjusting flow scheduling parameters accordingly. And finally realizing visual display and time sequence storage of the network state data, the routing strategy and the scheduling parameters. The method can effectively reduce transmission delay and improve the utilization rate of network resources.

The invention discloses a real-time data analysis method and system based on multi-modal semantic mapping, and the method comprises the steps: obtaining input event information containing a multi-modal data flow, and carrying out the preprocessing of the multi-modal data flow; performing space-time alignment on each piece of modal data in the preprocessed multi-modal data stream, extracting modal features of each piece of modal data by using a pre-trained multi-modal encoder, and mapping the modal features to a unified semantic space; dynamically adjusting the weight of each modal feature by using a routing network according to the event information, and fusing each modal feature based on the weight to obtain a fused feature; and dynamically analyzing the cluster structure change of the fusion feature distribution by using a clustering algorithm, carrying out anomaly detection by using an outlier analysis method according to the cluster structure change, and if an anomaly triggering condition is met, generating an alarm signal.

The invention discloses an eBPF-based kernel optimization method, system, equipment and medium, belongs to the technical field of Linux kernel network optimization, and aims to solve the technical problem of how to improve the transmission performance for a specific data stream and specify the data stream to carry out a special routing and transmission process so as to provide services for a special network adapter and improve the transmission performance of the specific data stream. According to the technical scheme, the method comprises the steps that data packet interception is carried out on a plurality of kernel eBPF loading points and program interception points selected by a user through an eBPF kernel hook program, routing query is carried out with a routing kernel module, and selection of an operation function and routing parameters is completed; the routing kernel module completes a routing decision based on various complex routing requirements, configuration conditions, data packet conditions and environment conditions, and returns a query result to the eBPF kernel hook program, and the query result is executed by the eBPF kernel hook program. The system comprises an eBPF user mode management program module, a user mode control tool, an eBPF kernel hook program module and a routing kernel module.

The invention provides an outbound routing method and system based on multi-dimensional index coupling and large model decision, and the method comprises the steps: carrying out the preprocessing of the historical data of each line provider, predicting the future performance index through an LSTM neural network, and optimizing the prediction result through a Kalman filter, and eliminating the noise. In the route decision-making link, the system calculates the Q value of each line provider based on the deep Q learning network, comprehensively considers the call completing rate, average cost, negative feedback rate and other factors of each line provider, and intelligently selects the optimal line provider in combination with the user portrait, scene recognition and real-time load state. The system also constructs a complete feedback closed loop, continuously optimizes the model by collecting call result data, and automatically adjusts decision parameters. The call completing rate and the service quality of the outbound service are improved, and the communication cost is reduced.

A routing method for supporting differential service requirements in a satellite internet comprises the following steps: step 1, constructing multi-dimensional network state perception as a base layer which is responsible for collecting and quantifying physical states and logic states of a satellite network in real time, and obtaining objective network states and subjective QoS (Quality of Service) requirements of services; 2, generating a nonlinear adaptive weight driven by a service as a strategy layer, mapping an objective network state and a subjective QoS (Quality of Service) demand of the service, dynamically adjusting the value orientation of routing judgment, and generating a dynamic weight; and step 3, taking the dynamic cost routing decision as an execution layer, calculating the comprehensive cost based on the dynamic weight, and executing next hop selection and path maintenance, and through cooperative work of the dynamic cost routing decision, the comprehensive cost and the next hop selection and path maintenance, realizing intelligent path selection and high-reliability transmission for different service flows in a high-dynamic and resource-limited environment of the low-orbit satellite. According to the invention, while the routing computation overhead is effectively reduced, multiple requirements of differentiated services on low time delay and high reliability can be considered.

In response to a programmatic request, configuration information representing a multi-network-segment gateway established on behalf of a customer is stored at a networking service. In response to another programmatic request, a communication session is established between a route signaling node of the gateway and a routing information source located at a customer premise. In response to additional programmatic input, the networking service stores an indication that the gateway is to be used to transfer packets between a cloud-side virtual network and a customer-side virtual network. The routing information exchanged in the session pertains to the cloud-side and customer-side virtual network, and is used to transfer data packets between the two virtual networks.

The invention relates to a dynamic routing and flow balancing method and system for a regionalized network topology, and the method comprises the steps: dividing a satellite network into a plurality of partitions, and distributing a core node for traffic scheduling and routing calculation for each partition; each core satellite node responds to each traffic demand, and bandwidth and traffic distribution in each partition are adjusted through a multi-anchor-segment routing method and a preset linear programming model; and each satellite node dynamically adjusts the route of each traffic demand through a probability forwarding scheduling method based on geometric topology according to the relative position of the satellite node and the target node. According to the method, low delay, high bandwidth utilization rate, load balance and efficient fault recovery of the satellite network are realized through network partitioning, a multi-anchor-segment routing method and probability forwarding based on geometric topology.

A system is provided for dynamic analysis and verification of smart contracts. The system includes an autoencoder configured to preprocess smart contract code to reduce noise and highlight critical features; a capsule network configured to analyze the preprocessed smart contract code, capturing hierarchical relationships and dependencies within the code; a generative adversarial network (GAN) configured to generate optimal routing coefficients for the capsule network, enhancing the efficiency and accuracy of the analysis; and a blockchain-based platform for deploying and executing smart contracts, wherein the platform utilizes the capsule network to continuously monitor the smart contracts for anomalies during execution.

The invention discloses a communication system based on a satellite multimode edge computing gateway, and relates to the technical field of satellite communication and edge computing. A decentralized decision network is constructed through the multimode edge computing gateway deployed in a distributed mode and a built-in cooperative control unit of the multimode edge computing gateway; the defect of high delay caused by satellite-ground long-distance transmission in traditional centralized control is effectively overcome, so that a load balancing and routing strategy can quickly respond to a continuously changing network state in a constellation, and the agility of the system to deal with dynamic services is remarkably improved; secondly, the inter-satellite link load sensing module adopts a multi-dimensional index fusion and self-adaptive weighting algorithm, so that the difference between the real-time load pressure and the service level of the satellite node can be accurately described, a high-quality and explainable decision basis is provided for distributed cooperative scheduling, misjudgment possibly caused by a single index is avoided, and the reliability of the distributed cooperative scheduling is improved. And the adaptive capacity of the system in different service scenes is enhanced.

The invention relates to an underwater wireless sensor network path sensing routing method based on deep reinforcement learning, which comprises the following steps that: firstly, a node constructs and periodically updates a transmission preference model based on local and neighbor node interaction information; secondly, deploying a deep reinforcement learning model at each underwater sensor node to perform distributed routing strategy learning; and finally, generating a global guide vector by the sink node according to the routing path information of the received data packet, reversely spreading the global guide vector to the source node, fusing the global guide vector with a local transmission preference vector of the node to generate a guide reward, optimizing the deep reinforcement learning model, and updating a routing strategy. According to the method, the problems of difference and complexity of underwater transmission tasks can be solved, and the network data transmission efficiency and the overall service quality are improved in combination with local preference and global guidance while the node online learning is kept to adapt to the dynamic underwater environment.

The invention provides a drainage basin level dam monitoring data standardized integration and cooperative processing method and device, and the method comprises the steps: building a four-dimensional unified data model, and carrying out the standardized mapping of space, time, equipment and business dimensions of multi-source heterogeneous monitoring data; deploying a self-adaptive protocol analyzer, automatically analyzing an equipment document based on a natural language processing technology, generating a protocol conversion rule, and realizing dynamic conversion of various industrial protocols; performing local cleaning, compression, encryption and time sequence feature extraction on the standardized data through an edge computing node to generate a data packet in a unified format; a transmission path is dynamically optimized according to channel quality by using an intelligent routing selection model based on Q-learning, and multi-mode hybrid transmission of a data packet among LoRaWAN, 5G and Beidou short messages is realized. Standardized integration and intelligent cooperative processing of multi-source heterogeneous dam monitoring data in a drainage basin are achieved, information islands are effectively eliminated, the comprehensive operation and maintenance cost is reduced by 30% or above, and the security situation awareness timeliness is improved to the minute level.

Techniques are disclosed for computing a priority of routes advertised by nodes implementing Layer-3 (L3) Multi-Node High Availability (MNHA) for a Software-Defined Wide Area Network (SD-WAN) interconnecting a first network device and a second network device according to link adherence to performance requirements. In one example, a node computes a priority for a route to the second network device based at least in part on a comparison between (1) one or more performance measurements of a link between the node and the second network device and (2) one or more performance requirements for the link. In some examples, the computed priority for the route is further based in part on a preference for the node. The node exports, to the first network device, the route to the second network device, wherein the route comprises data specifying the computed priority for the route.

The invention discloses a multi-level interconnection network based on real-time flow monitoring and dynamic priority adjustment. A multi-dimensional traffic sensing model is constructed through traffic intensity, queue depth, congestion signals and static priority dynamic indexes. A dynamic urgency value is generated by adopting a configurable weighting strategy, and a three-mode closed-loop optimization mechanism is designed. A layered interconnection structure is adopted, crossbar switches are used in computing clusters, and the computing clusters are connected through a low-diameter routing network. In a cluster gateway, a dynamic arbitration unit monitors the flow intensity in real time, calculates the urgency degree in combination with a queue state and a global congestion signal, and dynamically selects a fixed priority and a weighted polling or polling arbitration strategy. The problem that the resource utilization rate of a traditional static arbitration mechanism is reduced due to task switching is effectively solved, the risk of fairness imbalance in a mixed key data flow scene is effectively solved, and the communication adaptation capacity of a reconfigurable architecture to diversified computing loads is improved.